diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 00000000..18df372e
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,7 @@
+version: 2
+
+updates:
+  - package-ecosystem: "uv"
+    directory: "/"
+    schedule:
+      interval: "daily"
diff --git a/.github/workflows/ai4sim-ci-cd.yaml b/.github/workflows/ai4sim-ci-cd.yaml
index 7aa92f0d..0091b242 100644
--- a/.github/workflows/ai4sim-ci-cd.yaml
+++ b/.github/workflows/ai4sim-ci-cd.yaml
@@ -1,6 +1,6 @@
 name: ai4sim-ci-cd
 
-on: [push, pull_request]
+on: [pull_request]
 
 permissions:
   actions: write
@@ -19,25 +19,21 @@ jobs:
           - reactive-flows/cnf-combustion/unets
           - weather-forecast/gravity-wave-drag/cnns
           - weather-forecast/ecrad-3d-correction/unets
+          - weather-forecast/nwp-emulation/panguweather
       fail-fast: false
 
     steps:
     - name: Checkout sources
       uses: actions/checkout@v2
 
-    - name: Read model-project env.yaml file
-      uses: pietrobolcato/action-read-yaml@1.1.0
-      id: uc_env
-      with:
-        config: ${{ matrix.model-project }}/env.yaml
-
-    - name: Set up Python ${{ steps.uc_env.outputs['python_version'] }}
+    - name: Set up Python
       uses: actions/setup-python@v2
-      with:
-        python-version: ${{ steps.uc_env.outputs['python_version'] }}
 
     - name: Install nox build tool
-      run: pip3 install $(grep nox== ${{ matrix.model-project }}/requirements.txt)
+      run: pip3 install nox$(grep nox.needs_version ${{ matrix.model-project }}/noxfile.py | sed -rn "s/.*\"([><=]=.*)\"/\1/p")
+
+    - name: Install uv tool
+      run: pip3 install uv
 
     - name: Analysing the code with flake8
       working-directory: ${{ matrix.model-project }}
@@ -57,6 +53,12 @@ jobs:
         nox -s black -v -- check-only
         rm -rf .nox
 
+    - name: Analysing the code with mypy
+      working-directory: ${{ matrix.model-project }}
+      run: |
+        nox -s mypy
+        rm -rf .nox
+
     - name: Run unit tests with coverage report
       working-directory: ${{ matrix.model-project }}
       run: |
@@ -91,7 +93,7 @@ jobs:
     runs-on: ubuntu-latest
 
     needs: run-checks
-    if: github.event.pull_request.user.login == 'dependabot[bot]'
+    if: ${{ (github.event.pull_request.user.login == 'dependabot[bot]') || (github.event.repository.name == 'model-collection') }}
 
     steps:
       - name: Checkout sources
diff --git a/.github/workflows/images-ci-cd.yaml b/.github/workflows/images-ci-cd.yaml
index a5724a40..eb757fec 100644
--- a/.github/workflows/images-ci-cd.yaml
+++ b/.github/workflows/images-ci-cd.yaml
@@ -11,7 +11,7 @@ on:
 jobs:
   push_to_registries:
     name: Push Docker image to multiple registries
-    runs-on: ubuntu-latest
+    runs-on: linux-large
     permissions:
       packages: write
       contents: read
@@ -23,6 +23,7 @@ jobs:
           - reactive-flows/cnf-combustion/unets
           - weather-forecast/gravity-wave-drag/cnns
           - weather-forecast/ecrad-3d-correction/unets
+          - weather-forecast/nwp-emulation/panguweather
       fail-fast: false
 
     steps:
@@ -79,7 +80,6 @@ jobs:
           build-args: |
             UBUNTU_IMAGE_NAME=${{ steps.uc_env.outputs['ubuntu_base_image.name'] }}
             UBUNTU_IMAGE_TAG=${{ steps.uc_env.outputs['ubuntu_base_image.tag'] }}
-            PYTHON_VERS=${{ steps.uc_env.outputs['python_version'] }}
             MODEL_PROJECT_PATH=${{ matrix.model-project }}
           load: true
           tags: "${{ steps.image.outputs.name }}:${{ steps.image.outputs.tag }}"
@@ -104,6 +104,6 @@ jobs:
             UBUNTU_IMAGE_TAG=${{ steps.uc_env.outputs['ubuntu_base_image.tag'] }}
             PYTHON_VERS=${{ steps.uc_env.outputs['python_version'] }}
             MODEL_PROJECT_PATH=${{ matrix.model-project }}
-          push: true
+          push: ${{ github.event.repository.name == 'model-collection' }}
           tags: "${{ steps.image.outputs.name }}:${{ steps.image.outputs.tag }}"
           labels: ${{ steps.meta.outputs.labels }}
diff --git a/.github/workflows/pr-to-public.yaml b/.github/workflows/pr-to-public.yaml
new file mode 100644
index 00000000..44550932
--- /dev/null
+++ b/.github/workflows/pr-to-public.yaml
@@ -0,0 +1,51 @@
+name: merge-to-public
+
+on:
+  push:
+    # branches:
+    #   - main
+
+permissions:
+  actions: write
+  checks: write
+  contents: write
+  pull-requests: write
+  repository-projects: write
+
+jobs:
+  create-pr:
+    runs-on: ubuntu-latest
+
+    if: github.event.repository.name == 'model-collection-private'
+
+    steps:
+      - name: Checkout private sources
+        uses: actions/checkout@v6
+        with:
+          path: private
+          fetch-depth: 0
+
+      - name: Checkout public sources
+        uses: actions/checkout@v6
+        with:
+          repository: AI4SIM/model-collection
+          path: public
+          fetch-depth: 0
+          token: ${{ secrets.TOKEN }}
+
+      - name: Create merge branch and push
+        run: |
+          cd private
+          git checkout -b merge-private-into-public
+          git push ../public merge-private-into-public
+          cd ../public
+          git config --global user.name 'github-actions[bot]'
+          git config --global user.email 'username@users.noreply.github.com'
+          git push origin merge-private-into-public
+
+      - name: Create a PR
+        run: |
+          cd public
+          gh pr create -B main -H merge-private-into-public -R AI4SIM/model-collection --title 'Merge private into public' --body 'Created by Github action'
+        env:
+          GH_TOKEN: ${{ secrets.TOKEN }}
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index a0cd6fce..b209d157 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,9 @@ __pycache__/
 .coverage.*
 coverage.xml
 
+# Type hinting
+.mypy_cache/
+
 # Jupyter Notebook
 .ipynb_checkpoints
 
diff --git a/README.md b/README.md
index 95cc9b29..dde3d53b 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ Currently, the models that have been developed are based on the following use-ca
 - Weather Forecast
     - Gravity Wave Drag (with CNNs), from ECMWF
     - 3D cloud radiative effect correction (with UNets), from ECMWF
-
+    - Numerical Weather Prediction (NWP) emulation (with PanguWeather)
 ## Project Organization
 
 > In all of the following, an experiment designates a specific training run (hyperparameters) with a specific model (neural architecture) and a specific dataset and splitting strategy.
@@ -64,8 +64,8 @@ All model implementations should include the following folders and files:
 * ``models.py`` contains the model ("module"), including training logic. The architecture can be imported from specific libraries or a local module (e.g. unet.py).
 * ``trainer.py`` is the main entry point responsible for creating a Trainer object, a CLI, and saving artifacts in the experiment directory.
 * ``noxfile.py`` is the Nox build tool configuration file that defines all targets available for the model project.
-* ``env.yaml`` lists the environment requirements, like the Ubuntu docker base image or the Python version.
-* ``requirements.txt`` contains all the Python dependencies of the project, generated using the ``pip freeze`` command.
+* ``env.yaml`` lists the environment requirements, like the Ubuntu docker base image.
+* ``pyproject.toml`` contains all the project definition, like the Python dependencies of the project.
 * ``ci/`` contains all the additional files required to run the functional tests.
 
 Optionally, it can also include the following folders and files:
@@ -109,25 +109,25 @@ If you want to experiment with the model in a different environment you can buil
 
 #### Virtual Environment
 
-To experiment with a model on bare metal, we suggest building a python virtual environment that will embed all the python requirements of the targeted model.
+To experiment with a model on bare metal, we suggest building a python virtual environment using the [uv](https://docs.astral.sh/uv/getting-started/installation/) tool, that will embed all the python requirements of the targeted model.
 
 You can use a manual installation:
 
 ```bash
 cd <domain>/<use-case>/<NN architecture>
-python3 -m venv your-venv
-source your-venv/bin/activate
-pip install -U $(grep pip== ./requirements.txt)
-pip install -r ./requirements.txt
+python3 -m pip install uv
+uv sync
+source .venv/bin/activate
 ```
 
 or, alternatively, you can use the *Nox* target ``dev_dependencies`` (cf [Nox targets]()):
 
 ```bash
 cd <domain>/<use-case>/<NN architecture>
-python3 -m pip install $(grep nox== ./requirements.txt)
+python3 -m pip install nox$(grep nox.needs_version ./noxfile.py | sed -rn "s/.*\"([><=]=.*)\"/\1/p")
+python3 -m pip install uv
 nox -s dev_dependencies
-source dev_dependencies/bin/activate
+source .nox/dev_dependencies/bin/activate
 ```
 
 ### Prepare the Dataset
@@ -141,9 +141,11 @@ You can find, in all model project directories, a ``README.md`` file that descri
 
 - Weather Forecast
     - Gravity Wave Drag 
-        - [CNNs](weather-forecast/ecrad-3d-correction/unets/README.md)
+        - [CNNs](weather-forecast/gravity-wave-drag/cnns/README.md)
     - 3D Bias Correction
-        - [Unets](weather-forecast/gravity-wave-drag/cnns/README.md)
+        - [Unets](weather-forecast/ecrad-3d-correction/unets/README.md)
+    - Numerical Weather Prediction (NWP) emulation
+        - [Pangu-Weather](weather-forecast/nwp-emulation/panguweather/README.md)
 
 ### Launch the Training
 
@@ -165,7 +167,7 @@ podman run \
     -v ./data:/home/ai4sim/<domain>/<use-case>/<NN architecture>/data \
     -w /home/ai4sim/<domain>/<use-case>/<NN architecture> \
     ghcr.io/ai4sim/model-collection:<domain>-<use-case>-<NN architecture> \
-    python3 trainer.py --config configs/<training-config>.yaml
+    uv run trainer.py --config configs/<training-config>.yaml
 ```
 
 #### Virtual Environment
@@ -174,7 +176,7 @@ Using the python virtual environment built as described in [Setting-up the envir
 
 ```bash
 cd <domain>/<use-case>/<NN architecture>
-python3 trainer.py --config configs/<training-config>.yaml
+uv run trainer.py --config configs/<training-config>.yaml
 ```
 
 ## Contribute
@@ -198,7 +200,8 @@ pip install nox
 To install the version of *Nox* used in an existing model project please refer to the ``requirements.txt`` file of the project.
 
 ```bash
-pip install $(grep nox== requirements.txt)
+pip install nox$(grep nox.needs_version ./noxfile.py | sed -rn "s/.*\"([><=]=.*)\"/\1/p")
+
 ```
 
 #### Usage
@@ -249,15 +252,14 @@ The first step is to initiate the required files that will permit to set up the
 
 As a reminder of the [Model project files](#model-project-files) section, the required files to define the environment are:
 
-* ``env.yaml`` lists the environment requirements, like the Ubuntu docker base image or the Python version.
+* ``env.yaml`` lists the environment requirements, like the Ubuntu docker base image.
 * ``noxfile.py`` is the Nox build tool configuration file that defines all targets available for the model project.
-* ``requirements.txt`` contains all the python dependencies of the project, generated using the ``pip freeze`` command.
+* ``pyproject.toml`` contains all the project definition, like the Python dependencies of the project.
 
 ##### Create the *env.yaml* File
 
 This file will actually be used later by the GitHub Actions workflow in charge of building the docker image of the model project, but we recommend creating and filling it now to fix:
 - the **Ubuntu image** you will work on,
-- the **Python version** you want to use.
 
 The format of this file is the following:
 
@@ -265,15 +267,12 @@ The format of this file is the following:
 # This file defines the global environment dependencies for this model project.
 # It is read by the CI/CD workflows in charge of the tests (ai4sim-ci-cd.yaml)
 # and the build of the Docker image (images-ci-cd.yaml) to set the environment.
-python_version: 3.8
 ubuntu_base_image:
   name: nvidia/cuda
   tag: 11.7.1-cudnn8-runtime-ubuntu20.04
 ```
 
-Note, the **Ubuntu base image** should be available on the *docker.io* registry, and the **Python version** chosen must be available in the apt repositories of the chosen **Ubuntu base image**.
-
-> If you have chosen an **Ubuntu base image** with a pre-installed set of python libraries you plan to use (e.g. an nvidia pytorch image), You must report in the *env.yaml* file the python version already available in the image, to prevent a new install of a different python version that will make the pre-installed libraries unavailable.
+Note, the **Ubuntu base image** should be available on the *docker.io* registry.
 
 ##### Initiate *noxfile.py*
 
@@ -312,26 +311,59 @@ sys.path.insert(0, build_ref_dir)
 from nox_ref_file import *
 
 # Insert below the model project specific targets
-
+nox.needs_version = "==2026.2.9"  # You can change the version of nox
 ```
 
 If needed, you can add some sessions, specific to the model project, at the end of the file.
 
-This file must be initiated now because the *Nox* sessions will be used to install the python dependencies (using the ``requirements.txt`` file we will see just below) either in the development docker image, or in the ``dev_dependencies`` virtual environment.
-
-##### Initiate *requirements.txt*
-
-Like the two first files, the ``requirements.txt`` is used to set up the development environment. It should include, at least, the *pip*, *nox*, *torch* and *lightning* version you plan to use:
-
-```python
-pip==24.0
-nox==2024.4.15
-torch==1.13.1+cu117
-pytorch-lightning==1.5.7
+This file must be initiated now because the *Nox* sessions will be used to install the python dependencies (using the ``pyproject.toml`` file we will see just below) either in the development docker image, or in the ``dev_dependencies`` virtual environment.
+
+##### Initiate *pyproject.toml*
+
+Like the two first files, the ``pyproject.toml`` is used to set up the development environment. In it *dependencies* section, it should include, at least, the *torch* and *lightning* version you plan to use, and in its *classifiers* section the python version that should be used (will be used by *nox* and *uv* to set the proper environment) :
+
+```toml
+[project]
+name = "your-project"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    "lightning==2.4.0",
+    "torch==2.6.0",
+]
+
+# Nox will find here the python version that will be used
+classifiers = [
+  "Programming Language :: Python :: 3.10",
+]
+
+[dependency-groups]
+tests = [
+    "pytest-cov==7.0.0"
+]
+coverage = [
+    "coverage==7.13.4"
+]
+black = [
+    "black==26.1.0"
+]
+isort = [
+    "isort==7.0.0"
+]
+mypy = [
+    "mypy==1.19.1",
+    "types-PyYAML==6.0.12"
+]
+lint = [
+    "flake8==7.3.0",
+    "flake8-docstrings==1.7.0",
+    "flake8-use-fstring==1.4",
+    "flake8-variables-names==0.0.6",
+    "pep8-naming==0.15.1"
+]
+data = []
 ```
 
-Note this is not the final version of this file, because you will update it at the end of the process (see [Finalize the requirements.txt](#finalize-the-requirementstxt)), just before pushing your development branch to create a pull request. As a consequence, you can add other requirements right now, but any python library you will install, using ``pip install``, during your development process, will be included in the final version of the file.
-
 ##### Build the Development Environment
 
 You are now ready to set up your development environment, either as a *Docker container* or a Python *virtual environment*.
@@ -344,7 +376,6 @@ The 4 following parameters are mandatory and must be passed to the Dockerfile, u
 - MODEL_PROJECT_PATH: the path ``<domain>/<use-case>/<NN architecture>`` you are working on.
 - UBUNTU_IMAGE_NAME: the base Ubuntu docker image name indicated in the ``env.yaml`` file.
 - UBUNTU_IMAGE_TAG: the base Ubuntu docker image tag indicated in the ``env.yaml`` file.
-- PYTHON_VERS: the Python version indicated in the ``env.yaml`` file.
 
 To build the Docker image from the repository root directory, for example:
 
@@ -355,7 +386,6 @@ docker build \
     --build-arg MODEL_PROJECT_PATH=<domain>/<use-case>/<NN architecture> \
     --build-arg UBUNTU_IMAGE_NAME=nvidia/cuda \
     --build-arg UBUNTU_IMAGE_TAG=11.7.1-cudnn8-runtime-ubuntu20.04 \
-    --build-arg PYTHON_VERS=3.8 \
     --build-arg uid=$(id -u) \
     --build-arg gid=$(id -g) \
     --build-arg uname=$(id -un) \
@@ -371,7 +401,7 @@ docker run -ti \
     bash
 ```
 
-In the container, you can directly use the ``pip`` and ``python`` command of the system to respectively install the dependencies and execute your code.
+In the container, you can directly use the ``nox`` and ``uv`` command of the system to respectively install the dependencies and execute your code.
 
 ###### Virtual Environment
 
@@ -381,7 +411,7 @@ Then it is recommended to use the *Nox* session ``dev_dependencies``:
 
 ```bash
 nox -s dev_dependencies
-source dev_dependencies/bin/activate
+source .nox/dev_dependencies/bin/activate
 ```
 
 #### Model Development
@@ -448,7 +478,7 @@ The ``ci`` folder must contain:
 - a ``run.sh`` script file in charge of running the different training tests.
 
 Optionally it can also include:
-- a ``requirements_data.txt`` listing the dependencies related to the synthetic data generation.
+- a **data** entry in the ``[dependency-groups]`` section of the ``pyproject.toml`` to list the dependencies related to the synthetic data generation.
 
 The *Nox* session ``train_test`` allows running the functional tests:
 
@@ -474,14 +504,14 @@ In each model project, a ``README.md`` file should:
 - explain how to get the dataset,
 - briefly describe the model input and output variables and the NN architecture implemented.
 
-#### Finalize the *requirements.txt*
+#### Finalize the *uv.lock*
 
-Before creating the *Pull Request* itself, you must generate the final *requirements.txt* file to freeze all the python dependencies of your model project.
+Before creating the *Pull Request* itself, you must generate the final *uv.lock* file to freeze all the python dependencies of your model project.
 
-In your development environment use:
+Be sure the file has not be modified since the last :
 
 ```bash
-pip freeze --all > requirements.txt
+uv sync
 ```
 
 Then commit this last change.
@@ -507,6 +537,7 @@ jobs:
           - reactive-flows/cnf-combustion/unets
           - weather-forecast/gravity-wave-drag/cnns
           - weather-forecast/ecrad-3d-correction/unets
+          - weather-forecast/nwp-emulation/panguweather
           - <domain>/<use-case>/<NN architecture>       <-- Your new model project path
 ...
 ```
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 2549bdca..3b3536d7 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -14,19 +14,18 @@ ARG UBUNTU_IMAGE_TAG
 FROM ${DOCKER_REGISTRY}/${UBUNTU_IMAGE_NAME}:${UBUNTU_IMAGE_TAG}
 
 ARG MODEL_PROJECT_PATH
-ARG PYTHON_VERS
 
 # install required packages
 RUN apt-get update \
  && apt-get install -y \
-  python${PYTHON_VERS}-dev \
+  python3-dev \
   python3-pip \
   build-essential \
   libssl-dev \
   libffi-dev \
   vim \
+  git \
  && apt-get clean
-RUN ln -sf /usr/bin/python${PYTHON_VERS} /usr/bin/python3
 
 # create user ai4sim
 ARG uid=1000
@@ -48,7 +47,8 @@ ENV PATH=${PATH}:${AI4SIM_HOME}/.local/bin
 
 # Install all the python dependencies in the environment
 WORKDIR ${AI4SIM_HOME}/${MODEL_PROJECT_PATH}
-RUN python3 -m pip install $(grep nox== ${AI4SIM_HOME}/${MODEL_PROJECT_PATH}/requirements.txt || echo "nox") \
+RUN python3 -m pip install nox$(grep 'nox.needs_version' ./noxfile.py | sed -rn 's/.*\"([><=]=.*)\"/\1/p') \
+ && python3 -m pip install uv \
  && python3 -m nox -s dev_dependencies --no-venv -- purge
 
 CMD ["bash"]
\ No newline at end of file
diff --git a/reactive-flows/cnf-combustion/gnns/ci/generate_synthetic_data.py b/reactive-flows/cnf-combustion/gnns/ci/generate_synthetic_data.py
index 0d9d88d2..e473f79d 100644
--- a/reactive-flows/cnf-combustion/gnns/ci/generate_synthetic_data.py
+++ b/reactive-flows/cnf-combustion/gnns/ci/generate_synthetic_data.py
@@ -23,7 +23,7 @@
 data_path = "./data"
 
 
-def create_data():
+def create_data() -> None:
     """Create data folder with fake raw data"""
     filenames = [
         "test_1.h5",
diff --git a/reactive-flows/cnf-combustion/gnns/data.py b/reactive-flows/cnf-combustion/gnns/data.py
index 0b2daf1f..f2ab6150 100755
--- a/reactive-flows/cnf-combustion/gnns/data.py
+++ b/reactive-flows/cnf-combustion/gnns/data.py
@@ -22,7 +22,7 @@
 import yaml
 from torch import float as tfloat
 from torch import tensor
-from torch.utils.data import random_split
+from torch.utils.data import Subset, random_split
 
 from utils import create_graph_topo
 
@@ -73,7 +73,7 @@ def download(self) -> None:
             f"and move all files in file.tgz/DATA in {self.raw_dir}"
         )
 
-    def _get_data(self, idx: int) -> Dict[str, np.array]:
+    def _get_data(self, idx: int) -> Dict[str, np.ndarray]:
         """Return the dict of the feat and sigma of the corresponding data file.
 
         Returns:
@@ -117,7 +117,7 @@ def __init__(self, root: str, y_normalizer: float = None):
         """
         super().__init__(root, y_normalizer)
 
-    def _get_data(self, idx: int) -> Dict[str, np.array]:
+    def _get_data(self, idx: int) -> Dict[str, np.ndarray]:
         """Return the dict of the feat and sigma of the corresponding data file.
 
         Returns:
@@ -145,7 +145,7 @@ def __init__(self, root: str, y_normalizer: float = None):
         """
         super().__init__(root, y_normalizer)
 
-    def _get_data(self, idx: int) -> Dict[str, np.array]:
+    def _get_data(self, idx: int) -> Dict[str, np.ndarray]:
         """Return the dict of the feat and sigma of the corresponding data file.
 
         Returns:
@@ -197,10 +197,10 @@ def __init__(
         super().__init__()
 
         # init the attribute
-        self.val_dataset = None
-        self.test_dataset = None
-        self.train_dataset = None
-        self.graph_topology = None
+        self.val_dataset: Subset[pyg.data.Dataset]
+        self.test_dataset: Subset[pyg.data.Dataset]
+        self.train_dataset: Subset[pyg.data.Dataset]
+        self.graph_topology: pyg.data.Data
 
         # Init the dataset and build the graph topology
         self.dataset = self.dataset_class(
@@ -298,7 +298,7 @@ def test_dataloader(self) -> pyg.loader.DataLoader:
 class LinkRawData:
     """Link dataset to the use case."""
 
-    def __init__(self, source_raw_data_path, data_path):
+    def __init__(self, source_raw_data_path: str, data_path: str) -> None:
         """Link the source_raw_data_path in the data_path, if it does not already exists."""
         self.source_raw_data_path = source_raw_data_path
         self.local_data_path = data_path
@@ -320,7 +320,7 @@ def __init__(self, source_raw_data_path, data_path):
             else:
                 self.symlink_dataset()
 
-    def symlink_dataset(self):
+    def symlink_dataset(self) -> None:
         """Create the filenames.yaml file from the content of the source_raw_data_path."""
         filenames = os.listdir(self.source_raw_data_path)
         temp_file_path = os.path.join(self.local_data_path, "filenames.yaml")
@@ -336,7 +336,7 @@ def symlink_dataset(self):
                 os.path.join(self.local_raw_data, filename),
             )
 
-    def rm_old_dataset(self):
+    def rm_old_dataset(self) -> None:
         """Clean the local_data_path."""
         for item in ["raw", "filenames.yaml", "processed"]:
             file_location = os.path.join(self.local_data_path, item)
diff --git a/reactive-flows/cnf-combustion/gnns/env.yaml b/reactive-flows/cnf-combustion/gnns/env.yaml
index 0186867e..8bce1bdc 100644
--- a/reactive-flows/cnf-combustion/gnns/env.yaml
+++ b/reactive-flows/cnf-combustion/gnns/env.yaml
@@ -1,7 +1,6 @@
 # This file defines the global environment dependencies for this model project.
 # It is read by the CI/CD workflows in charge of the tests (ai4sim-ci-cd.yaml)
 # and the build of the Docker image (images-ci-cd.yaml) to set the environment.
-python_version: 3.10
 ubuntu_base_image:
   name: nvidia/cuda
   tag: 12.4.1-cudnn-runtime-ubuntu22.04
\ No newline at end of file
diff --git a/reactive-flows/cnf-combustion/gnns/inferer.py b/reactive-flows/cnf-combustion/gnns/inferer.py
index a642de20..7905b6dd 100644
--- a/reactive-flows/cnf-combustion/gnns/inferer.py
+++ b/reactive-flows/cnf-combustion/gnns/inferer.py
@@ -17,7 +17,7 @@
 import pickle
 
 import h5py
-import numpy
+import numpy as np
 import torch
 import torch_geometric as pyg
 from lightning import LightningModule
@@ -47,8 +47,8 @@ def __init__(self, model_path: str, data_path: str, wkd: str = CWD) -> None:
         self.wkd = wkd
 
         # init the additional attributes to None
-        self.model = None
-        self.data = None
+        self.model: torch.nn.Module
+        self.data: pyg.data.Data
 
         # Load the NN from self.model_path file
         logging.info(f"Start loading NN model from {self.model_path} ...")
@@ -63,14 +63,13 @@ def load_nn(self) -> None:
         self.model = torch.load(self.model_path, weights_only=False)
         self.model.eval()
 
-    def load_data(self) -> None:
+    def load_data(self) -> np.ndarray:
         """Load the input data that will be injected in the model to process the inference.
         Provide the self.data.
 
         Raises:
             NotImplementedError
         """
-        self.data = ...
         raise NotImplementedError
 
     @property
@@ -87,7 +86,6 @@ def preprocess(self, save: bool = False) -> None:
         Args:
             save (bool): If the preprocessed data will be saved in a file or not. Default, False.
         """
-        self.data = ...
         raise NotImplementedError
 
     def predict(self) -> torch.Tensor:
@@ -108,7 +106,7 @@ def __init__(
         self,
         model_path: str,
         data_path: str,
-        model_class: LightningModule,
+        model_class: type[LightningModule],
         wkd: str = CWD,
     ) -> None:
         """Init the InferencePthGnn class with additional args required (model_class).
@@ -130,35 +128,35 @@ class that generated the model (provided as input of the class, "model_class").
         self.model = self.model_class.load_from_checkpoint(self.model_path)
         self.model.eval()
 
-    def load_data(self) -> numpy.ndarray:
+    def load_data(self) -> np.ndarray:
         """Load the input data that will be injected in the model to process the inference.
 
         Returns:
-            (numpy.ndarray): the array of the data to be inferred.
+            (np.ndarray): the array of the data to be inferred.
         """
         with h5py.File(self.data_path, "r") as file:
             feat = file["/c_filt"][:]
             logging.info("Data input R3 loaded")
             return feat
 
-    def load_y_dns(self) -> numpy.ndarray:
+    def load_y_dns(self) -> np.ndarray:
         """Load the ground truth data provided in the input file, to be used in a post inference
         validation step. These data corresponds to the DNS ones.
 
         Returns:
-            (numpy.ndarray): the array of the DNS data.
+            (np.ndarray): the array of the DNS data.
         """
         with h5py.File(self.data_path, "r") as file:
             y_gt = file["/c_grad_filt"][:]
             logging.info("Data ground truth R3 loaded")
             return y_gt
 
-    def load_y_les(self) -> numpy.ndarray:
+    def load_y_les(self) -> np.ndarray:
         """Load additional data provided in the input file, to be used in a post inference
         validation step. These data corresponds to the LES ones.
 
         Returns:
-            (numpy.ndarray): the array of the LES data.
+            (np.ndarray): the array of the LES data.
         """
         with h5py.File(self.data_path, "r") as file:
             y_les = file["/c_filt_grad"][:]
@@ -182,7 +180,7 @@ def _create_graph(self, feat: torch.Tensor, save: bool = False) -> None:
             grid_shape = (z_size, y_size, x_size)
             graph_topo = create_graph_topo(grid_shape)
             self.data = pyg.data.Data(
-                x=feat.reshape(-1, 1).clone().detach().type(torch.FloatTensor),
+                x=feat.reshape(-1, 1).clone().detach().type(torch.float32),
                 edge_index=graph_topo.edge_index.clone()
                 .detach()
                 .type(torch.LongTensor),
@@ -192,7 +190,7 @@ def _create_graph(self, feat: torch.Tensor, save: bool = False) -> None:
                 with open(self.data_processed_path, "wb") as file:
                     pickle.dump(self.data, file)
 
-    def preprocess(self, save: bool = False):
+    def preprocess(self, save: bool = False) -> None:
         """Preprocess the input data to provide the self.data.
 
         Args:
diff --git a/reactive-flows/cnf-combustion/gnns/models.py b/reactive-flows/cnf-combustion/gnns/models.py
index 64aa48b2..dbf48c32 100755
--- a/reactive-flows/cnf-combustion/gnns/models.py
+++ b/reactive-flows/cnf-combustion/gnns/models.py
@@ -32,15 +32,19 @@ class CombustionModule(pl.LightningModule):
     Loss is MSE and the metric of interest is R2 determination score.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         """Init the CombustionModule class."""
         super().__init__()
-        self.grid_shape = None
+        self.model: nn.Module
+        self.graph_topology: pyg.data.Data
+        self.grid_shape: Tuple[int, int, int]
+        self.lr: float
 
-        self.ys_test = list()
-        self.y_hats_test = list()
+        self.ys_test: List[torch.Tensor] = []
+        self.y_hats_test: List[torch.Tensor] = []
 
         if self.graph_topology:
+            self.edge_index: torch.Tensor
             self.register_buffer(
                 "edge_index", self.graph_topology.edge_index, persistent=False
             )
@@ -58,8 +62,8 @@ def forward(self, x_val: torch.Tensor, edge_index: torch.Tensor) -> torch.Tensor
         return self.model(x_val, edge_index)
 
     def _common_step(
-        self, batch: torch.Tensor, batch_idx: int, stage: str
-    ) -> List[torch.Tensor]:
+        self, batch: pyg.data.Data, batch_idx: int, stage: str
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """Define the common operations performed on data."""
         batch_size = batch.ptr[0] - 1
         y_hat = self(batch.x, self.edge_index)
@@ -73,11 +77,11 @@ def _common_step(
 
         return y_hat, loss, r2
 
-    def training_step(self, batch: torch.Tensor, batch_idx: int) -> torch.Tensor:
+    def training_step(self, batch: pyg.data.Data, batch_idx: int) -> torch.Tensor:
         """Compute one training step.
 
         Args:
-            batch (torch.Tensor): Batch containing nodes features and connectivity matrix.
+            batch (pyg.data.Data): Batch containing nodes features and connectivity matrix.
             batch_idx (int): Batch index.
 
         Returns:
@@ -86,21 +90,21 @@ def training_step(self, batch: torch.Tensor, batch_idx: int) -> torch.Tensor:
         _, loss, _ = self._common_step(batch, batch_idx, "train")
         return loss
 
-    def validation_step(self, batch: torch.Tensor, batch_idx: int) -> None:
+    def validation_step(self, batch: pyg.data.Data, batch_idx: int) -> None:
         """Compute one validation step.
 
         Args:
-            batch (torch.Tensor): Batch containing nodes features and connectivity matrix.
+            batch (pyg.data.Data): Batch containing nodes features and connectivity matrix.
             batch_idx (int): Batch index.
         """
         y_hat, _, _ = self._common_step(batch, batch_idx, "val")
 
-    def test_step(self, batch: torch.Tensor, batch_idx: int) -> Tuple[torch.Tensor]:
+    def test_step(self, batch: pyg.data.Data, batch_idx: int) -> None:
         """Compute one testing step. Additionally, also generates outputs to plots for the test
         Dataset.
 
         Args:
-            batch (torch.Tensor): Batch containing nodes features and connectivity matrix.
+            batch (pyg.data.Data): Batch containing nodes features and connectivity matrix.
             batch_idx (int): Batch index.
 
         Returns:
@@ -108,14 +112,12 @@ def test_step(self, batch: torch.Tensor, batch_idx: int) -> Tuple[torch.Tensor]:
         """
         y_hat, _, _ = self._common_step(batch, batch_idx, "test")
 
-        if not self.grid_shape:
+        if not hasattr(self, "grid_shape"):
             self.grid_shape = self.graph_topology.grid_shape
 
         self.ys_test.append(batch.y)
         self.y_hats_test.append(y_hat)
 
-        return batch.y, y_hat
-
     def on_test_epoch_end(self) -> None:
         """Gather all the outputs from the test_step to plot the test Dataset."""
         ys = torch.stack(self.ys_test)
@@ -126,11 +128,11 @@ def on_test_epoch_end(self) -> None:
         self.y_hats = self.all_gather(y_hats)
 
         # Reshape the outputs to the original grid shape plus the batch dimension
-        self.ys = self.ys.squeeze().view((-1,) + self.grid_shape).detach().cpu().numpy()
-        self.y_hats = (
-            self.y_hats.squeeze().view((-1,) + self.grid_shape).detach().cpu().numpy()
-        )
-
+        assert isinstance(self.ys, torch.Tensor)
+        assert isinstance(self.y_hats, torch.Tensor)
+        self.ys = self.ys.squeeze().view((-1,) + self.grid_shape).detach().cpu()
+        self.y_hats = self.y_hats.squeeze().view((-1,) + self.grid_shape).detach().cpu()
+        assert self.trainer.log_dir is not None, "Trainer log directory is not set."
         plots_path = os.path.join(self.trainer.log_dir, "plots")
         if self.trainer.is_global_zero:
             if not os.path.exists(plots_path):
@@ -139,11 +141,13 @@ def on_test_epoch_end(self) -> None:
             self.plotter = plotters.Plotter(
                 self.model.__class__.__name__, plots_path, self.grid_shape
             )
-            self.plotter.cross_section((self.ys.shape[1] // 2), self.ys, self.y_hats)
-            self.plotter.dispersion_plot(self.ys, self.y_hats)
-            self.plotter.histo(self.ys, self.y_hats)
-            self.plotter.histo2d(self.ys, self.y_hats)
-            self.plotter.boxplot(self.ys, self.y_hats)
+            self.plotter.cross_section(
+                (self.ys.shape[1] // 2), self.ys.numpy(), self.y_hats.numpy()
+            )
+            self.plotter.dispersion_plot(self.ys.numpy(), self.y_hats.numpy())
+            self.plotter.histo(self.ys.numpy(), self.y_hats.numpy())
+            self.plotter.histo2d(self.ys.numpy(), self.y_hats.numpy())
+            self.plotter.boxplot(self.ys.numpy(), self.y_hats.numpy())
 
     def configure_optimizers(self) -> optim.Optimizer:
         """Set the model optimizer.
diff --git a/reactive-flows/cnf-combustion/gnns/mypy.ini b/reactive-flows/cnf-combustion/gnns/mypy.ini
new file mode 100644
index 00000000..ab71faa5
--- /dev/null
+++ b/reactive-flows/cnf-combustion/gnns/mypy.ini
@@ -0,0 +1,18 @@
+[mypy]
+
+mypy_path = reactive-flows/cnf-combustion/gnns
+
+# Override mypy default value `ignore_missing_imports = false` to `true` for modules with no typing
+# Has the effect of silencing `import-untyped` error
+[mypy-h5py]
+ignore_missing_imports = true
+[mypy-networkx]
+ignore_missing_imports = true
+[mypy-torch_geometric]
+ignore_missing_imports = true
+[mypy-plotly]
+ignore_missing_imports = true
+[mypy-plotly.graph_objects]
+ignore_missing_imports = true
+[mypy-plotly.subplots]
+ignore_missing_imports = true
\ No newline at end of file
diff --git a/reactive-flows/cnf-combustion/gnns/noxfile.py b/reactive-flows/cnf-combustion/gnns/noxfile.py
index 2585a153..517a6f3c 100644
--- a/reactive-flows/cnf-combustion/gnns/noxfile.py
+++ b/reactive-flows/cnf-combustion/gnns/noxfile.py
@@ -20,6 +20,8 @@
 import os
 import sys
 
+import nox
+
 # Insert the tools/nox folder to the python path to fetch the nox_ref_file.py content
 current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 common_dir = os.path.dirname(os.path.dirname(os.path.dirname(current_dir)))
@@ -30,3 +32,4 @@
 from nox_ref_file import *
 
 # Insert below the model project specific targets
+nox.needs_version = "==2026.2.9"
diff --git a/reactive-flows/cnf-combustion/gnns/plotters.py b/reactive-flows/cnf-combustion/gnns/plotters.py
index 216c49f7..9e273933 100644
--- a/reactive-flows/cnf-combustion/gnns/plotters.py
+++ b/reactive-flows/cnf-combustion/gnns/plotters.py
@@ -13,13 +13,15 @@
 # limitations under the License.
 
 import os
+from collections.abc import Sequence
 
 import matplotlib.pyplot as plt
 import numpy as np
 import plotly.graph_objects as go
-import torch
 from matplotlib.colors import LogNorm
 from matplotlib.lines import Line2D
+from matplotlib.patches import Polygon
+from numpy._typing import ArrayLike
 from plotly.subplots import make_subplots
 
 plt.style.use("bmh")
@@ -31,9 +33,9 @@ class Plotter:
 
     def __init__(
         self,
-        model_type: torch.Tensor,
+        model_type: str,
         plots_path: str,
-        grid_shape: torch.Tensor,
+        grid_shape: tuple[int, int, int],
     ) -> None:
         """Init the Plotter."""
         self.model_type = model_type
@@ -95,7 +97,11 @@ def histo(self, y_val: np.ndarray, y_hat: np.ndarray) -> None:
         ax.set_xlabel(r"$\overline{\Sigma}$")
         # Create new legend handles but use the colors from the existing ones
         handles, labels = ax.get_legend_handles_labels()
-        new_handles = [Line2D([], [], c=h.get_edgecolor()) for h in handles]
+        new_handles = []
+        for h in handles:
+            if isinstance(h, Polygon):
+                # Create a Line2D object with the same color as the Polygon
+                new_handles.append(Line2D([], [], color=h.get_edgecolor()))
 
         plt.legend(handles=new_handles, labels=labels)
         plt.savefig(os.path.join(self.plots_path, f"histogram-{self.model_type}.png"))
@@ -127,7 +133,11 @@ def boxplot(self, y_val: np.ndarray, y_hat: np.ndarray) -> None:
             )
 
         fig, ax = plt.subplots(figsize=(20, 10))
-        ax.boxplot(flat_err, labels=np.arange(y_val.shape[0]), showmeans=True)
+        ax.boxplot(
+            flat_err,
+            label=[str(idx) for idx in np.arange(y_val.shape[0])],
+            showmeans=True,
+        )
         ax.set_xlabel("Snapshot")
         ax.set_ylabel(f"$RMSE$({self.label_predicted}, {self.label_target})")
         plt.savefig(os.path.join(self.plots_path, f"boxplot-{self.model_type}"))
@@ -135,10 +145,10 @@ def boxplot(self, y_val: np.ndarray, y_hat: np.ndarray) -> None:
 
     def total_flame_surface(
         self,
-        y_target: np.ndarray,
-        y_hat: np.ndarray,
-        y_hat_2: np.ndarray = None,
-        y_hat_3: np.ndarray = None,
+        y_target: Sequence[ArrayLike],
+        y_hat: Sequence[ArrayLike],
+        y_hat_2: Sequence[ArrayLike] = None,
+        y_hat_3: Sequence[ArrayLike] = None,
         target_title: str = "Ground Truth",
         pred_title: str = "Prediction GNN",
         pred_2_title: str = "Prediction CNN",
diff --git a/reactive-flows/cnf-combustion/gnns/pyproject.toml b/reactive-flows/cnf-combustion/gnns/pyproject.toml
new file mode 100644
index 00000000..aa8799cc
--- /dev/null
+++ b/reactive-flows/cnf-combustion/gnns/pyproject.toml
@@ -0,0 +1,66 @@
+[project]
+name = "cnf-combustion-gnns"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    "h5py==3.12.1",
+    "jsonargparse[signatures]==4.35.0",
+    "kaleido==0.2.1",
+    "lightning==2.4.0",
+    "matplotlib==3.9.4",
+    "numpy==2.2.0",
+    "omegaconf==2.3.0",
+    "plotly==5.24.1",
+    "tensorboard==2.18.0",
+    "torch==2.8.0+cu128",
+    "torch-geometric==2.6.1",
+    "torch-optimizer==0.3.0",
+    "torch_scatter==2.1.2+pt28cu128",
+    "torch_sparse==0.6.18+pt28cu128"
+]
+
+# Nox will find here the python version that will be used
+classifiers = [
+  "Programming Language :: Python :: 3.10",
+]
+
+[dependency-groups]
+tests = [
+    "pytest-cov==7.0.0"
+]
+coverage = [
+    "coverage==7.13.4"
+]
+black = [
+    "black==26.1.0"
+]
+isort = [
+    "isort==7.0.0"
+]
+mypy = [
+    "mypy==1.19.1",
+    "types-PyYAML==6.0.12"
+]
+lint = [
+    "flake8==7.3.0",
+    "flake8-docstrings==1.7.0",
+    "flake8-use-fstring==1.4",
+    "flake8-variables-names==0.0.6",
+    "pep8-naming==0.15.1"
+]
+data = []
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch" },
+]
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl"
+explicit = true
+
+[tool.uv]
+find-links = [
+    "https://data.pyg.org/whl/torch-2.8.0+cu128.html"
+]
diff --git a/reactive-flows/cnf-combustion/gnns/requirements.txt b/reactive-flows/cnf-combustion/gnns/requirements.txt
deleted file mode 100644
index 5497e172..00000000
--- a/reactive-flows/cnf-combustion/gnns/requirements.txt
+++ /dev/null
@@ -1,113 +0,0 @@
-absl-py==2.1.0
-aiohappyeyeballs==2.4.4
-aiohttp==3.11.10
-aiosignal==1.3.1
-antlr4-python3-runtime==4.9.3
-argcomplete==3.5.2
-async-timeout==5.0.1
-attrs==24.2.0
-cachetools==5.5.0
-certifi==2024.8.30
-charset-normalizer==3.4.0
-colorlog==6.9.0
-contourpy==1.3.1
-cycler==0.12.1
-distlib==0.3.9
-docstring_parser==0.16
-filelock==3.16.1
-fire==0.7.0
-fonttools==4.55.3
-frozenlist==1.5.0
-fsspec==2024.10.0
-future==1.0.0
-google-auth==2.37.0
-google-auth-oauthlib==1.2.1
-googledrivedownloader==0.4
-grpcio==1.68.1
-h5py==3.12.1
-idna==3.10
-importlib_metadata==8.5.0
-importlib_resources==6.5.2
-isodate==0.7.2
-Jinja2==3.1.5
-joblib==1.4.2
-jsonargparse==4.34.1
-kaleido==0.2.1
-kiwisolver==1.4.7
-lightning==2.4.0
-lightning-utilities==0.11.9
-Markdown==3.7
-MarkupSafe==3.0.2
-matplotlib==3.9.4
-mpmath==1.3.0
-multidict==6.1.0
-networkx==3.4.2
-nox==2024.10.9
-numpy==2.2.0
-nvidia-cublas-cu12==12.4.5.8
-nvidia-cuda-cupti-cu12==12.4.127
-nvidia-cuda-nvrtc-cu12==12.4.127
-nvidia-cuda-runtime-cu12==12.4.127
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.2.1.3
-nvidia-curand-cu12==10.3.5.147
-nvidia-cusolver-cu12==11.6.1.9
-nvidia-cusparse-cu12==12.3.1.170
-nvidia-nccl-cu12==2.21.5
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.4.127
-oauthlib==3.2.2
-omegaconf==2.3.0
-packaging==24.2
-pandas==2.2.3
-pillow==11.0.0
-pip==24.3.1
-platformdirs==4.3.6
-plotly==5.24.1
-propcache==0.2.1
-protobuf==5.29.1
-psutil==6.1.0
-pyasn1==0.6.1
-pyasn1_modules==0.4.1
-pyDeprecate==0.3.2
-pyg-lib==0.4.0+pt26cu124
-pyparsing==3.2.0
-python-dateutil==2.9.0.post0
-pytorch-lightning==2.4.0
-pytorch-ranger==0.1.1
-pytz==2024.2
-PyYAML==6.0.2
-randomname==0.2.1
-rdflib==7.1.1
-requests==2.32.3
-requests-oauthlib==2.0.0
-rsa==4.9
-scikit-learn==1.6.0
-scipy==1.14.1
-setuptools==75.1.0
-six==1.17.0
-sympy==1.13.1
-tenacity==9.0.0
-tensorboard==2.18.0
-tensorboard-data-server==0.7.2
-termcolor==2.5.0
-threadpoolctl==3.5.0
-tomli==2.2.1
-torch==2.6.0+cu124
-torch-geometric==2.6.1
-torch-optimizer==0.3.0
-torch_scatter==2.1.2+pt26cu124
-torch_sparse==0.6.18+pt26cu124
-torchmetrics==1.6.0
-tqdm==4.67.1
-triton==3.2.0
-typeshed_client==2.7.0
-typing_extensions==4.12.2
-tzdata==2024.2
-urllib3==2.2.3
-virtualenv==20.28.0
-Werkzeug==3.1.3
-wheel==0.44.0
-yacs==0.1.8
-yarl==1.18.3
-zipp==3.21.0
diff --git a/reactive-flows/cnf-combustion/gnns/tests/test_data.py b/reactive-flows/cnf-combustion/gnns/tests/test_data.py
index ad05a388..fb91dea9 100644
--- a/reactive-flows/cnf-combustion/gnns/tests/test_data.py
+++ b/reactive-flows/cnf-combustion/gnns/tests/test_data.py
@@ -17,6 +17,7 @@
 import unittest
 import warnings
 from copy import copy
+from typing import TypedDict
 
 import h5py
 import numpy as np
@@ -26,21 +27,31 @@
 from data import CnfDataModule, CnfDataset, LinkRawData
 
 
+class InitParam(TypedDict):
+    """Type definition for the data module initialization parameters."""
+
+    batch_size: int
+    num_workers: int
+    y_normalizer: float
+    splitting_ratios: tuple[float, float, float]
+    data_path: str
+
+
 class TestData(unittest.TestCase):
     """Data test suite."""
 
     def setUp(self) -> None:
         """Define default parameters."""
         self.filenames = ["DNS1_00116000.h5", "DNS1_00117000.h5", "DNS1_00118000.h5"]
-        self.init_param = {
+        self.init_param: InitParam = {
             "batch_size": 1,
             "num_workers": 0,
             "y_normalizer": 342.553,
-            "splitting_ratios": [0.8, 0.1, 0.1],
+            "splitting_ratios": (0.8, 0.1, 0.1),
             "data_path": "./data",
         }
 
-    def create_env(self, tempdir):
+    def create_env(self, tempdir: str) -> None:
         """Create a test environment and data test."""
         os.mkdir(os.path.join(tempdir, "data"))
         os.mkdir(os.path.join(tempdir, "data", "raw"))
@@ -55,13 +66,13 @@ def create_env(self, tempdir):
         with open(temp_file_path, "w") as tmpfile:
             _ = yaml.dump(self.filenames, tmpfile)
 
-    def create_obj_rm_warning(self, path):
+    def create_obj_rm_warning(self, path: str) -> CnfDataset:
         """Instantiante the CombustionDataset object with a warning filtering."""
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             return CnfDataset(path)
 
-    def test_raw_file_names(self):
+    def test_raw_file_names(self) -> None:
         """Test raw file name."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -70,7 +81,7 @@ def test_raw_file_names(self):
             raw_test = data_test.raw_file_names
             self.assertEqual(len(self.filenames), len(raw_test))
 
-    def test_processed_file_names(self):
+    def test_processed_file_names(self) -> None:
         """Test processed file name."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -80,17 +91,17 @@ def test_processed_file_names(self):
 
             self.assertEqual(len(self.filenames), len(processed_test))
 
-    def test_download(self):
+    def test_download(self) -> None:
         """Test download raise error."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
             data_test = self.create_obj_rm_warning(os.path.join(tempdir, "data"))
 
             with self.assertRaises(RuntimeError) as context:
-                _ = data_test.download()
+                data_test.download()
                 self.assertTrue("Data not found." in str(context.exception))
 
-    def test_get(self):
+    def test_get(self) -> None:
         """Test download raise error."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -99,7 +110,7 @@ def test_get(self):
 
             self.assertEqual(len(data_get.x), 10 * 10 * 10)
 
-    def test_setup(self):
+    def test_setup(self) -> None:
         """Test the "setup" method."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -110,7 +121,7 @@ def test_setup(self):
             dataset_test = CnfDataModule(**init_param)
 
             with self.assertRaises(ValueError) as context:
-                dataset_test.setup(stage=None)
+                dataset_test.setup(stage="test")
                 self.assertTrue(
                     "The dataset is too small to be split properly."
                     in str(context.exception)
@@ -120,7 +131,7 @@ def test_setup(self):
             self.assertEqual(len(dataset_test.val_dataset), 0)
             self.assertEqual(len(dataset_test.test_dataset), 1)
 
-    def test_train_dataloader(self):
+    def test_train_dataloader(self) -> None:
         """Test the "train_dataloader"."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -131,12 +142,12 @@ def test_train_dataloader(self):
             dataset_test = CnfDataModule(**init_param)
 
             with self.assertRaises(ValueError):
-                _ = dataset_test.setup(stage=None)
+                dataset_test.setup(stage="fit")
 
             test_train_dl = dataset_test.train_dataloader()
             self.assertTrue(isinstance(test_train_dl, torch.utils.data.DataLoader))
 
-    def test_val_dataloader(self):
+    def test_val_dataloader(self) -> None:
         """Test the "val_dataloader"."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -147,12 +158,12 @@ def test_val_dataloader(self):
             dataset_test = CnfDataModule(**init_param)
 
             with self.assertRaises(ValueError):
-                _ = dataset_test.setup(stage=None)
+                dataset_test.setup(stage="fit")
 
             test_val_dl = dataset_test.val_dataloader()
             self.assertTrue(isinstance(test_val_dl, torch.utils.data.DataLoader))
 
-    def test_test_dataloader(self):
+    def test_test_dataloader(self) -> None:
         """Test the "test_dataloader"."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -163,7 +174,7 @@ def test_test_dataloader(self):
             dataset_test = CnfDataModule(**init_param)
 
             with self.assertRaises(ValueError):
-                _ = dataset_test.setup(stage=None)
+                dataset_test.setup(stage="test")
 
             test_test_dl = dataset_test.test_dataloader()
             self.assertTrue(isinstance(test_test_dl, torch.utils.data.DataLoader))
@@ -172,7 +183,7 @@ def test_test_dataloader(self):
 class TestLinkRawData(unittest.TestCase):
     """LinkRawData test suite."""
 
-    def create_env(self, tempdir):
+    def create_env(self, tempdir: str) -> None:
         """Create a test environment and data test."""
         os.makedirs(os.path.join(tempdir, "raw_data"), exist_ok=True)
         os.makedirs(os.path.join(tempdir, "local_data"), exist_ok=True)
@@ -190,13 +201,13 @@ def create_env(self, tempdir):
         with open(temp_file_path, "w") as tmpfile:
             _ = yaml.dump(self.filenames, tmpfile)
 
-    def create_obj_rm_warning(self, raw_path, local_path):
+    def create_obj_rm_warning(self, raw_path: str, local_path: str) -> LinkRawData:
         """Instantiante the CombustionDataset object with a warning filtering."""
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             return LinkRawData(raw_path, local_path)
 
-    def test_linkrawdata(self):
+    def test_linkrawdata(self) -> None:
         """Test the LinkRawData class."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
diff --git a/reactive-flows/cnf-combustion/gnns/tests/test_inferer.py b/reactive-flows/cnf-combustion/gnns/tests/test_inferer.py
index 8a7692a0..6336a478 100644
--- a/reactive-flows/cnf-combustion/gnns/tests/test_inferer.py
+++ b/reactive-flows/cnf-combustion/gnns/tests/test_inferer.py
@@ -72,6 +72,8 @@ def tearDown(self) -> None:
 class TestInferencePthGnn(unittest.TestCase):
     """InferencePthGnn test suite."""
 
+    infer_data: str
+
     @classmethod
     def setUpClass(cls) -> None:
         """Init the global data used."""
@@ -152,7 +154,7 @@ def test_create_graph_existing(self) -> None:
 
     def test_preprocess(self) -> None:
         """Test the preprocess method set the self.data attribute."""
-        self.assertIsNone(self.inferer.data)
+        self.assertFalse(hasattr(self.inferer, "data"))
         self.inferer.preprocess()
         self.assertIsInstance(self.inferer.data, pyg.data.Data)
 
diff --git a/reactive-flows/cnf-combustion/gnns/tests/test_models.py b/reactive-flows/cnf-combustion/gnns/tests/test_models.py
index 013e6381..cabe5a89 100644
--- a/reactive-flows/cnf-combustion/gnns/tests/test_models.py
+++ b/reactive-flows/cnf-combustion/gnns/tests/test_models.py
@@ -23,11 +23,63 @@
 import torch_optimizer as optim
 import yaml
 from lightning.pytorch.trainer import Trainer
+from typing_extensions import NotRequired, TypedDict
 
 from models import LitGAT, LitGCN, LitGIN, LitGraphUNet
 from utils import create_graph_topo
 
 
+class InitParam(TypedDict):
+    """Type definition for the model initialization parameters."""
+
+    in_channels: int
+    hidden_channels: int
+    out_channels: int
+    num_layers: int
+    dropout: float
+    jk: str
+    lr: float
+    graph_topology: NotRequired[pyg.data.Data | None]
+
+
+class GinInitParam(TypedDict):
+    """Type definition for the GIN model initialization parameters."""
+
+    in_channels: int
+    hidden_channels: int
+    out_channels: int
+    num_layers: int
+    dropout: float
+    lr: float
+    graph_topology: NotRequired[pyg.data.Data | None]
+
+
+class GatInitParam(TypedDict):
+    """Type definition for the GAT model initialization parameters."""
+
+    in_channels: int
+    hidden_channels: int
+    out_channels: int
+    num_layers: int
+    dropout: float
+    heads: int
+    jk: str
+    lr: float
+    graph_topology: NotRequired[pyg.data.Data | None]
+
+
+class GunetInitParam(TypedDict):
+    """Type definition for the GraphUNet model initialization parameters."""
+
+    in_channels: int
+    hidden_channels: int
+    out_channels: int
+    depth: int
+    pool_ratios: float
+    lr: float
+    graph_topology: NotRequired[pyg.data.Data | None]
+
+
 class TestModel(unittest.TestCase):
     """Model test file."""
 
@@ -35,7 +87,7 @@ def setUp(self) -> None:
         """Define default parameters."""
         self.filenames = ["DNS1_00116000.h5", "DNS1_00117000.h5", "DNS1_00118000.h5"]
 
-        self.init_param = {
+        self.init_param: InitParam = {
             "in_channels": 1,
             "hidden_channels": 32,
             "out_channels": 1,
@@ -45,7 +97,7 @@ def setUp(self) -> None:
             "lr": 0.0001,
         }
 
-    def create_env(self, tempdir):
+    def create_env(self, tempdir: str) -> None:
         """Create a test environment and data files test."""
         os.mkdir(os.path.join(tempdir, "data"))
         os.mkdir(os.path.join(tempdir, "data", "raw"))
@@ -60,7 +112,7 @@ def create_env(self, tempdir):
         with open(temp_file_path, "w") as tmpfile:
             _ = yaml.dump(self.filenames, tmpfile)
 
-    def create_graph(self, file_path):
+    def create_graph(self, file_path: str) -> tuple[pyg.data.Data, pyg.data.Data]:
         """Create a test graph."""
         with h5py.File(file_path, "r") as file:
             col = file["/filt_8"][:]
@@ -74,7 +126,7 @@ def create_graph(self, file_path):
 
         return data, graph_topo
 
-    def test_forward(self):
+    def test_forward(self) -> None:
         """Test the "forward" method generates a Tensor."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
@@ -87,9 +139,9 @@ def test_forward(self):
 
             self.assertTrue(isinstance(test_forward, torch.Tensor))
 
-    def test_common_step(self):
+    def test_common_step(self) -> None:
         """Test the "_common_step" method returns a 3 length tuple."""
-        gin_init_param = {
+        gin_init_param: GinInitParam = {
             "in_channels": 1,
             "hidden_channels": 32,
             "out_channels": 1,
@@ -111,9 +163,9 @@ def test_common_step(self):
 
             self.assertEqual(len(loss), 3)
 
-    def test_training_step(self):
+    def test_training_step(self) -> None:
         """Test the "training_step" method returns a Tensor."""
-        gat_init_param = {
+        gat_init_param: GatInitParam = {
             "in_channels": 1,
             "hidden_channels": 32,
             "out_channels": 1,
@@ -136,9 +188,9 @@ def test_training_step(self):
             loss = test_gat.training_step(batch=batch, batch_idx=1)
             self.assertTrue(isinstance(loss, torch.Tensor))
 
-    def test_test_step(self):
+    def test_test_step(self) -> None:
         """Test the "test_step" method returns a tuple of same size Tensors."""
-        gunet_init_param = {
+        gunet_init_param: GunetInitParam = {
             "in_channels": 1,
             "hidden_channels": 32,
             "out_channels": 1,
@@ -156,19 +208,22 @@ def test_test_step(self):
             test_gunet = LitGraphUNet(**gunet_init_param)
             batch = pyg.data.Batch.from_data_list([data_test, data_test])
 
-            out_tuple = test_gunet.test_step(batch=batch, batch_idx=1)
+            test_gunet.test_step(batch=batch, batch_idx=1)
 
-            self.assertEqual(len(out_tuple), 2)
-            self.assertEqual(out_tuple[0].size(), out_tuple[1].size())
+            self.assertEqual(
+                test_gunet.ys_test[0].size(), test_gunet.y_hats_test[0].size()
+            )
 
             test_gunet._trainer = Trainer()
             test_gunet.on_test_epoch_end()
-
+            assert (
+                test_gunet.trainer.log_dir is not None
+            ), "Trainer log directory is not set."
             self.assertTrue(
                 os.path.exists(os.path.join(test_gunet.trainer.log_dir, "plots"))
             )
 
-    def test_configure_optimizers(self):
+    def test_configure_optimizers(self) -> None:
         """Test the "configure_optimizers" method returns an optim.Optimizer."""
         with tempfile.TemporaryDirectory() as tempdir:
             self.create_env(tempdir)
diff --git a/reactive-flows/cnf-combustion/gnns/tests/test_plotter.py b/reactive-flows/cnf-combustion/gnns/tests/test_plotter.py
index 60911b4d..aa1be77e 100644
--- a/reactive-flows/cnf-combustion/gnns/tests/test_plotter.py
+++ b/reactive-flows/cnf-combustion/gnns/tests/test_plotter.py
@@ -38,7 +38,7 @@ def setUp(self) -> None:
             [1 + np.random.randn(grid, grid, grid) for _ in range(self.f_num)], axis=0
         )
 
-    def test_dispersion_plot(self):
+    def test_dispersion_plot(self) -> None:
         """Test the "dispersion_plot" produces the result file."""
         with tempfile.TemporaryDirectory() as tempdir:
             tt = plotters.Plotter(self.model_type, tempdir, self.grid)
@@ -51,7 +51,7 @@ def test_dispersion_plot(self):
                 )
             )
 
-    def test_histo(self):
+    def test_histo(self) -> None:
         """Test the "histo" produces the result file."""
         with tempfile.TemporaryDirectory() as tempdir:
             tt = plotters.Plotter(self.model_type, tempdir, self.grid)
@@ -64,7 +64,7 @@ def test_histo(self):
                 )
             )
 
-    def test_histo2d(self):
+    def test_histo2d(self) -> None:
         """Test the "histo2d" produces the result file."""
         with tempfile.TemporaryDirectory() as tempdir:
 
@@ -78,7 +78,7 @@ def test_histo2d(self):
                 )
             )
 
-    def test_boxplot(self):
+    def test_boxplot(self) -> None:
         """Test the "boxplot" produces the result file."""
         with tempfile.TemporaryDirectory() as tempdir:
             tt = plotters.Plotter(self.model_type, tempdir, self.grid)
@@ -89,7 +89,7 @@ def test_boxplot(self):
                 os.path.exists(os.path.join(tempdir, f"boxplot-{self.model_type}.png"))
             )
 
-    def test_total_flame_surface(self):
+    def test_total_flame_surface(self) -> None:
         """Test the "total_flame_surface" produces the result files."""
         with tempfile.TemporaryDirectory() as tempdir:
             tt = plotters.Plotter(self.model_type, tempdir, self.grid)
@@ -118,7 +118,7 @@ def test_total_flame_surface(self):
                 )
             )
 
-    def test_cross_section(self):
+    def test_cross_section(self) -> None:
         """Test the "cross_section" produces the result files."""
         with tempfile.TemporaryDirectory() as tempdir:
             tt = plotters.Plotter(self.model_type, tempdir, self.grid)
diff --git a/reactive-flows/cnf-combustion/gnns/tests/test_utils.py b/reactive-flows/cnf-combustion/gnns/tests/test_utils.py
index 4047421c..6ea2b3a2 100644
--- a/reactive-flows/cnf-combustion/gnns/tests/test_utils.py
+++ b/reactive-flows/cnf-combustion/gnns/tests/test_utils.py
@@ -20,7 +20,7 @@
 class TestUtils(unittest.TestCase):
     """Utils function test suite."""
 
-    def test_create_graph_topo(self):
+    def test_create_graph_topo(self) -> None:
         """Test the "create_graph_topo" function."""
         topo = create_graph_topo((10, 10, 10))
         self.assertEqual(topo.num_nodes, 1000)
diff --git a/reactive-flows/cnf-combustion/gnns/trainer.py b/reactive-flows/cnf-combustion/gnns/trainer.py
index f392eca8..7e84326f 100755
--- a/reactive-flows/cnf-combustion/gnns/trainer.py
+++ b/reactive-flows/cnf-combustion/gnns/trainer.py
@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lightning.pytorch.cli import LightningCLI
+from lightning.pytorch.cli import LightningArgumentParser, LightningCLI
 
 
 class MyLightningCLI(LightningCLI):
-    def add_arguments_to_parser(self, parser):
+    def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
         parser.link_arguments(
             "data.graph_topology",
             "model.init_args.graph_topology",
diff --git a/reactive-flows/cnf-combustion/gnns/uv.lock b/reactive-flows/cnf-combustion/gnns/uv.lock
new file mode 100644
index 00000000..8cd27875
--- /dev/null
+++ b/reactive-flows/cnf-combustion/gnns/uv.lock
@@ -0,0 +1,3013 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "(python_full_version == '3.12.*' and platform_machine == 'x86_64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_machine == 'AMD64' and platform_python_implementation == 'CPython' and sys_platform == 'win32')",
+    "(python_full_version >= '3.13' and platform_machine == 'x86_64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_machine == 'AMD64' and platform_python_implementation == 'CPython' and sys_platform == 'win32') or (python_full_version >= '3.12' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version >= '3.12' and platform_machine != 'AMD64' and sys_platform == 'win32') or (python_full_version >= '3.12' and platform_python_implementation != 'CPython' and sys_platform == 'linux') or (python_full_version >= '3.12' and platform_python_implementation != 'CPython' and sys_platform == 'win32') or (python_full_version >= '3.12' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version == '3.11.*' and platform_machine == 'x86_64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_machine == 'AMD64' and platform_python_implementation == 'CPython' and sys_platform == 'win32')",
+    "(python_full_version == '3.11.*' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_machine != 'AMD64' and sys_platform == 'win32') or (python_full_version == '3.11.*' and platform_python_implementation != 'CPython' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_python_implementation != 'CPython' and sys_platform == 'win32') or (python_full_version == '3.11.*' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version < '3.11' and platform_machine == 'x86_64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_machine == 'AMD64' and platform_python_implementation == 'CPython' and sys_platform == 'win32')",
+    "(python_full_version < '3.11' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_machine != 'AMD64' and sys_platform == 'win32') or (python_full_version < '3.11' and platform_python_implementation != 'CPython' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation != 'CPython' and sys_platform == 'win32') or (python_full_version < '3.11' and sys_platform != 'linux' and sys_platform != 'win32')",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+]
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+[[package]]
+name = "aiohttp"
+version = "3.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "async-timeout", marker = "python_full_version < '3.11'" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556, upload-time = "2026-01-03T17:33:05.204Z" }
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+    { url = "https://files.pythonhosted.org/packages/73/ae/b48f95715333080afb75a4504487cbe142cae1268afc482d06692d605ae6/yarl-1.22.0-py3-none-any.whl", hash = "sha256:1380560bdba02b6b6c90de54133c81c9f2a453dee9912fe58c1dcced1edb7cff", size = 46814, upload-time = "2025-10-06T14:12:53.872Z" },
+]
diff --git a/reactive-flows/cnf-combustion/unets/ci/generate_synthetic_data.py b/reactive-flows/cnf-combustion/unets/ci/generate_synthetic_data.py
index 1346c977..d846f055 100644
--- a/reactive-flows/cnf-combustion/unets/ci/generate_synthetic_data.py
+++ b/reactive-flows/cnf-combustion/unets/ci/generate_synthetic_data.py
@@ -23,7 +23,7 @@
 data_path = "./data"
 
 
-def create_data():
+def create_data() -> None:
     """Create data folder with fake raw data"""
     filenames = ["test_1.h5", "test_2.h5", "test_3.h5"]
 
diff --git a/reactive-flows/cnf-combustion/unets/data.py b/reactive-flows/cnf-combustion/unets/data.py
index 68fe338b..1bafd5f6 100755
--- a/reactive-flows/cnf-combustion/unets/data.py
+++ b/reactive-flows/cnf-combustion/unets/data.py
@@ -12,11 +12,11 @@
 
 from os import listdir, makedirs
 from os.path import isfile, join
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 from h5py import File
 from lightning import LightningDataModule
-from torch import load, save, tensor
+from torch import Tensor, load, save, tensor
 from torch.utils.data import DataLoader, Dataset, random_split
 
 from utils import RandomCropper3D
@@ -29,7 +29,7 @@ def __init__(
         root: str,
         y_normalizer: float = None,
         subblock_shape: Union[int, tuple] = None,
-    ):
+    ) -> None:
         super().__init__()
         self.root = root
         self.y_normalizer = y_normalizer
@@ -43,28 +43,28 @@ def __init__(
                 self.process(i, raw_path)
 
     @property
-    def raw_dir(self):
+    def raw_dir(self) -> str:
         return join(self.root, "raw")
 
     @property
-    def processed_dir(self):
+    def processed_dir(self) -> str:
         return join(self.root, "processed")
 
     @property
-    def raw_filenames(self):
+    def raw_filenames(self) -> List[str]:
         return listdir(self.raw_dir)
 
     @property
-    def processed_filenames(self):
+    def processed_filenames(self) -> List[str]:
         return [f"{filename.split('.')[0]}.pt" for filename in self.raw_filenames]
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self.raw_filenames)
 
-    def __getitem__(self, i):
+    def __getitem__(self, i: int) -> Tuple[Tensor, Tensor]:
         return load(join(self.processed_dir, self.processed_filenames[i]))
 
-    def process(self, i, path) -> None:
+    def process(self, i: int, path: str) -> None:
         makedirs(self.processed_dir, exist_ok=True)
         with File(path, "r") as file:
             c = tensor(file["/filt_8"][:])
@@ -109,7 +109,7 @@ def __init__(
         shuffling: bool = False,
         y_normalizer: float = None,
         subblock_shape: Union[int, tuple] = None,
-    ):
+    ) -> None:
         self.batch_size = batch_size
         self.num_workers = num_workers
         self.splitting_ratios = splitting_ratios
@@ -119,11 +119,11 @@ def __init__(
         self.subblock_shape = subblock_shape
         super().__init__()
 
-    def prepare_data(self):
+    def prepare_data(self) -> None:
         """Initialize dataset."""
         CnfCombustionDataset(self.data_path, self.y_normalizer, self.subblock_shape)
 
-    def setup(self, stage: Optional[str] = None):
+    def setup(self, stage: Optional[str] = None) -> None:
         """Preprocessing: splitting and shuffling."""
         dataset = CnfCombustionDataset(
             self.data_path, self.y_normalizer, self.subblock_shape
@@ -150,7 +150,7 @@ def setup(self, stage: Optional[str] = None):
                 f"Current length is : {length}."
             )
 
-    def train_dataloader(self):
+    def train_dataloader(self) -> DataLoader:
         return DataLoader(
             self.train_dataset,
             batch_size=self.batch_size,
@@ -158,12 +158,12 @@ def train_dataloader(self):
             num_workers=self.num_workers,
         )
 
-    def val_dataloader(self):
+    def val_dataloader(self) -> DataLoader:
         return DataLoader(
             self.val_dataset, batch_size=self.batch_size, num_workers=self.num_workers
         )
 
-    def test_dataloader(self):
+    def test_dataloader(self) -> DataLoader:
         return DataLoader(
             self.test_dataset, batch_size=self.batch_size, num_workers=self.num_workers
         )
diff --git a/reactive-flows/cnf-combustion/unets/env.yaml b/reactive-flows/cnf-combustion/unets/env.yaml
index 0186867e..8bce1bdc 100644
--- a/reactive-flows/cnf-combustion/unets/env.yaml
+++ b/reactive-flows/cnf-combustion/unets/env.yaml
@@ -1,7 +1,6 @@
 # This file defines the global environment dependencies for this model project.
 # It is read by the CI/CD workflows in charge of the tests (ai4sim-ci-cd.yaml)
 # and the build of the Docker image (images-ci-cd.yaml) to set the environment.
-python_version: 3.10
 ubuntu_base_image:
   name: nvidia/cuda
   tag: 12.4.1-cudnn-runtime-ubuntu22.04
\ No newline at end of file
diff --git a/reactive-flows/cnf-combustion/unets/models.py b/reactive-flows/cnf-combustion/unets/models.py
index 4e399f7a..13140786 100755
--- a/reactive-flows/cnf-combustion/unets/models.py
+++ b/reactive-flows/cnf-combustion/unets/models.py
@@ -11,7 +11,9 @@
 # limitations under the License.
 
 from lightning import LightningModule
-from torch import flatten
+from torch import Tensor, flatten
+from torch.nn import Module
+from torch.optim import Optimizer
 from torch_optimizer import AdamP
 from torchmetrics.functional import mean_squared_error, r2_score
 
@@ -21,10 +23,16 @@
 class CombustionModule(LightningModule):
     """LightningModule for combustion use-cases (R2-scored)."""
 
-    def forward(self, x):
+    def __init__(self) -> None:
+        super().__init__()
+        self.model: Module
+
+    def forward(self, x: Tensor) -> Tensor:
         return self.model(x)
 
-    def _common_step(self, batch, stage):
+    def _common_step(
+        self, batch: tuple[Tensor, Tensor], stage: str
+    ) -> tuple[Tensor, Tensor, Tensor]:
         """Compute the loss, additional metrics, and log them."""
         x, y = batch
         y_hat = self(x)
@@ -36,21 +44,28 @@ def _common_step(self, batch, stage):
 
         return y_hat, loss, r2
 
-    def training_step(self, batch, batch_idx):
+    def training_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
         _, loss, _ = self._common_step(batch, "train")
         return loss
 
-    def validation_step(self, batch, batch_idx):
+    def validation_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> None:
         self._common_step(batch, "val")
 
-    def test_step(self, batch, batch_idx):
+    def test_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> None:
         self._common_step(batch, "test")
 
 
 class LitUnet3D(CombustionModule):
     """Lit wrapper to compile a 3D U-net, generic volume shapes."""
 
-    def __init__(self, in_channels, out_channels, n_levels, n_features_root, lr):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        n_levels: int,
+        n_features_root: int,
+        lr: float,
+    ) -> None:
         super().__init__()
         self.save_hyperparameters()
 
@@ -62,5 +77,5 @@ def __init__(self, in_channels, out_channels, n_levels, n_features_root, lr):
             n_features_root=n_features_root,
         )
 
-    def configure_optimizers(self):
+    def configure_optimizers(self) -> Optimizer:
         return AdamP(self.parameters(), lr=self.lr)
diff --git a/reactive-flows/cnf-combustion/unets/mypy.ini b/reactive-flows/cnf-combustion/unets/mypy.ini
new file mode 100644
index 00000000..7a8a90ae
--- /dev/null
+++ b/reactive-flows/cnf-combustion/unets/mypy.ini
@@ -0,0 +1,8 @@
+[mypy]
+
+mypy_path = reactive-flows/cnf-combustion/unets
+
+# Override mypy default value `ignore_missing_imports = false` to `true` for modules with no typing
+# Has the effect of silencing `import-untyped` error
+[mypy-h5py]
+ignore_missing_imports = true
diff --git a/reactive-flows/cnf-combustion/unets/noxfile.py b/reactive-flows/cnf-combustion/unets/noxfile.py
index 2585a153..517a6f3c 100644
--- a/reactive-flows/cnf-combustion/unets/noxfile.py
+++ b/reactive-flows/cnf-combustion/unets/noxfile.py
@@ -20,6 +20,8 @@
 import os
 import sys
 
+import nox
+
 # Insert the tools/nox folder to the python path to fetch the nox_ref_file.py content
 current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 common_dir = os.path.dirname(os.path.dirname(os.path.dirname(current_dir)))
@@ -30,3 +32,4 @@
 from nox_ref_file import *
 
 # Insert below the model project specific targets
+nox.needs_version = "==2026.2.9"
diff --git a/reactive-flows/cnf-combustion/unets/pyproject.toml b/reactive-flows/cnf-combustion/unets/pyproject.toml
new file mode 100644
index 00000000..cd27e068
--- /dev/null
+++ b/reactive-flows/cnf-combustion/unets/pyproject.toml
@@ -0,0 +1,54 @@
+[project]
+name = "cnf-combustion-unets"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    "h5py==3.12.1",
+    "jsonargparse[signatures]==4.35.0",
+    "lightning==2.4.0",
+    "omegaconf==2.3.0",
+    "tensorboard==2.18.0",
+    "torch==2.8.0+cu128",
+    "torch-optimizer==0.3.0"
+]
+
+# Nox will find here the python version that will be used
+classifiers = [
+  "Programming Language :: Python :: 3.10",
+]
+
+[dependency-groups]
+tests = [
+    "pytest-cov==7.0.0"
+]
+coverage = [
+    "coverage==7.13.4"
+]
+black = [
+    "black==26.1.0"
+]
+isort = [
+    "isort==7.0.0"
+]
+mypy = [
+    "mypy==1.19.1",
+    "types-PyYAML==6.0.12"
+]
+lint = [
+    "flake8==7.3.0",
+    "flake8-docstrings==1.7.0",
+    "flake8-use-fstring==1.4",
+    "flake8-variables-names==0.0.6",
+    "pep8-naming==0.15.1"
+]
+data = []
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch" },
+]
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl"
+explicit = true
diff --git a/reactive-flows/cnf-combustion/unets/requirements.txt b/reactive-flows/cnf-combustion/unets/requirements.txt
deleted file mode 100644
index 6bf58cf3..00000000
--- a/reactive-flows/cnf-combustion/unets/requirements.txt
+++ /dev/null
@@ -1,87 +0,0 @@
-absl-py==2.1.0
-aiohappyeyeballs==2.4.4
-aiohttp==3.11.10
-aiosignal==1.3.2
-antlr4-python3-runtime==4.9.3
-argcomplete==3.5.2
-async-timeout==5.0.1
-attrs==24.3.0
-cachetools==5.5.0
-certifi==2024.12.14
-charset-normalizer==3.4.0
-colorlog==6.9.0
-distlib==0.3.9
-docstring_parser==0.16
-filelock==3.16.1
-fire==0.7.0
-frozenlist==1.5.0
-fsspec==2024.10.0
-future==1.0.0
-google-auth==2.37.0
-google-auth-oauthlib==1.2.1
-grpcio==1.68.1
-h5py==3.12.1
-idna==3.10
-importlib_metadata==8.5.0
-importlib_resources==6.4.5
-Jinja2==3.1.5
-jsonargparse==4.35.0
-kaleido==0.2.1
-lightning==2.4.0
-lightning-utilities==0.11.9
-Markdown==3.7
-MarkupSafe==3.0.2
-mpmath==1.3.0
-multidict==6.1.0
-networkx==3.4.2
-nox==2024.10.9
-numpy==2.2.0
-nvidia-cublas-cu12==12.4.5.8
-nvidia-cuda-cupti-cu12==12.4.127
-nvidia-cuda-nvrtc-cu12==12.4.127
-nvidia-cuda-runtime-cu12==12.4.127
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.2.1.3
-nvidia-curand-cu12==10.3.5.147
-nvidia-cusolver-cu12==11.6.1.9
-nvidia-cusparse-cu12==12.3.1.170
-nvidia-nccl-cu12==2.21.5
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.4.127
-oauthlib==3.2.2
-omegaconf==2.3.0
-packaging==24.2
-pip==24.3.1
-platformdirs==4.3.6
-propcache==0.2.1
-protobuf==5.29.1
-pyasn1==0.6.1
-pyasn1_modules==0.4.1
-pyDeprecate==0.3.2
-pytorch-lightning==2.4.0
-pytorch-ranger==0.1.1
-PyYAML==6.0.2
-randomname==0.2.1
-requests==2.32.3
-requests-oauthlib==2.0.0
-rsa==4.9
-setuptools==75.1.0
-six==1.17.0
-sympy==1.13.1
-tensorboard==2.18.0
-tensorboard-data-server==0.7.2
-termcolor==2.5.0
-tomli==2.2.1
-torch==2.6.0+cu124
-torch-optimizer==0.3.0
-torchmetrics==1.6.0
-tqdm==4.67.1
-triton==3.2.0
-typeshed_client==2.7.0
-typing_extensions==4.12.2
-urllib3==2.2.3
-virtualenv==20.28.0
-Werkzeug==3.1.3
-wheel==0.44.0
-yarl==1.18.3
-zipp==3.21.0
diff --git a/reactive-flows/cnf-combustion/unets/tests/test_data.py b/reactive-flows/cnf-combustion/unets/tests/test_data.py
index 09fe3986..0a519308 100644
--- a/reactive-flows/cnf-combustion/unets/tests/test_data.py
+++ b/reactive-flows/cnf-combustion/unets/tests/test_data.py
@@ -14,6 +14,7 @@
 from os.path import exists, join
 from shutil import rmtree
 from tempfile import mkdtemp
+from typing import TypedDict
 from unittest import TestCase, main
 from warnings import catch_warnings, simplefilter
 
@@ -24,17 +25,29 @@
 
 from data import CnfCombustionDataModule, CnfCombustionDataset
 
+DataModuleParam = TypedDict(
+    "DataModuleParam",
+    {
+        "batch_size": int,
+        "num_workers": int,
+        "y_normalizer": float,
+        "data_path": str,
+        "splitting_ratios": tuple[float, float, float],
+        "subblock_shape": tuple[int, int, int],
+    },
+)
+
 
 class TestData(TestCase):
 
     def setUp(self) -> None:
         self.filenames = ["DNS1_00116000.h5", "DNS1_00117000.h5", "DNS1_00118000.h5"]
-        data_module_params = {
+        data_module_params: DataModuleParam = {
             "batch_size": 1,
             "num_workers": 0,
             "y_normalizer": 342.553,
             "data_path": "./data",
-            "splitting_ratios": [0.4, 0.4, 0.2],
+            "splitting_ratios": (0.4, 0.4, 0.2),
             "subblock_shape": (32, 16, 16),
         }
 
@@ -54,7 +67,7 @@ def setUp(self) -> None:
     def tearDown(self) -> None:
         rmtree(self.dir)
 
-    def create_env(self, tempdir):
+    def create_env(self, tempdir: str) -> None:
         mkdir(join(tempdir, "data"))
         mkdir(join(tempdir, "data", "raw"))
 
@@ -68,33 +81,33 @@ def create_env(self, tempdir):
         with open(temp_file_path, "w") as tmpfile:
             dump(self.filenames, tmpfile)
 
-    def test_process(self):
+    def test_process(self) -> None:
         self.dataset.process(0, join(self.dir, "data", "raw", "DNS1_00116000.h5"))
         self.assertTrue(exists(join(self.dir, "data", "processed")))
         self.assertEqual(
             len(listdir(join(self.dir, "data", "processed"))), len(self.filenames)
         )
 
-    def test_len(self):
+    def test_len(self) -> None:
         self.assertEqual(len(self.dataset), 3)
 
-    def test_setup(self):
+    def test_setup(self) -> None:
         self.data_module.setup()
         self.assertEqual(len(self.data_module.train_dataset), 1)
         self.assertEqual(len(self.data_module.val_dataset), 1)
         self.assertEqual(len(self.data_module.test_dataset), 1)
 
-    def test_train_dataloader(self):
+    def test_train_dataloader(self) -> None:
         self.data_module.setup()
         test_train_dl = self.data_module.train_dataloader()
         self.assertTrue(isinstance(test_train_dl, DataLoader))
 
-    def test_val_dataloader(self):
+    def test_val_dataloader(self) -> None:
         self.data_module.setup()
         test_val_dl = self.data_module.train_dataloader()
         self.assertTrue(isinstance(test_val_dl, DataLoader))
 
-    def test_test_dataloader(self):
+    def test_test_dataloader(self) -> None:
         self.data_module.setup()
         test_test_dl = self.data_module.train_dataloader()
         self.assertTrue(isinstance(test_test_dl, DataLoader))
diff --git a/reactive-flows/cnf-combustion/unets/tests/test_models.py b/reactive-flows/cnf-combustion/unets/tests/test_models.py
index 8ec6cc90..cd0c1004 100644
--- a/reactive-flows/cnf-combustion/unets/tests/test_models.py
+++ b/reactive-flows/cnf-combustion/unets/tests/test_models.py
@@ -10,6 +10,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import TypedDict
 from unittest import TestCase, main
 
 from numpy import zeros
@@ -18,11 +19,22 @@
 
 from models import LitUnet3D
 
+InitParam = TypedDict(
+    "InitParam",
+    {
+        "in_channels": int,
+        "out_channels": int,
+        "n_levels": int,
+        "n_features_root": int,
+        "lr": float,
+    },
+)
+
 
 class TestModels(TestCase):
 
     def setUp(self) -> None:
-        self.initParam = {
+        self.initParam: InitParam = {
             "in_channels": 1,
             "out_channels": 2,
             "n_levels": 2,
@@ -30,7 +42,7 @@ def setUp(self) -> None:
             "lr": 0.0001,
         }
 
-    def test_forward_common_step(self):
+    def test_forward_common_step(self) -> None:
         # Fake data, of dim (n_batchs, n_channels, x, y, z).
         x = from_numpy(zeros((1, self.initParam["in_channels"], 10, 10, 10)))
         y = from_numpy(zeros((1, self.initParam["in_channels"], 10, 10, 10)))
@@ -45,7 +57,7 @@ def test_forward_common_step(self):
         loss = test_unet._common_step(batch=(x, y), stage="train")
         self.assertEqual(len(loss), 3)
 
-    def test_configure_optimizers(self):
+    def test_configure_optimizers(self) -> None:
         test_unet = LitUnet3D(**self.initParam)
         op = test_unet.configure_optimizers()
         self.assertTrue(isinstance(op, Optimizer))
diff --git a/reactive-flows/cnf-combustion/unets/tests/test_unet.py b/reactive-flows/cnf-combustion/unets/tests/test_unet.py
index acaf39ec..ee6374c6 100644
--- a/reactive-flows/cnf-combustion/unets/tests/test_unet.py
+++ b/reactive-flows/cnf-combustion/unets/tests/test_unet.py
@@ -21,11 +21,11 @@
 class TestUnet3D(TestCase):
     """Testing 3D U-nets."""
 
-    def n_conv(self, n_levels: int, bilinear: bool = False):
+    def n_conv(self, n_levels: int, bilinear: bool = False) -> int:
         # 2 per DoubleConv + 1 per upsampler.
         return 4 * n_levels + (n_levels - 1 if bilinear else 0)
 
-    def test_3d(self):
+    def test_3d(self) -> None:
 
         n_levels = 1
         bilinear = True
@@ -49,20 +49,20 @@ def test_3d(self):
         self.assertEqual(summary.count("Upsampler"), n_levels - 1)
         self.assertEqual(summary.count("Conv3d"), self.n_conv(n_levels, bilinear))
 
-    def test_inference_3d(self):
+    def test_inference_3d(self) -> None:
         net = UNet3D(inp_ch=1, out_ch=1, n_levels=3, n_features_root=4)
         n = 32
         inp = from_numpy(rand(1, 1, n, n, n))
         shp = tuple(net(inp).shape)
         self.assertEqual(shp, (1, 1, n, n, n))
 
-    def test_downsampler(self):
+    def test_downsampler(self) -> None:
         sampler = Downsampler(inp_ch=4, out_ch=8).double()
         inp = from_numpy(rand(1, 4, 16, 16, 16))
         shp = tuple(sampler(inp).shape)
         self.assertEqual(shp, (1, 8, 8, 8, 8))
 
-    def test_upsampler(self):
+    def test_upsampler(self) -> None:
         sampler = Upsampler(inp_ch=8, out_ch=4).double()
         inp = from_numpy(rand(1, 8, 16, 16, 16))
         res = from_numpy(rand(1, 4, 32, 32, 32))
diff --git a/reactive-flows/cnf-combustion/unets/tests/test_utils.py b/reactive-flows/cnf-combustion/unets/tests/test_utils.py
index 81d923f1..8c00553e 100644
--- a/reactive-flows/cnf-combustion/unets/tests/test_utils.py
+++ b/reactive-flows/cnf-combustion/unets/tests/test_utils.py
@@ -12,18 +12,17 @@
 
 from unittest import TestCase, main
 
-from numpy import copy
-from numpy.random import rand
+import torch
 
 from utils import RandomCropper3D
 
 
 class TestData(TestCase):
 
-    def test_random_cropper(self):
+    def test_random_cropper(self) -> None:
         n, n_ = 64, 32
-        x = rand(n, n, n)
-        y = copy(x)
+        x = torch.rand(n, n, n)
+        y = torch.clone(x)
         random_cropper = RandomCropper3D(n_)
         x_, y_ = random_cropper(x, y)
         self.assertEqual(x_.shape, (n_, n_, n_))
diff --git a/reactive-flows/cnf-combustion/unets/trainer.py b/reactive-flows/cnf-combustion/unets/trainer.py
index f99515ad..baad4131 100755
--- a/reactive-flows/cnf-combustion/unets/trainer.py
+++ b/reactive-flows/cnf-combustion/unets/trainer.py
@@ -13,7 +13,7 @@
 from lightning.pytorch.cli import LightningCLI
 
 
-def main():
+def main() -> None:
     cli = LightningCLI(run=False, parser_kwargs={"parser_mode": "omegaconf"})
     cli.trainer.fit(model=cli.model, datamodule=cli.datamodule)
     cli.trainer.test(model=cli.model, datamodule=cli.datamodule)
diff --git a/reactive-flows/cnf-combustion/unets/unet.py b/reactive-flows/cnf-combustion/unets/unet.py
index dfdee6cc..66e74c12 100644
--- a/reactive-flows/cnf-combustion/unets/unet.py
+++ b/reactive-flows/cnf-combustion/unets/unet.py
@@ -10,6 +10,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import List
+
 import torch.nn as nn
 from torch import Tensor, cat
 
@@ -38,7 +40,7 @@ def __init__(
         self.n_levels = n_levels
 
         # First level hardcoded.
-        layers = [DoubleConv(inp_ch, n_features_root)]
+        layers: List[nn.Module] = [DoubleConv(inp_ch, n_features_root)]
 
         # Downward path.
         f = n_features_root
@@ -120,7 +122,7 @@ class Upsampler(nn.Module):
 
     def __init__(self, inp_ch: int, out_ch: int, bilinear: bool = False):
         super().__init__()
-        self.upsample = None
+        self.upsample: nn.Module
         if inp_ch < 2:
             raise ValueError(f"Input channel ({inp_ch}) too low.")
         if bilinear:
diff --git a/reactive-flows/cnf-combustion/unets/utils.py b/reactive-flows/cnf-combustion/unets/utils.py
index 3c5f4337..70d98dea 100644
--- a/reactive-flows/cnf-combustion/unets/utils.py
+++ b/reactive-flows/cnf-combustion/unets/utils.py
@@ -13,6 +13,7 @@
 from typing import Union
 
 from numpy.random import randint
+from torch import Tensor
 
 
 class RandomCropper3D:
@@ -22,7 +23,7 @@ class RandomCropper3D:
         out_shape (tuple or int): desired output shape.
     """
 
-    def __init__(self, out_shape: Union[int, tuple]):
+    def __init__(self, out_shape: Union[int, tuple]) -> None:
         """
         Args:
             out_shape (int | tuple): desired shape (after cropping), expanded to 3D if int.
@@ -34,7 +35,7 @@ def __init__(self, out_shape: Union[int, tuple]):
             assert len(out_shape) == 3
             self.out_shape = out_shape
 
-    def __call__(self, x, y):
+    def __call__(self, x: Tensor, y: Tensor) -> tuple[Tensor, Tensor]:
         """Apply the random cropping to a (x,y) pair."""
         h, w, d = x.shape[0], x.shape[1], x.shape[2]
         bh, bw, bd = self.out_shape
diff --git a/reactive-flows/cnf-combustion/unets/uv.lock b/reactive-flows/cnf-combustion/unets/uv.lock
new file mode 100644
index 00000000..caf01bc5
--- /dev/null
+++ b/reactive-flows/cnf-combustion/unets/uv.lock
@@ -0,0 +1,2312 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/86/a0/c2ab48d74599c7c84cb104ebd799c5813de252bea0f360ffc29d270c2caa/yarl-1.22.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:e4b582bab49ac33c8deb97e058cd67c2c50dac0dd134874106d9c774fd272529", size = 352400, upload-time = "2025-10-06T14:12:06.624Z" },
+    { url = "https://files.pythonhosted.org/packages/32/75/f8919b2eafc929567d3d8411f72bdb1a2109c01caaab4ebfa5f8ffadc15b/yarl-1.22.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0b5bcc1a9c4839e7e30b7b30dd47fe5e7e44fb7054ec29b5bb8d526aa1041093", size = 357140, upload-time = "2025-10-06T14:12:08.362Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/72/6a85bba382f22cf78add705d8c3731748397d986e197e53ecc7835e76de7/yarl-1.22.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c0232bce2170103ec23c454e54a57008a9a72b5d1c3105dc2496750da8cfa47c", size = 341473, upload-time = "2025-10-06T14:12:10.994Z" },
+    { url = "https://files.pythonhosted.org/packages/35/18/55e6011f7c044dc80b98893060773cefcfdbf60dfefb8cb2f58b9bacbd83/yarl-1.22.0-cp314-cp314t-win32.whl", hash = "sha256:8009b3173bcd637be650922ac455946197d858b3630b6d8787aa9e5c4564533e", size = 89056, upload-time = "2025-10-06T14:12:13.317Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/86/0f0dccb6e59a9e7f122c5afd43568b1d31b8ab7dda5f1b01fb5c7025c9a9/yarl-1.22.0-cp314-cp314t-win_amd64.whl", hash = "sha256:9fb17ea16e972c63d25d4a97f016d235c78dd2344820eb35bc034bc32012ee27", size = 96292, upload-time = "2025-10-06T14:12:15.398Z" },
+    { url = "https://files.pythonhosted.org/packages/48/b7/503c98092fb3b344a179579f55814b613c1fbb1c23b3ec14a7b008a66a6e/yarl-1.22.0-cp314-cp314t-win_arm64.whl", hash = "sha256:9f6d73c1436b934e3f01df1e1b21ff765cd1d28c77dfb9ace207f746d4610ee1", size = 85171, upload-time = "2025-10-06T14:12:16.935Z" },
+    { url = "https://files.pythonhosted.org/packages/73/ae/b48f95715333080afb75a4504487cbe142cae1268afc482d06692d605ae6/yarl-1.22.0-py3-none-any.whl", hash = "sha256:1380560bdba02b6b6c90de54133c81c9f2a453dee9912fe58c1dcced1edb7cff", size = 46814, upload-time = "2025-10-06T14:12:53.872Z" },
+]
diff --git a/tools/mypy/mypy.ini b/tools/mypy/mypy.ini
new file mode 100644
index 00000000..9ceabd03
--- /dev/null
+++ b/tools/mypy/mypy.ini
@@ -0,0 +1,25 @@
+[mypy]
+
+# [mypy configuration file doc](https://mypy.readthedocs.io/en/stable/config_file.html#confval-exclude)
+# exclude = (?x)(/nox.*)
+exclude = (?x)(noxfile.py)
+
+# Disallows defining functions without type annotations or with incomplete type annotations.
+# Use Any for *args and **kwargs
+disallow_untyped_defs = true
+
+# Causes mypy to treat parameters with a None default value as having an implicit optional type (T | None)
+# Allows `def foo(a: str = None)` instead of `def foo(a: str | None = None)` or `def foo(a: Union[str, None] = None)`
+implicit_optional = true
+
+# Warn when a `#type: ignore` silences a line that is not actually generating an error
+warn_unused_ignores = true
+
+# Disable the check for dynamically defined attributes, for more details see link below
+# [attr-defined](https://mypy.readthedocs.io/en/stable/error_code_list.html#check-that-attribute-exists-attr-defined)
+# disable_error_code = "attr-defined"
+
+# Configure mypy output styling
+show_error_code_links = true
+show_error_codes = true
+pretty = true
diff --git a/tools/nox/nox_ref_file.py b/tools/nox/nox_ref_file.py
index ad685b53..c90abd3b 100644
--- a/tools/nox/nox_ref_file.py
+++ b/tools/nox/nox_ref_file.py
@@ -18,10 +18,14 @@
 # limitations under the License.
 
 import os
-import sys
-import re
 import glob
 import nox
+from configparser import ConfigParser
+
+try:
+    import uv
+except ImportError:
+    raise ImportError("uv not found. Please install it in your environment, with 'pip install uv'")
 
 REPORTS_DIR = ".ci-reports/"
 COV_FT_FILE = ".coverage-ft"
@@ -31,21 +35,9 @@
 
 # The list of the default targets executed with the simple command "nox".
 nox.options.sessions = ["lint", "tests"]
-
-# Check the required python version is available
-current_python = f'{sys.version_info.major}.{sys.version_info.minor}'
-with open("env.yaml", 'r') as env_file:
-    python_req = None
-    for line in env_file.readlines():
-        if 'python_version:' in line:
-            match = re.search(r'python_version: ([0-9\.]*)', line)
-            python_req = match.group(1)
-    if not python_req:
-        raise RuntimeError("Required python version not found in the env.yaml file.")
-    elif current_python != python_req:
-        raise RuntimeError("Current python version does not match the required version: "
-                           f"{current_python} != {python_req}.")
-
+nox.options.default_venv_backend = "uv"
+PYPROJECT = nox.project.load_toml("pyproject.toml")
+PYTHON_VERSIONS = nox.project.python_versions(PYPROJECT)
 
 def _wheel_version(wheel: str, req_file: str = 'requirements.txt') -> str:
     """Extract the version of a wheel from a requirement.txt, if it is present.
@@ -58,11 +50,10 @@ def _wheel_version(wheel: str, req_file: str = 'requirements.txt') -> str:
             an empty string otherwise.
     """
     version = wheel
-    with open(req_file, encoding='utf-8') as file:
-        for line in file.readlines():
-            if f"{wheel}==" in line:
-                version = line.rstrip()
-                break
+    for lib in PYPROJECT["project"]["dependencies"]:
+        if f"{wheel}==" in lib:
+            version = lib.rstrip()
+            break
     return version
 
 
@@ -77,25 +68,26 @@ def _torch_version(req_file: str = 'requirements.txt') -> str:
             an empty string otherwise.
     """
     cuda = 'cpu'
-    version = _wheel_version("torch", req_file).split('==')[1]
+    version = _wheel_version("torch").split('==')[1]
     if "+" in version:
         version, cuda = version.split('+')
     return version, cuda
 
-@nox.session
-def base_dependencies(session):
-    """Target to install the basics python requirements."""
-    req_file = "requirements.txt"
-    session.run("python3", "-m", "pip", "install",
-                _wheel_version("pip", req_file),
-                _wheel_version("wheel", req_file),
-                _wheel_version("setuptools", req_file))
 
-@nox.session
+def _build_mypy_config():
+    """Build the mypy configuration file by combining common and model specific configs."""
+    mypy_common_cfg = os.path.join(ROOT_PATH, 'tools', 'mypy', 'mypy.ini')
+    mypy_config = ConfigParser()
+    mypy_config.read([mypy_common_cfg, "mypy.ini"])
+    with open("mypy_full.ini", "w") as mypy_file:
+        # Write the full mypy configuration
+        mypy_config.write(mypy_file)
+
+
+@nox.session(python=PYTHON_VERSIONS)
 def dev_dependencies(session):
     """Target to install all requirements of the use-case code."""
     torch_vers, cuda_vers = _torch_version()
-    req_file = "requirements.txt"
 
     additional_url = ''
     extra_url = ''
@@ -107,126 +99,106 @@ def dev_dependencies(session):
         # Set the url of precompiled torch-geometric dependencies depending on torch version
         additional_url = f"https://data.pyg.org/whl/torch-{torch_vers}+{cuda_vers}.html"
 
-    # Install base python dependencies
-    base_dependencies(session)
-
     # Install use-case python dependencies
-    session.run("python3", "-m", "pip", "install",
-                "-r", req_file,
+    session.run("uv", "sync", "--active",
                 "-f", additional_url,
                 "--extra-index-url", extra_url)
     if "purge" in session.posargs:
-        session.run("python3", "-m", "pip", "cache", "purge")
+        session.run("uv", "cache", "clean")
 
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def tests(session):
     """Target to run unit tests on the code with pytest, and generate coverage report."""
-    # Install use-case python dependencies
-    dev_dependencies(session)
-    session.run("python3", "-m", "pip", "install", "pytest-cov")
     session.run('rm','-rf', COV_UT_FILE, external=True)
-    session.run("python3", "-m", "pytest", "--cache-clear", "--cov=./", "-v")
+    session.run("uv", "run", "--active", "--group", "tests", "-m", "pytest", "--cache-clear", "--cov=./", "-v")
     session.run('mv','.coverage', COV_UT_FILE, external=True)
     session.notify("coverage_report", ['data-file', f'{COV_UT_FILE}'])
 
 
-def coverage_install(session):
-    session.run("python3", "-m", "pip", "install", "coverage")
-
-
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def coverage_report(session):
     """Target to generate coverage report from test results."""
-    coverage_install(session)
-
+    uv_run = ["uv", "run", "--active", "--only-group", "coverage"]
     cov_file=".coverage"
     if "combine" in session.posargs:
-        session.run("coverage", "combine", "--keep", "--append", *glob.glob(".coverage-*"))
+        session.run(*uv_run, "coverage", "combine", "--keep", "--append", *glob.glob(".coverage-*"))
     elif "data-file" in session.posargs:
         # set the coverage input coverage datafile name, if provided in the command line with the "data-file" key word
         # ex: nox -s coverage_report -- data-file .coverage-out
         cov_file = session.posargs[session.posargs.index("data-file") + 1]
 
-    session.run("coverage", "xml", f"--data-file={cov_file}", "-o", f"{REPORTS_DIR}/py{cov_file.lstrip('.')}.xml")
-    session.run("coverage", "report", "-m", f"--data-file={cov_file}")
+    session.run(*uv_run, "coverage", "xml", f"--data-file={cov_file}", "-o", f"{REPORTS_DIR}/py{cov_file.lstrip('.')}.xml")
+    session.run(*uv_run, "coverage", "report", "-m", f"--data-file={cov_file}")
 
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def lint(session):
     """Target to lint the code with flake8."""
-    # Install base python dependencies
-    base_dependencies(session)
-    session.run("python3", "-m", "pip", "install",
-                "flake8",
-                "flake8-docstrings",
-                "flake8-use-fstring",
-                "flake8-variables-names",
-                "pep8-naming")
-    session.run("flake8", "--config", FLAKE8_CFG)
+    uv_run = ["uv", "run", "--active", "--only-group", "lint"]
+    session.run(*uv_run, "flake8", "--config", FLAKE8_CFG)
 
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def import_sort(session):
     """Target to sort automatically the import in the code with isort."""
-    # Install base python dependencies
-    base_dependencies(session)
-    session.run("python3", "-m", "pip", "install", "isort")
+    uv_run = ["uv", "run", "--active", "--only-group", "isort"]
     if "check-only" in session.posargs:
-        session.run("isort", "--profile", "black", "--check-only", ".")
+        session.run(*uv_run, "isort", "--profile", "black", "--check-only", ".")
     else:
-        session.run("isort", "--profile", "black", ".")
+        session.run(*uv_run, "isort", "--profile", "black", ".")
 
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def black(session):
     """Target to reformat automatically the code with black."""
-    # Install base python dependencies
-    base_dependencies(session)
-    session.run("python3", "-m", "pip", "install", "black")
+    uv_run = ["uv", "run", "--active", "--only-group", "black"]
     if "check-only" in session.posargs:
-        session.run("black", "--check", ".")
+        session.run(*uv_run, "black", "--check", ".")
     else:
-        session.run("black", ".")
+        session.run(*uv_run, "black", ".")
 
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
+def mypy(session):
+    """Target to check type hint with mypy."""
+    # Install base python dependencies
+    _build_mypy_config()
+    uv_run = ["uv", "run", "--active", "--group", "mypy"]
+    try:
+        session.run(*uv_run, "mypy", "--config-file", "mypy_full.ini", "--explicit-package-bases", ".")
+    finally:
+        session.run("rm", "mypy_full.ini", external=True)
+
+
+@nox.session(python=PYTHON_VERSIONS)
 def docs(session):
     """Target to build the documentation (not yet implemented)."""
     raise NotImplementedError("This target is not yet implemented.")
 
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def generate_synthetic_data(session):
     """Target to generate synthetic data related to the use case."""
-    # Install python dependencies
-    dev_dependencies(session)
-
-    # Install requirement related to synthetic data generation
-    req_file = "ci/requirements_data.txt"
-    if os.path.isfile(req_file):
-        session.run("python3", "-m", "pip", "install","-r", req_file)
-
     # Generate the data
-    session.run("python3", "ci/generate_synthetic_data.py")
+    session.run("uv", "run", "--active", "--group", "data", "ci/generate_synthetic_data.py")
     
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def download_data(session):
     """Target to download the data required to train the use-case models (not yet implemented).""" 
     raise NotImplementedError("This target is not yet implemented.")
 
 
-@nox.session
+@nox.session(python=PYTHON_VERSIONS)
 def train_test(session):
     """Target to launch a basic training of the use-case (not yet implemented)."""
     # Generate the synthetic dataset required for the functional tests
     generate_synthetic_data(session)
 
-    # Run te functional tests with coverage
-    coverage_install(session)
+    # Run the functional tests with coverage
     session.run('rm','-rf', COV_FT_FILE, external=True)
-    session.run('bash', './ci/run.sh', '--runner', f'coverage run --append --data-file={COV_FT_FILE}', external=True)
+    session.run('bash', './ci/run.sh', '--runner', f'uv run --group coverage --active coverage run --append --data-file={COV_FT_FILE}', external=True)
     if "clean_data" in session.posargs:
         session.run('rm','-rf', './data')
     session.notify("coverage_report", ['data-file', f'{COV_FT_FILE}'])
diff --git a/weather-forecast/ecrad-3d-correction/unets/ci/generate_synthetic_data.py b/weather-forecast/ecrad-3d-correction/unets/ci/generate_synthetic_data.py
index dce75822..74176a41 100644
--- a/weather-forecast/ecrad-3d-correction/unets/ci/generate_synthetic_data.py
+++ b/weather-forecast/ecrad-3d-correction/unets/ci/generate_synthetic_data.py
@@ -28,7 +28,7 @@ class ThreeDCorrectionDataprocSyntheticData(ThreeDCorrectionDataproc):
     To be called once before experiments to build the dataset on the filesystem.
     """
 
-    def download(self) -> xr.DataArray:
+    def download(self) -> xr.Dataset:
         """Create data folder with fake raw data"""
         mu = 0.0
         sigma = 1.0
diff --git a/weather-forecast/ecrad-3d-correction/unets/data.py b/weather-forecast/ecrad-3d-correction/unets/data.py
index 0f3cf21a..6e0ed622 100644
--- a/weather-forecast/ecrad-3d-correction/unets/data.py
+++ b/weather-forecast/ecrad-3d-correction/unets/data.py
@@ -15,7 +15,6 @@
 
 import dask.array as da
 import lightning as pl
-import numpy as np
 import torch
 from torch.utils.data import DataLoader, Dataset, SubsetRandomSampler
 
@@ -44,7 +43,7 @@ def __init__(self, data_path: str) -> None:
         stats = torch.load(osp.join(data_path, "stats.pt"))
         self.n_data = stats["x_nb"].item()
 
-    def __getitem__(self, i: int) -> Tuple[np.ndarray]:
+    def __getitem__(self, i: int) -> Tuple[torch.Tensor, torch.Tensor]:
         """
         Load data from chunks on disk.
         Return (x, y) as tensors.
@@ -54,9 +53,9 @@ def __getitem__(self, i: int) -> Tuple[np.ndarray]:
         """
         x = self.x[i].compute()
         y = self.y[i].compute()
-        x = torch.from_numpy(x)
-        y = torch.from_numpy(y)
-        return x, y
+        x_tensor = torch.from_numpy(x)
+        y_tensor = torch.from_numpy(y)
+        return x_tensor, y_tensor
 
     def __len__(self) -> int:
         return self.n_data
@@ -84,12 +83,12 @@ def __init__(
         self.batch_size = batch_size
         self.num_workers = num_workers
         self.splitting_ratios = splitting_ratios
-        self.dataset = None
+        self.dataset: ThreeDCorrectionDataset
 
-    def prepare_data(self):
+    def prepare_data(self) -> None:
         ThreeDCorrectionDataset(self.data_path)
 
-    def setup(self, stage: Optional[str] = None):
+    def setup(self, stage: Optional[str] = None) -> None:
 
         # Define subsets.
         tr, va, te = self.splitting_ratios
@@ -109,7 +108,7 @@ def setup(self, stage: Optional[str] = None):
         self.val_sampler = SubsetRandomSampler(val_idx)
         self.test_sampler = SubsetRandomSampler(test_idx)
 
-    def train_dataloader(self):
+    def train_dataloader(self) -> DataLoader:
         return DataLoader(
             self.dataset,
             batch_size=self.batch_size,
@@ -117,7 +116,7 @@ def train_dataloader(self):
             num_workers=self.num_workers,
         )
 
-    def val_dataloader(self):
+    def val_dataloader(self) -> DataLoader:
         return DataLoader(
             self.dataset,
             batch_size=self.batch_size,
@@ -125,7 +124,7 @@ def val_dataloader(self):
             num_workers=self.num_workers,
         )
 
-    def test_dataloader(self):
+    def test_dataloader(self) -> DataLoader:
         return DataLoader(
             self.dataset,
             batch_size=self.batch_size,
diff --git a/weather-forecast/ecrad-3d-correction/unets/dataproc.py b/weather-forecast/ecrad-3d-correction/unets/dataproc.py
index f9ffed55..e5057310 100644
--- a/weather-forecast/ecrad-3d-correction/unets/dataproc.py
+++ b/weather-forecast/ecrad-3d-correction/unets/dataproc.py
@@ -12,7 +12,7 @@
 
 import os
 import os.path as osp
-from typing import Dict, Tuple, Union
+from typing import List, Tuple, Union
 
 import climetlab as cml
 import dask.array as da
@@ -78,7 +78,7 @@ def process(self) -> None:
         x_path, y_path = self.reshard(x, y)
         self.compute_stats(x_path, y_path)
 
-    def download(self) -> xr.DataArray:
+    def download(self) -> xr.Dataset:
         """Download the data for 3D Correction UC and return an xr.Array."""
         cml.settings.set("cache-directory", self.cached_data_path)
         cml_ds = cml.load_dataset(
@@ -94,7 +94,7 @@ def download(self) -> xr.DataArray:
 
         return cml_ds.to_xarray()
 
-    def build_features(self, xr_array) -> Tuple[da.Array]:
+    def build_features(self, xr_array: xr.Dataset) -> Tuple[da.Array, da.Array]:
         """
         Build feature vectors (of spatial dimension 138)
             * Select features from the array provided in input;
@@ -134,7 +134,7 @@ def pad_tensor(arr: da.Array, pads: Tuple) -> da.Array:
             arr = da.rechunk(arr, chunks=(self.shard_size, *arr.shape[1:]))
             data.update({f: arr})
 
-        x = da.concatenate(
+        x: da.Array = da.concatenate(
             [
                 data["hl_inputs"],
                 pad_tensor(data["inter_inputs"], (1, 1)),
@@ -144,7 +144,7 @@ def pad_tensor(arr: da.Array, pads: Tuple) -> da.Array:
             axis=-1,
         )
 
-        y = da.concatenate(
+        y: da.Array = da.concatenate(
             [
                 data["flux_dn_sw"][..., np.newaxis],
                 data["flux_up_sw"][..., np.newaxis],
@@ -158,7 +158,7 @@ def pad_tensor(arr: da.Array, pads: Tuple) -> da.Array:
 
         return x, y
 
-    def purgedirs(self, paths: Union[str, list]) -> Union[str, list]:
+    def purgedirs(self, paths: Union[str, list]) -> Union[str, List[str]]:
         """Remove all content of directories in paths."""
         if isinstance(paths, str):
             paths = [paths]
@@ -172,7 +172,7 @@ def purgedirs(self, paths: Union[str, list]) -> Union[str, list]:
 
         return paths[0] if len(paths) == 1 else paths
 
-    def reshard(self, x: da.Array, y: da.Array) -> Tuple[str]:
+    def reshard(self, x: da.Array, y: da.Array) -> Tuple[str, str]:
         """
         Reshard the arrays by rechunking on memory,
         store the chunks on disk in Numpy file format (.npy)
@@ -181,18 +181,22 @@ def reshard(self, x: da.Array, y: da.Array) -> Tuple[str]:
         x_chunked = da.rechunk(x, chunks=(self.shard_size, *x.shape[1:]))
         y_chunked = da.rechunk(y, chunks=(self.shard_size, *y.shape[1:]))
 
-        x_path, y_path = self.purgedirs(
+        purged_paths = self.purgedirs(
             [
                 osp.join(self.processed_data_path, "x"),
                 osp.join(self.processed_data_path, "y"),
             ]
         )
+        assert (
+            isinstance(purged_paths, list) and len(purged_paths) == 2
+        ), "Two directories should be purged."
+        x_path, y_path = purged_paths
         da.to_npy_stack(x_path, x_chunked, axis=0)
         da.to_npy_stack(y_path, y_chunked, axis=0)
 
         return x_path, y_path
 
-    def compute_stats(self, x_path: str, y_path: str) -> Dict[str, np.array]:
+    def compute_stats(self, x_path: str, y_path: str) -> None:
         """Compute stats: mean and standard deviation for features of x and y."""
         stats = {}
         for a in [da.from_npy_stack(x_path), da.from_npy_stack(y_path)]:
diff --git a/weather-forecast/ecrad-3d-correction/unets/env.yaml b/weather-forecast/ecrad-3d-correction/unets/env.yaml
index 0186867e..8bce1bdc 100644
--- a/weather-forecast/ecrad-3d-correction/unets/env.yaml
+++ b/weather-forecast/ecrad-3d-correction/unets/env.yaml
@@ -1,7 +1,6 @@
 # This file defines the global environment dependencies for this model project.
 # It is read by the CI/CD workflows in charge of the tests (ai4sim-ci-cd.yaml)
 # and the build of the Docker image (images-ci-cd.yaml) to set the environment.
-python_version: 3.10
 ubuntu_base_image:
   name: nvidia/cuda
   tag: 12.4.1-cudnn-runtime-ubuntu22.04
\ No newline at end of file
diff --git a/weather-forecast/ecrad-3d-correction/unets/models.py b/weather-forecast/ecrad-3d-correction/unets/models.py
index 7b48d62e..48ad702c 100644
--- a/weather-forecast/ecrad-3d-correction/unets/models.py
+++ b/weather-forecast/ecrad-3d-correction/unets/models.py
@@ -16,6 +16,8 @@
 import lightning as pl
 import numpy as np
 import torch
+from lightning.pytorch.loggers import TensorBoardLogger
+from torch.optim.optimizer import Optimizer
 from torch_optimizer import AdamP
 from torchmetrics.functional import mean_squared_error
 
@@ -38,40 +40,48 @@ def __init__(self, data_path: str):
             normalize (bool): Whether or not to normalize during training.
         """
         super().__init__()
-
+        self.model: torch.nn.Module
         stats = torch.load(osp.join(data_path, "stats.pt"))
+        self.x_mean: torch.Tensor
         self.register_buffer("x_mean", stats["x_mean"], persistent=True)
+        self.x_std: torch.Tensor
         self.register_buffer("x_std", stats["x_std"], persistent=True)
 
-    def forward(self, x: Union[torch.Tensor, np.ndarray]):
+    def forward(self, x: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
         """
         Args:
             x (ndarray | tensor): Input tensor (numpy or tensor).
         """
         if isinstance(x, np.ndarray):
             x = torch.from_numpy(x)
-        x = self.normalize(x)
+        x_norm = self.normalize(x)
 
-        x = torch.moveaxis(x, 1, -1)
-        y_hat = self.model(x)
+        x_norm = torch.moveaxis(x_norm, 1, -1)
+        y_hat = self.model(x_norm)
         y_hat = torch.moveaxis(y_hat, 1, -1)
 
         return y_hat
 
-    def weight_histograms_adder(self):
+    def weight_histograms_adder(self) -> None:
         for name, params in self.named_parameters():
+            assert isinstance(
+                self.logger, TensorBoardLogger
+            ), "Logger must be a TensorBoardLogger to add histograms."
             self.logger.experiment.add_histogram(name, params, self.current_epoch)
 
-    def gradient_histograms_adder(self):
+    def gradient_histograms_adder(self) -> None:
         global_step = self.global_step
         if global_step % 50 == 0:
             for name, param in self.named_parameters():
                 if param.requires_grad:
+                    assert isinstance(
+                        self.logger, TensorBoardLogger
+                    ), "Logger must be a TensorBoardLogger to add histograms."
                     self.logger.experiment.add_histogram(
                         f"{name}_grad", param.grad, global_step
                     )
 
-    def normalize(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
+    def normalize(self, x: torch.Tensor) -> torch.Tensor:
         """
         Normalize inside the network.
         If y is None, then only process x (e.g. forward mode).
@@ -81,7 +91,9 @@ def normalize(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
 
         return x
 
-    def _common_step(self, batch, stage):
+    def _common_step(
+        self, batch: Tuple[torch.Tensor, torch.Tensor], stage: str
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Compute the loss, additional metrics, and log them."""
         x, y = batch
 
@@ -91,20 +103,20 @@ def _common_step(self, batch, stage):
         self.log(f"{stage}_loss", loss, prog_bar=True, on_step=True, batch_size=len(x))
         return y_hat, loss
 
-    def training_step(self, batch):
+    def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
         _, loss = self._common_step(batch, "train")
         return loss
 
-    def on_after_backward(self):
+    def on_after_backward(self) -> None:
         self.gradient_histograms_adder()
 
-    def on_train_epoch_end(self):
+    def on_train_epoch_end(self) -> None:
         self.weight_histograms_adder()
 
-    def validation_step(self, batch):
+    def validation_step(self, batch: Tuple[torch.Tensor, torch.Tensor]) -> None:
         self._common_step(batch, "val")
 
-    def test_step(self, batch):
+    def test_step(self, batch: Tuple[torch.Tensor, torch.Tensor]) -> None:
         self._common_step(batch, "test")
 
 
@@ -131,5 +143,5 @@ def __init__(
             n_features_root=n_features_root,
         )
 
-    def configure_optimizers(self):
+    def configure_optimizers(self) -> Optimizer:
         return AdamP(self.parameters(), lr=self.lr)
diff --git a/weather-forecast/ecrad-3d-correction/unets/mypy.ini b/weather-forecast/ecrad-3d-correction/unets/mypy.ini
new file mode 100644
index 00000000..57f0c527
--- /dev/null
+++ b/weather-forecast/ecrad-3d-correction/unets/mypy.ini
@@ -0,0 +1,10 @@
+[mypy]
+
+mypy_path = weather-forecast/ecrad-3d-correction/unets
+
+# Override mypy default value `ignore_missing_imports = false` to `true` for modules with no typing
+# Has the effect of silencing `import-untyped` error
+[mypy-h5py]
+ignore_missing_imports = true
+[mypy-climetlab]
+ignore_missing_imports = true
\ No newline at end of file
diff --git a/weather-forecast/ecrad-3d-correction/unets/noxfile.py b/weather-forecast/ecrad-3d-correction/unets/noxfile.py
index 2585a153..517a6f3c 100644
--- a/weather-forecast/ecrad-3d-correction/unets/noxfile.py
+++ b/weather-forecast/ecrad-3d-correction/unets/noxfile.py
@@ -20,6 +20,8 @@
 import os
 import sys
 
+import nox
+
 # Insert the tools/nox folder to the python path to fetch the nox_ref_file.py content
 current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 common_dir = os.path.dirname(os.path.dirname(os.path.dirname(current_dir)))
@@ -30,3 +32,4 @@
 from nox_ref_file import *
 
 # Insert below the model project specific targets
+nox.needs_version = "==2026.2.9"
diff --git a/weather-forecast/ecrad-3d-correction/unets/pyproject.toml b/weather-forecast/ecrad-3d-correction/unets/pyproject.toml
new file mode 100644
index 00000000..6772e79e
--- /dev/null
+++ b/weather-forecast/ecrad-3d-correction/unets/pyproject.toml
@@ -0,0 +1,56 @@
+[project]
+name = "ecrad-3d-correction-unets"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    "climetlab==0.24.0",
+    "climetlab-maelstrom-radiation==0.7.5",
+    "dask==2024.12.0",
+    "jsonargparse[signatures]==4.35.0",
+    "lightning==2.4.0",
+    "omegaconf==2.3.0",
+    "tensorboard==2.18.0",
+    "torch==2.8.0+cu128",
+    "torch-optimizer==0.3.0",
+    "xarray==2024.11.0"
+]
+
+# Nox will find here the python version that will be used
+classifiers = [
+  "Programming Language :: Python :: 3.10",
+]
+
+[dependency-groups]
+tests = [
+    "pytest-cov==7.0.0"
+]
+coverage = [
+    "coverage==7.13.4"
+]
+black = [
+    "black==26.1.0"
+]
+isort = [
+    "isort==7.0.0"
+]
+mypy = [
+    "mypy==1.19.1"
+]
+lint = [
+    "flake8==7.3.0",
+    "flake8-docstrings==1.7.0",
+    "flake8-use-fstring==1.4",
+    "flake8-variables-names==0.0.6",
+    "pep8-naming==0.15.1"
+]
+data = []
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch" },
+]
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl"
+explicit = true
diff --git a/weather-forecast/ecrad-3d-correction/unets/requirements.txt b/weather-forecast/ecrad-3d-correction/unets/requirements.txt
deleted file mode 100644
index 35f89aac..00000000
--- a/weather-forecast/ecrad-3d-correction/unets/requirements.txt
+++ /dev/null
@@ -1,142 +0,0 @@
-absl-py==2.1.0
-aiohappyeyeballs==2.4.4
-aiohttp==3.11.10
-aiosignal==1.3.2
-antlr4-python3-runtime==4.9.3
-argcomplete==3.5.2
-astunparse==1.6.3
-async-timeout==5.0.1
-attrs==24.3.0
-branca==0.8.1
-cachetools==5.5.0
-cads-api-client==1.5.4
-cdsapi==0.7.5
-certifi==2024.12.14
-cffi==1.17.1
-cfgrib==0.9.14.1
-cftime==1.6.4.post1
-charset-normalizer==3.4.0
-click==8.1.7
-climetlab==0.24.0
-climetlab-maelstrom-radiation==0.7.5
-cloudpickle==3.1.0
-colorlog==6.9.0
-dask==2024.12.0
-datapi==0.1.1
-distlib==0.3.9
-docstring_parser==0.16
-earthkit-meteo==0.3.0
-eccodes==2.39.1
-ecmwf-api-client==1.6.3
-ecmwf-opendata==0.3.14
-ecmwflibs==0.6.3
-entrypoints==0.4
-filelock==3.16.1
-findlibs==0.0.5
-fire==0.7.0
-flatbuffers==24.3.25
-frozenlist==1.5.0
-fsspec==2024.10.0
-future==1.0.0
-gast==0.6.0
-google-auth==2.37.0
-google-auth-oauthlib==1.2.1
-google-pasta==0.2.0
-grpcio==1.68.1
-h5py==3.12.1
-idna==3.10
-imageio==2.36.1
-importlib_metadata==8.5.0
-importlib_resources==6.4.5
-Jinja2==3.1.5
-jsonargparse==4.35.0
-keras==3.9.0
-libclang==18.1.1
-lightning==2.4.0
-lightning-utilities==0.11.9
-locket==1.0.0
-lru-dict==1.3.0
-Magics==1.5.8
-Markdown==3.7
-markdown-it-py==3.0.0
-MarkupSafe==3.0.2
-mdurl==0.1.2
-ml-dtypes==0.4.1
-mpmath==1.3.0
-multidict==6.1.0
-multiurl==0.3.3
-namex==0.0.8
-netCDF4==1.7.2
-networkx==3.4.2
-nox==2024.10.9
-numpngw==0.1.4
-numpy==2.0.2
-nvidia-cublas-cu12==12.4.5.8
-nvidia-cuda-cupti-cu12==12.4.127
-nvidia-cuda-nvrtc-cu12==12.4.127
-nvidia-cuda-runtime-cu12==12.4.127
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.2.1.3
-nvidia-curand-cu12==10.3.5.147
-nvidia-cusolver-cu12==11.6.1.9
-nvidia-cusparse-cu12==12.3.1.170
-nvidia-nccl-cu12==2.21.5
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.4.127
-oauthlib==3.2.2
-omegaconf==2.3.0
-opt_einsum==3.4.0
-optree==0.13.1
-packaging==24.2
-pandas==2.2.3
-partd==1.4.2
-pdbufr==0.11.0
-pillow==11.0.0
-pip==24.3.1
-platformdirs==4.3.6
-propcache==0.2.1
-protobuf==5.29.1
-pyasn1==0.6.1
-pyasn1_modules==0.4.1
-pycparser==2.22
-pyDeprecate==0.3.2
-Pygments==2.18.0
-pyodc==1.4.1
-python-dateutil==2.9.0.post0
-pytorch-lightning==2.4.0
-pytorch-ranger==0.1.1
-pytz==2024.2
-PyYAML==6.0.2
-randomname==0.2.1
-requests==2.32.3
-requests-oauthlib==2.0.0
-rich==13.9.4
-rsa==4.9
-setuptools==75.1.0
-six==1.17.0
-sympy==1.13.1
-tensorboard==2.18.0
-tensorboard-data-server==0.7.2
-tensorboard-plugin-wit==1.8.1
-tensorflow==2.18.0
-tensorflow-estimator==2.15.0
-tensorflow-io-gcs-filesystem==0.37.1
-termcolor==2.5.0
-tomli==2.2.1
-toolz==1.0.0
-torch==2.6.0+cu124
-torch-optimizer==0.3.0
-torchmetrics==1.6.0
-tqdm==4.67.1
-triton==3.2.0
-typeshed_client==2.7.0
-typing_extensions==4.12.2
-tzdata==2024.2
-urllib3==2.2.3
-virtualenv==20.28.0
-Werkzeug==3.1.3
-wheel==0.44.0
-wrapt==1.17.0
-xarray==2024.11.0
-yarl==1.18.3
-zipp==3.21.0
diff --git a/weather-forecast/ecrad-3d-correction/unets/tests/test_data.py b/weather-forecast/ecrad-3d-correction/unets/tests/test_data.py
index 1fb54d0e..a73f3fdd 100644
--- a/weather-forecast/ecrad-3d-correction/unets/tests/test_data.py
+++ b/weather-forecast/ecrad-3d-correction/unets/tests/test_data.py
@@ -48,7 +48,7 @@ def setUp(self) -> None:
     def tearDown(self) -> None:
         rmtree(self.root)
 
-    def create_env(self, root) -> None:
+    def create_env(self, root: str) -> None:
         """Build a data/processed environment with a single input."""
         data_path = osp.join(root, "processed")
         os.makedirs(osp.join(data_path, "x"), exist_ok=True)
diff --git a/weather-forecast/ecrad-3d-correction/unets/tests/test_models.py b/weather-forecast/ecrad-3d-correction/unets/tests/test_models.py
index bfa63a4f..ab4ba210 100644
--- a/weather-forecast/ecrad-3d-correction/unets/tests/test_models.py
+++ b/weather-forecast/ecrad-3d-correction/unets/tests/test_models.py
@@ -13,6 +13,7 @@
 import os.path as osp
 from shutil import rmtree
 from tempfile import mkdtemp
+from typing import TypedDict
 from unittest import TestCase, main
 
 import torch
@@ -21,6 +22,18 @@
 
 from models import LitUnet1D
 
+InitParam = TypedDict(
+    "InitParam",
+    {
+        "data_path": str,
+        "in_channels": int,
+        "out_channels": int,
+        "n_levels": int,
+        "n_features_root": int,
+        "lr": float,
+    },
+)
+
 
 class TestModels(TestCase):
 
@@ -33,7 +46,7 @@ def setUp(self) -> None:
         self.root = mkdtemp()
         self.create_env(self.root)
 
-        self.initParam = {
+        self.initParam: InitParam = {
             "data_path": self.root,
             "in_channels": self.in_ch,
             "out_channels": self.out_ch,
@@ -45,7 +58,7 @@ def setUp(self) -> None:
     def tearDown(self) -> None:
         rmtree(self.root)
 
-    def create_env(self, root) -> None:
+    def create_env(self, root: str) -> None:
         """Build an environment with stats.pt."""
         torch.save(
             {
@@ -59,7 +72,7 @@ def create_env(self, root) -> None:
             osp.join(root, "stats.pt"),
         )
 
-    def test_forward_common_step(self):
+    def test_forward_common_step(self) -> None:
         # Fake data, of dim (n_batchs, height, n_channels).
         x = zeros((1, self.height, self.in_ch))
         y = zeros((1, self.height, self.out_ch))
@@ -74,7 +87,7 @@ def test_forward_common_step(self):
         loss = test_unet._common_step(batch=(x, y), stage="train")
         self.assertEqual(len(loss), 2)
 
-    def test_configure_optimizers(self):
+    def test_configure_optimizers(self) -> None:
         test_unet = LitUnet1D(**self.initParam)
         op = test_unet.configure_optimizers()
         self.assertTrue(isinstance(op, Optimizer))
diff --git a/weather-forecast/ecrad-3d-correction/unets/tests/test_unet.py b/weather-forecast/ecrad-3d-correction/unets/tests/test_unet.py
index a5f86fb0..6d0735c0 100644
--- a/weather-forecast/ecrad-3d-correction/unets/tests/test_unet.py
+++ b/weather-forecast/ecrad-3d-correction/unets/tests/test_unet.py
@@ -20,11 +20,11 @@
 class TestUnet1D(TestCase):
     """Testing 1D U-nets."""
 
-    def n_conv(self, n_levels: int, bilinear: bool = False):
+    def n_conv(self, n_levels: int, bilinear: bool = False) -> int:
         # 2 per DoubleConv + 1 per upsampler.
         return 4 * n_levels + (n_levels - 1 if bilinear else 0)
 
-    def test_1d(self):
+    def test_1d(self) -> None:
 
         n_levels = 1
         net = UNet1D(inp_ch=1, out_ch=1, n_levels=n_levels, n_features_root=4)
@@ -42,19 +42,19 @@ def test_1d(self):
         self.assertEqual(summary.count("Upsampler"), n_levels - 1)
         self.assertEqual(summary.count("Conv1d"), self.n_conv(n_levels))
 
-    def test_inference_1d(self):
+    def test_inference_1d(self) -> None:
         net = UNet1D(inp_ch=1, out_ch=1, n_levels=3, n_features_root=4)
         inp = rand(1, 1, 16)
         shp = tuple(net(inp).shape)
         self.assertEqual(shp, (1, 1, 16))
 
-    def test_downsampler(self):
+    def test_downsampler(self) -> None:
         sampler = Downsampler(inp_ch=4, out_ch=8)
         inp = rand(1, 4, 16)
         shp = tuple(sampler(inp).shape)
         self.assertEqual(shp, (1, 8, 8))
 
-    def test_upsampler(self):
+    def test_upsampler(self) -> None:
         sampler = Upsampler(inp_ch=8, out_ch=4)
         inp = rand(1, 8, 16)
         res = rand(1, 4, 32)
diff --git a/weather-forecast/ecrad-3d-correction/unets/trainer.py b/weather-forecast/ecrad-3d-correction/unets/trainer.py
index 9983a888..e83dce54 100644
--- a/weather-forecast/ecrad-3d-correction/unets/trainer.py
+++ b/weather-forecast/ecrad-3d-correction/unets/trainer.py
@@ -10,17 +10,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lightning.pytorch.cli import LightningCLI
+from lightning.pytorch.cli import LightningArgumentParser, LightningCLI
 
 
 class MyLightningCLI(LightningCLI):
-    def add_arguments_to_parser(self, parser):
+    def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
         parser.link_arguments(
             "data.data_path", "model.init_args.data_path", apply_on="instantiate"
         )
 
 
-def main():
+def main() -> None:
     cli = MyLightningCLI(run=False, parser_kwargs={"parser_mode": "omegaconf"})
     cli.trainer.fit(model=cli.model, datamodule=cli.datamodule)
     cli.trainer.test(model=cli.model, datamodule=cli.datamodule)
diff --git a/weather-forecast/ecrad-3d-correction/unets/unet.py b/weather-forecast/ecrad-3d-correction/unets/unet.py
index 263cf703..4ecb2fc3 100644
--- a/weather-forecast/ecrad-3d-correction/unets/unet.py
+++ b/weather-forecast/ecrad-3d-correction/unets/unet.py
@@ -10,6 +10,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import List
+
 import torch.nn as nn
 from torch import Tensor, cat
 
@@ -32,12 +34,12 @@ def __init__(
         n_levels: int,
         n_features_root: int,
         bilinear: bool = False,
-    ):
+    ) -> None:
         super().__init__()
         self.n_levels = n_levels
 
         # First level hardcoded.
-        layers = [DoubleConv(inp_ch, n_features_root)]
+        layers: List[nn.Module] = [DoubleConv(inp_ch, n_features_root)]
 
         # Downward path.
         f = n_features_root
@@ -117,7 +119,7 @@ class Upsampler(nn.Module):
 
     def __init__(self, inp_ch: int, out_ch: int, bilinear: bool = False):
         super().__init__()
-        self.upsample = None
+        self.upsample: nn.Module
         if inp_ch < 2:
             raise ValueError(f"Input channel ({inp_ch}) too low.")
 
diff --git a/weather-forecast/ecrad-3d-correction/unets/uv.lock b/weather-forecast/ecrad-3d-correction/unets/uv.lock
new file mode 100644
index 00000000..221c1434
--- /dev/null
+++ b/weather-forecast/ecrad-3d-correction/unets/uv.lock
@@ -0,0 +1,3901 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.14' and platform_machine == 'ARM64' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 'ARM64' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 'ARM64' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and platform_machine == 'ARM64' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 'ARM64' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and platform_machine != 'ARM64' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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+    { url = "https://files.pythonhosted.org/packages/32/75/f8919b2eafc929567d3d8411f72bdb1a2109c01caaab4ebfa5f8ffadc15b/yarl-1.22.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0b5bcc1a9c4839e7e30b7b30dd47fe5e7e44fb7054ec29b5bb8d526aa1041093", size = 357140, upload-time = "2025-10-06T14:12:08.362Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/72/6a85bba382f22cf78add705d8c3731748397d986e197e53ecc7835e76de7/yarl-1.22.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c0232bce2170103ec23c454e54a57008a9a72b5d1c3105dc2496750da8cfa47c", size = 341473, upload-time = "2025-10-06T14:12:10.994Z" },
+    { url = "https://files.pythonhosted.org/packages/35/18/55e6011f7c044dc80b98893060773cefcfdbf60dfefb8cb2f58b9bacbd83/yarl-1.22.0-cp314-cp314t-win32.whl", hash = "sha256:8009b3173bcd637be650922ac455946197d858b3630b6d8787aa9e5c4564533e", size = 89056, upload-time = "2025-10-06T14:12:13.317Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/86/0f0dccb6e59a9e7f122c5afd43568b1d31b8ab7dda5f1b01fb5c7025c9a9/yarl-1.22.0-cp314-cp314t-win_amd64.whl", hash = "sha256:9fb17ea16e972c63d25d4a97f016d235c78dd2344820eb35bc034bc32012ee27", size = 96292, upload-time = "2025-10-06T14:12:15.398Z" },
+    { url = "https://files.pythonhosted.org/packages/48/b7/503c98092fb3b344a179579f55814b613c1fbb1c23b3ec14a7b008a66a6e/yarl-1.22.0-cp314-cp314t-win_arm64.whl", hash = "sha256:9f6d73c1436b934e3f01df1e1b21ff765cd1d28c77dfb9ace207f746d4610ee1", size = 85171, upload-time = "2025-10-06T14:12:16.935Z" },
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+]
+
+[[package]]
+name = "zipp"
+version = "3.23.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547, upload-time = "2025-06-08T17:06:39.4Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" },
+]
diff --git a/weather-forecast/gravity-wave-drag/cnns/ci/generate_synthetic_data.py b/weather-forecast/gravity-wave-drag/cnns/ci/generate_synthetic_data.py
index a628093b..dd28b8e0 100644
--- a/weather-forecast/gravity-wave-drag/cnns/ci/generate_synthetic_data.py
+++ b/weather-forecast/gravity-wave-drag/cnns/ci/generate_synthetic_data.py
@@ -23,7 +23,7 @@
 data_path = "./data"
 
 
-def create_data():
+def create_data() -> None:
     """Create data folder with fake raw data"""
     file_split = {
         "train": ["test_1.h5", "test_2.h5", "test_3.h5"],
diff --git a/weather-forecast/gravity-wave-drag/cnns/data.py b/weather-forecast/gravity-wave-drag/cnns/data.py
index 5539f9f7..6b33d043 100644
--- a/weather-forecast/gravity-wave-drag/cnns/data.py
+++ b/weather-forecast/gravity-wave-drag/cnns/data.py
@@ -30,8 +30,8 @@ class NOGWDDataset(torch.utils.data.Dataset):
         Output features: .
     """
 
-    x_feat = 191
-    y_feat = 126
+    x_feat: int = 191
+    y_feat: int = 126
 
     def __init__(
         self, root: str, mode: str, shard_len: int, splitting_file: str
@@ -124,7 +124,7 @@ def __len__(self) -> int:
         """
         return len(self.raw_filenames) * self.shard_len
 
-    def __getitem__(self, idx: int) -> Tuple[torch.Tensor]:
+    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
         """Return the Tensor at the given index.
 
         Returns:
@@ -159,9 +159,9 @@ def __init__(
         self.shard_len = shard_len
         self.data_path = data_path
         super().__init__()
-        self.train = None
-        self.val = None
-        self.test = None
+        self.train: NOGWDDataset
+        self.val: NOGWDDataset
+        self.test: NOGWDDataset
 
     def prepare_data(self) -> None:
         """Not used. The download logic is the responsibility for the Dataset."""
diff --git a/weather-forecast/gravity-wave-drag/cnns/env.yaml b/weather-forecast/gravity-wave-drag/cnns/env.yaml
index 0186867e..8bce1bdc 100644
--- a/weather-forecast/gravity-wave-drag/cnns/env.yaml
+++ b/weather-forecast/gravity-wave-drag/cnns/env.yaml
@@ -1,7 +1,6 @@
 # This file defines the global environment dependencies for this model project.
 # It is read by the CI/CD workflows in charge of the tests (ai4sim-ci-cd.yaml)
 # and the build of the Docker image (images-ci-cd.yaml) to set the environment.
-python_version: 3.10
 ubuntu_base_image:
   name: nvidia/cuda
   tag: 12.4.1-cudnn-runtime-ubuntu22.04
\ No newline at end of file
diff --git a/weather-forecast/gravity-wave-drag/cnns/models.py b/weather-forecast/gravity-wave-drag/cnns/models.py
index 994ebf5a..866b895a 100644
--- a/weather-forecast/gravity-wave-drag/cnns/models.py
+++ b/weather-forecast/gravity-wave-drag/cnns/models.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import os
-from typing import Tuple
+from typing import Any, Tuple
 
 import lightning as pl
 import torch
@@ -28,6 +28,8 @@ class NOGWDModule(pl.LightningModule):
     def __init__(self, data_path: str):
         """Load previously computed stats for model-level normalization purposes."""
         super().__init__()
+        self.net: nn.Module
+        self.lr: float
         # Init attributes to fake data
         self.x_mean = torch.tensor(0)
         self.x_std = torch.tensor(1)
@@ -39,7 +41,7 @@ def __init__(self, data_path: str):
             self.x_std = stats["x_std"]
             self.y_std = stats["y_std"].max()
 
-    def forward(self, features: torch.Tensor):
+    def forward(self, features: torch.Tensor) -> torch.Tensor:
         """Compute the forward pass.
 
         Args:
@@ -52,7 +54,7 @@ def forward(self, features: torch.Tensor):
 
     def _common_step(
         self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int, stage: str
-    ):
+    ) -> torch.Tensor:
         """Define the common operations performed on data."""
         x_val, y_val = batch
         x_val = (x_val - self.x_mean.to(self.device)) / self.x_std.to(self.device)
@@ -69,7 +71,9 @@ def _common_step(
 
         return loss
 
-    def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int):
+    def training_step(
+        self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int
+    ) -> torch.Tensor:
         """Compute one training step.
 
         Args:
@@ -82,7 +86,9 @@ def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int
         loss = self._common_step(batch, batch_idx, "train")
         return loss
 
-    def validation_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int):
+    def validation_step(
+        self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int
+    ) -> None:
         """Compute one validation step.
 
         Args:
@@ -91,7 +97,9 @@ def validation_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: i
         """
         self._common_step(batch, batch_idx, "val")
 
-    def test_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int):
+    def test_step(
+        self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int
+    ) -> None:
         """Compute one testing step.
 
         Args:
@@ -100,7 +108,7 @@ def test_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int):
         """
         self._common_step(batch, batch_idx, "test")
 
-    def configure_optimizers(self):
+    def configure_optimizers(self) -> optim.Optimizer:
         """Set the model optimizer.
 
         Returns:
@@ -118,9 +126,9 @@ def __init__(
         hidden_channels: int,
         out_channels: int,
         lr: float,
-        *args,
-        **kwargs,
-    ):
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
         """Define the network LitMLP architecture."""
         super().__init__(*args, **kwargs)
 
@@ -144,7 +152,7 @@ class LitCNN(NOGWDModule):
     class Reshape(nn.Module):
         """Create a custom Reshape layer."""
 
-        def forward(self, tensor: torch.Tensor):
+        def forward(self, tensor: torch.Tensor) -> torch.Tensor:
             """Reshape the input tensor to expose the 5 features of each columnar cell.
             The 3 first ones features are Atmospheric level dependant quantities, while the last 2
             ones are constants.
@@ -168,9 +176,9 @@ def __init__(
         pool_size: int,
         out_channels: int,
         lr: float,
-        *args,
-        **kwargs,
-    ):
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
         """Define the network LitCNN architecture."""
         super().__init__(*args, **kwargs)
 
diff --git a/weather-forecast/gravity-wave-drag/cnns/mypy.ini b/weather-forecast/gravity-wave-drag/cnns/mypy.ini
new file mode 100644
index 00000000..bd9a60bc
--- /dev/null
+++ b/weather-forecast/gravity-wave-drag/cnns/mypy.ini
@@ -0,0 +1,10 @@
+[mypy]
+
+mypy_path = weather-forecast/gravity-wave-drag/cnns
+
+# Override mypy default value `ignore_missing_imports = false` to `true` for modules with no typing
+# Has the effect of silencing `import-untyped` error
+[mypy-h5py]
+ignore_missing_imports = true
+[mypy-yaml]
+ignore_missing_imports = true
\ No newline at end of file
diff --git a/weather-forecast/gravity-wave-drag/cnns/noxfile.py b/weather-forecast/gravity-wave-drag/cnns/noxfile.py
index 2585a153..517a6f3c 100644
--- a/weather-forecast/gravity-wave-drag/cnns/noxfile.py
+++ b/weather-forecast/gravity-wave-drag/cnns/noxfile.py
@@ -20,6 +20,8 @@
 import os
 import sys
 
+import nox
+
 # Insert the tools/nox folder to the python path to fetch the nox_ref_file.py content
 current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 common_dir = os.path.dirname(os.path.dirname(os.path.dirname(current_dir)))
@@ -30,3 +32,4 @@
 from nox_ref_file import *
 
 # Insert below the model project specific targets
+nox.needs_version = "==2026.2.9"
diff --git a/weather-forecast/gravity-wave-drag/cnns/pyproject.toml b/weather-forecast/gravity-wave-drag/cnns/pyproject.toml
new file mode 100644
index 00000000..990323a4
--- /dev/null
+++ b/weather-forecast/gravity-wave-drag/cnns/pyproject.toml
@@ -0,0 +1,53 @@
+[project]
+name = "gravity-wave-drag-cnns"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    "h5py==3.12.1",
+    "jsonargparse[signatures]==4.35.0",
+    "lightning==2.4.0",
+    "omegaconf==2.3.0",
+    "tensorboard==2.18.0",
+    "torch==2.8.0+cu128",
+    "torch-optimizer==0.3.0"
+]
+
+# Nox will find here the python version that will be used
+classifiers = [
+  "Programming Language :: Python :: 3.10",
+]
+
+[dependency-groups]
+tests = [
+    "pytest-cov==7.0.0"
+]
+coverage = [
+    "coverage==7.13.4"
+]
+black = [
+    "black==26.1.0"
+]
+isort = [
+    "isort==7.0.0"
+]
+mypy = [
+    "mypy==1.19.1"
+]
+lint = [
+    "flake8==7.3.0",
+    "flake8-docstrings==1.7.0",
+    "flake8-use-fstring==1.4",
+    "flake8-variables-names==0.0.6",
+    "pep8-naming==0.15.1"
+]
+data = []
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch" },
+]
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl"
+explicit = true
diff --git a/weather-forecast/gravity-wave-drag/cnns/requirements.txt b/weather-forecast/gravity-wave-drag/cnns/requirements.txt
deleted file mode 100644
index c0ee87b4..00000000
--- a/weather-forecast/gravity-wave-drag/cnns/requirements.txt
+++ /dev/null
@@ -1,86 +0,0 @@
-absl-py==2.1.0
-aiohappyeyeballs==2.4.4
-aiohttp==3.11.10
-aiosignal==1.3.2
-antlr4-python3-runtime==4.9.3
-argcomplete==3.5.2
-async-timeout==5.0.1
-attrs==24.3.0
-cachetools==5.5.0
-certifi==2024.12.14
-charset-normalizer==3.4.0
-colorlog==6.9.0
-distlib==0.3.9
-docstring_parser==0.16
-filelock==3.16.1
-fire==0.7.0
-frozenlist==1.5.0
-fsspec==2024.10.0
-future==1.0.0
-google-auth==2.37.0
-google-auth-oauthlib==1.2.1
-grpcio==1.68.1
-h5py==3.12.1
-idna==3.10
-importlib_metadata==8.5.0
-importlib_resources==6.4.5
-Jinja2==3.1.5
-jsonargparse==4.35.0
-lightning==2.4.0
-lightning-utilities==0.11.9
-Markdown==3.7
-MarkupSafe==3.0.2
-mpmath==1.3.0
-multidict==6.1.0
-networkx==3.4.2
-nox==2024.10.9
-numpy==2.2.0
-nvidia-cublas-cu12==12.4.5.8
-nvidia-cuda-cupti-cu12==12.4.127
-nvidia-cuda-nvrtc-cu12==12.4.127
-nvidia-cuda-runtime-cu12==12.4.127
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.2.1.3
-nvidia-curand-cu12==10.3.5.147
-nvidia-cusolver-cu12==11.6.1.9
-nvidia-cusparse-cu12==12.3.1.170
-nvidia-nccl-cu12==2.21.5
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.4.127
-oauthlib==3.2.2
-omegaconf==2.3.0
-packaging==24.2
-pip==24.3.1
-platformdirs==4.3.6
-propcache==0.2.1
-protobuf==5.29.1
-pyasn1==0.6.1
-pyasn1_modules==0.4.1
-pyDeprecate==0.3.2
-pytorch-lightning==2.4.0
-pytorch-ranger==0.1.1
-PyYAML==6.0.2
-randomname==0.2.1
-requests==2.32.3
-requests-oauthlib==2.0.0
-rsa==4.9
-setuptools==75.1.0
-six==1.17.0
-sympy==1.13.1
-tensorboard==2.18.0
-tensorboard-data-server==0.7.2
-termcolor==2.5.0
-tomli==2.2.1
-torch==2.6.0+cu124
-torch-optimizer==0.3.0
-torchmetrics==1.6.0
-tqdm==4.67.1
-triton==3.2.0
-typeshed_client==2.7.0
-typing_extensions==4.12.2
-urllib3==2.2.3
-virtualenv==20.28.0
-Werkzeug==3.1.3
-wheel==0.44.0
-yarl==1.18.3
-zipp==3.21.0
diff --git a/weather-forecast/gravity-wave-drag/cnns/tests/test_data.py b/weather-forecast/gravity-wave-drag/cnns/tests/test_data.py
index 2babf74d..c7444a6d 100644
--- a/weather-forecast/gravity-wave-drag/cnns/tests/test_data.py
+++ b/weather-forecast/gravity-wave-drag/cnns/tests/test_data.py
@@ -19,7 +19,7 @@
 
 import numpy as np
 import torch
-from test_utils import get_filenames, populate_test_data
+from test_utils import get_filenames, populate_test_data  # type: ignore
 
 from data import NOGWDDataModule, NOGWDDataset
 
@@ -52,16 +52,16 @@ def setUp(self) -> None:
             splitting_file=REF_FILENAMES_FILE,
         )
 
-    def test__compute_stats(self):
+    def test__compute_stats(self) -> None:
         """Test the instantiated NOGWDDataset class in train mode, has created the stat file."""
         self.assertTrue(os.path.isfile(os.path.join(TEST_DATA_PATH, "stats.pt")))
 
-    def test_raw_filenames_property(self):
+    def test_raw_filenames_property(self) -> None:
         """Test the 'raw_filenames' property is properly set with the train dataset files."""
         expected_file_list = ["2015-01-01.h5", "2016-01-01.h5", "2017-01-01.h5"]
         self.assertListEqual(self.data_test.raw_filenames, expected_file_list)
 
-    def test_download(self):
+    def test_download(self) -> None:
         """Test 'download' method is not yet implemented."""
         self.assertRaisesRegex(
             NotImplementedError,
@@ -69,7 +69,7 @@ def test_download(self):
             self.data_test.download,
         )
 
-    def test_load(self):
+    def test_load(self) -> None:
         """Test the 'load' method returns tensors with good shapes."""
         x, y = self.data_test.load()
         self.assertIsInstance(x, torch.Tensor)
@@ -82,7 +82,7 @@ def test_load(self):
         self.assertEqual(x.dtype, torch.float32)
         self.assertEqual(y.dtype, torch.float32)
 
-    def test_get(self):
+    def test_get(self) -> None:
         """Test the '__getitem__' method returns the proper (x, y) tuple of tensors."""
         data_get = self.data_test[1]
         np.testing.assert_array_equal(
@@ -92,7 +92,7 @@ def test_get(self):
             data_get[1].numpy(), self.data_test.y[1, :].numpy()
         )
 
-    def test_len(self):
+    def test_len(self) -> None:
         """Test the '__len__' method returns the proper value.
         FIXME: note for now the 'shard_len' is hardcoded, but should be dynamically set.
         """
@@ -130,11 +130,11 @@ def setUp(self) -> None:
             splitting_file=REF_FILENAMES_FILE,
         )
 
-    def test__compute_stats(self):
+    def test__compute_stats(self) -> None:
         """Test the instantiated NOGWDDataset class in train mode, has NOT created the stat file."""
         self.assertFalse(os.path.isfile(os.path.join(TEST_DATA_PATH, "stats.pt")))
 
-    def test_raw_filenames_property(self):
+    def test_raw_filenames_property(self) -> None:
         """Test the 'raw_filenames' property is properly set with the train dataset files."""
         expected_file_list = ["2017-02-19.h5"]
         self.assertListEqual(self.data_test.raw_filenames, expected_file_list)
@@ -169,11 +169,11 @@ def setUp(self) -> None:
             splitting_file=REF_FILENAMES_FILE,
         )
 
-    def test__compute_stats(self):
+    def test__compute_stats(self) -> None:
         """Test the instantiated NOGWDDataset class in train mode, has NOT created the stat file."""
         self.assertFalse(os.path.isfile(os.path.join(TEST_DATA_PATH, "stats.pt")))
 
-    def test_raw_filenames_property(self):
+    def test_raw_filenames_property(self) -> None:
         """Test the 'raw_filenames' property is properly set with the train dataset files."""
         expected_file_list = ["2016-02-25.h5"]
         self.assertListEqual(self.data_test.raw_filenames, expected_file_list)
@@ -209,35 +209,35 @@ def setUp(self) -> None:
             splitting_file=REF_FILENAMES_FILE,
         )
 
-    def test_setup_fit(self):
+    def test_setup_fit(self) -> None:
         """Test the 'setup' method set properly the train and val attributes in 'fit' mode."""
         self.data_module.setup("fit")
         self.assertIsInstance(self.data_module.train, NOGWDDataset)
         self.assertIsInstance(self.data_module.val, NOGWDDataset)
-        self.assertIsNone(self.data_module.test)
+        self.assertFalse(hasattr(self.data_module, "test"))
 
-    def test_setup_test(self):
+    def test_setup_test(self) -> None:
         """Test the 'setup' method set properly the test attribute in 'test' mode."""
         self.data_module.setup("test")
-        self.assertIsNone(self.data_module.train)
-        self.assertIsNone(self.data_module.val)
+        self.assertFalse(hasattr(self.data_module, "train"))
+        self.assertFalse(hasattr(self.data_module, "val"))
         self.assertIsInstance(self.data_module.test, NOGWDDataset)
 
-    def test_train_dataloader(self):
+    def test_train_dataloader(self) -> None:
         """Test the 'train_dataloader' method properly instantiate a DataLoader."""
         self.data_module.setup("fit")
         dataloader = self.data_module.train_dataloader()
         self.assertIsInstance(dataloader, torch.utils.data.DataLoader)
 
-    def test_val_dataloader(self):
+    def test_val_dataloader(self) -> None:
         """Test the 'val_dataloader' method properly instantiate a DataLoader."""
         self.data_module.setup("fit")
         dataloader = self.data_module.val_dataloader()
         self.assertIsInstance(dataloader, torch.utils.data.DataLoader)
 
-    def test_test_dataloader(self):
+    def test_test_dataloader(self) -> None:
         """Test the 'test_dataloader' method properly instantiate a DataLoader."""
-        self.data_module.setup("fit")
+        self.data_module.setup("test")
         dataloader = self.data_module.test_dataloader()
         self.assertIsInstance(dataloader, torch.utils.data.DataLoader)
 
diff --git a/weather-forecast/gravity-wave-drag/cnns/tests/test_models.py b/weather-forecast/gravity-wave-drag/cnns/tests/test_models.py
index 84a317ba..0d534d4a 100644
--- a/weather-forecast/gravity-wave-drag/cnns/tests/test_models.py
+++ b/weather-forecast/gravity-wave-drag/cnns/tests/test_models.py
@@ -19,7 +19,7 @@
 
 import torch
 import torch_optimizer as optim
-from test_utils import get_filenames, populate_test_data
+from test_utils import get_filenames, populate_test_data  # type: ignore
 
 from data import NOGWDDataset
 from models import LitCNN, LitMLP
@@ -61,7 +61,7 @@ def setUp(self) -> None:
             data_path=TEST_DATA_PATH,
         )
 
-    def test_forward(self):
+    def test_forward(self) -> None:
         """Test the 'forward' method returns a properly formatted tensor."""
         test_forward = self.model_test.forward(self.data[0])
         self.assertIsInstance(test_forward, torch.Tensor)
@@ -70,19 +70,19 @@ def test_forward(self):
         # 126 : nb of feature for y
         self.assertEqual(test_forward.size(), torch.Size([3 * 36 * 10, 126]))
 
-    def test_compute_stats(self):
+    def test_compute_stats(self) -> None:
         """Test the '_compute_stats' method returns a scalar tensor as the loss."""
         loss = self.model_test._common_step(self.data, 10, "stage")
         self.assertIsInstance(loss, torch.Tensor)
         self.assertEqual(loss.numel(), 1)
 
-    def test_training_step(self):
+    def test_training_step(self) -> None:
         """Test the 'training_step' method returns a scalar tensor as the loss."""
         loss = self.model_test.training_step(self.data, 10)
         self.assertIsInstance(loss, torch.Tensor)
         self.assertEqual(loss.numel(), 1)
 
-    def test_configure_optimizers(self):
+    def test_configure_optimizers(self) -> None:
         """Test the 'configure_optimizers' method returns an 'optim.AdamP' object."""
         optimizer = self.model_test.configure_optimizers()
         self.assertIsInstance(optimizer, optim.AdamP)
@@ -128,26 +128,26 @@ def setUp(self) -> None:
             data_path=TEST_DATA_PATH,
         )
 
-    def test_forward(self):
+    def test_forward(self) -> None:
         """Test the 'forward' method returns a properly formatted tensor."""
         print(self.model_test)
         test_forward = self.model_test.forward(self.data[0])
         self.assertIsInstance(test_forward, torch.Tensor)
         self.assertEqual(test_forward.size(), torch.Size([1080, 126]))
 
-    def test_compute_stats(self):
+    def test_compute_stats(self) -> None:
         """Test the '_compute_stats' method returns a scalar tensor as the loss."""
         loss = self.model_test._common_step(self.data, 10, "stage")
         self.assertIsInstance(loss, torch.Tensor)
         self.assertEqual(loss.numel(), 1)
 
-    def test_training_step(self):
+    def test_training_step(self) -> None:
         """Test the 'training_step' method returns a scalar tensor as the loss."""
         loss = self.model_test.training_step(self.data, 10)
         self.assertIsInstance(loss, torch.Tensor)
         self.assertEqual(loss.numel(), 1)
 
-    def test_configure_optimizers(self):
+    def test_configure_optimizers(self) -> None:
         """Test the 'configure_optimizers' method returns an 'optim.AdamP' object."""
         optimizer = self.model_test.configure_optimizers()
         self.assertIsInstance(optimizer, optim.AdamP)
diff --git a/weather-forecast/gravity-wave-drag/cnns/tests/test_utils.py b/weather-forecast/gravity-wave-drag/cnns/tests/test_utils.py
index bfcf9da8..87054580 100644
--- a/weather-forecast/gravity-wave-drag/cnns/tests/test_utils.py
+++ b/weather-forecast/gravity-wave-drag/cnns/tests/test_utils.py
@@ -20,7 +20,7 @@
 import yaml
 
 
-def get_filenames(filenames_file):
+def get_filenames(filenames_file: str) -> list:
     """Extract the list of file that are required for test, from the input file.
 
     Args:
@@ -37,7 +37,7 @@ def get_filenames(filenames_file):
     return filenames
 
 
-def populate_test_data(root_data_path, filenames) -> None:
+def populate_test_data(root_data_path: str, filenames: list) -> None:
     """Create fake random data file.
 
     Args:
diff --git a/weather-forecast/gravity-wave-drag/cnns/trainer.py b/weather-forecast/gravity-wave-drag/cnns/trainer.py
index 12f79a21..d25076ba 100644
--- a/weather-forecast/gravity-wave-drag/cnns/trainer.py
+++ b/weather-forecast/gravity-wave-drag/cnns/trainer.py
@@ -12,17 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lightning.pytorch.cli import LightningCLI
+from lightning.pytorch.cli import LightningArgumentParser, LightningCLI
 
 
 class MyLightningCLI(LightningCLI):
-    def add_arguments_to_parser(self, parser):
+    def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
         parser.link_arguments(
             "data.data_path", "model.init_args.data_path", apply_on="instantiate"
         )
 
 
-def main():
+def main() -> None:
     """Instantiate the CLI and launch the trainer."""
     cli = MyLightningCLI(run=False, parser_kwargs={"parser_mode": "omegaconf"})
     cli.trainer.fit(model=cli.model, datamodule=cli.datamodule)
diff --git a/weather-forecast/gravity-wave-drag/cnns/uv.lock b/weather-forecast/gravity-wave-drag/cnns/uv.lock
new file mode 100644
index 00000000..17bf518d
--- /dev/null
+++ b/weather-forecast/gravity-wave-drag/cnns/uv.lock
@@ -0,0 +1,2171 @@
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+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
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diff --git a/weather-forecast/nwp-emulation/panguweather/.coveragerc b/weather-forecast/nwp-emulation/panguweather/.coveragerc
new file mode 100644
index 00000000..acb4e457
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/.coveragerc
@@ -0,0 +1,3 @@
+[run]
+include = ./*.py
+omit = noxfile.py,tests/*
diff --git a/weather-forecast/nwp-emulation/panguweather/.gitignore b/weather-forecast/nwp-emulation/panguweather/.gitignore
new file mode 100755
index 00000000..5495c782
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/.gitignore
@@ -0,0 +1,32 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+
+# Build tool
+.nox/
+.ci-reports/
+
+# Unit test / coverage reports
+.pytest_cache/
+.coverage
+.coverage-*
+.coverage.*
+coverage.xml
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.vscode
+
+# Data
+data/
+
+# Experiments
+experiments/
\ No newline at end of file
diff --git a/weather-forecast/nwp-emulation/panguweather/README.md b/weather-forecast/nwp-emulation/panguweather/README.md
new file mode 100644
index 00000000..82ae1f8d
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/README.md
@@ -0,0 +1,43 @@
+# PanguWeather
+
+## Description
+
+This repository contains an implementation of the **PanguWeather** model. PanguWeather is a deep learning-based weather forecasting model that leverages high-resolution, gridded datasets to produce fast, accurate global forecasts.
+
+This work builds on the research of Bi et al. (2023) and others who have pioneered the use of deep learning architectures for global weather forecasting. We have taken their approach and adapted it for our specific research goals.
+
+## Dataset
+
+We rely on ERA5, the state-of-the-art global reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 provides:
+
+- **Temporal coverage:** Hourly data from 1979 to present.
+- **Spatial resolution:** Approximately 31 km on a reduced Gaussian grid.
+- **Variables:** A wide range of atmospheric, land, and oceanic variables, including temperature, pressure, wind, precipitation, and radiation fluxes.
+
+
+## AI4Sim vs other implementations
+### Attention mask
+Other implementations implement a "limited area" version of the attention mask, considering borders along all the directions (pressure levels, latitudes and longitudes), following the Swin Transformers architecture.
+AI4Sim implements a more versatile version of the mask, including the limited area one and the vanilla PanguWeather one considering the continuity along the longitudes.
+
+### Surface constant masks
+AI4Sim uses more recent surface masks (from the ERA5 files). The main differences are:
+* Land-sea: continuous values between 0 and 1 for our implementation vs binary for older versions.
+* Soil type: different representation of soil types 4, 5 and 6 (total of 7 types).
+
+## MLFlow logging
+In the `configs` folder, the `config_pangu_lite_mlflow.yaml` shows how to add MLFlowLogger to the lightning config file. 
+If the mlflow experience is logged on a server is strongly recommended to define the following shell variable before 
+running the training:
+```
+export MLFLOW_TRACKING_URI=<server url address>
+export MLFLOW_TRACKING_USERNAME=<username>
+export MLFLOW_TRACKING_PASSWORD=<password>`
+```
+## References
+
+- Bi, K., et al. (2023). *Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast.* arXiv:2211.02556.
+  
+- Hersbach, H., et al. (2020). *The ERA5 global reanalysis.* Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. [doi:10.1002/qj.3803](https://doi.org/10.1002/qj.3803)
+
+- Dueben, P. D. and Bauer, P. (2018). *Challenges and design choices for global weather and climate models based on machine learning.* Geosci. Model Dev., 11, 3999–4009.
diff --git a/weather-forecast/nwp-emulation/panguweather/__init__.py b/weather-forecast/nwp-emulation/panguweather/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/weather-forecast/nwp-emulation/panguweather/callback.py b/weather-forecast/nwp-emulation/panguweather/callback.py
new file mode 100644
index 00000000..064077fb
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/callback.py
@@ -0,0 +1,57 @@
+# Copyright 2024 Eviden.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tempfile
+from pathlib import Path
+
+from jsonargparse._namespace import Namespace
+from lightning import LightningModule, Trainer
+from lightning.pytorch.cli import LightningArgumentParser, SaveConfigCallback
+from lightning.pytorch.loggers import MLFlowLogger
+
+
+class GlobalSaveConfigCallback(SaveConfigCallback):
+    """Work around to save config file in MLFlow.
+    See https://github.com/Lightning-AI/pytorch-lightning/issues/16310
+    """
+
+    def __init__(
+        self,
+        parser: LightningArgumentParser,
+        config: Namespace,
+        config_filename: str = "config.yaml",
+        overwrite: bool = False,
+        multifile: bool = False,
+    ):
+        super().__init__(
+            parser, config, config_filename, overwrite, multifile, save_to_log_dir=True
+        )
+
+    def save_config(
+        self, trainer: Trainer, pl_module: LightningModule, stage: str
+    ) -> None:
+        if pl_module.mlflow_logger is not None:
+            assert isinstance(pl_module.mlflow_logger, MLFlowLogger)
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                config_path = Path(tmp_dir) / self.config_filename
+                self.parser.save(
+                    self.config,
+                    config_path,
+                    skip_none=False,
+                    overwrite=self.overwrite,
+                    multifile=self.multifile,
+                )
+                pl_module.mlflow_logger.experiment.log_artifact(
+                    local_path=config_path, run_id=pl_module.mlflow_logger.run_id
+                )
diff --git a/weather-forecast/nwp-emulation/panguweather/ci/configs/pangu_test.yaml b/weather-forecast/nwp-emulation/panguweather/ci/configs/pangu_test.yaml
new file mode 100644
index 00000000..8f675b90
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/ci/configs/pangu_test.yaml
@@ -0,0 +1,45 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+test:
+    model:
+        class_path: transformer_module.PanguModule
+        init_args:
+            plevel_patch_size: [2, 8, 8]
+            checkpoint_activation: False
+            # mean_file: ./data/era5_mean_from_graphcast_to_pangu.nc
+            # stddev_file: ./data/era5_stddev_from_graphcast_to_pangu.nc
+            # plevel_variable_weights: [0.2, 0.2, 0.2, 0.2, 0.2]
+            # surface_variable_weights: [0.25, 0.25, 0.25, 0.25]
+            lam: False
+            norm_cst_mask: False
+            use_scheduler: True
+            add_const2: False
+
+    data:
+        class_path: data.ERA5DataModule
+        init_args:
+            zarr_store: ./data/
+            constant_mask_list: ['land_sea_mask', 'soil_type', 'geopotential_at_surface']
+            # constant_mask_list: null
+            batch_size: 1
+            num_workers: 1
+            train_time: slice('2020-01-01', '2020-01-03')
+            val_time: slice('2020-01-01', '2020-01-02')
+            test_time: ['2020']
+            train_time_step: 6
+            test_time_step: 6
+
+    trainer:
+        max_epochs: 1
+        accelerator: "cpu"
+        devices: 1        
diff --git a/weather-forecast/nwp-emulation/panguweather/ci/generate_synthetic_data.py b/weather-forecast/nwp-emulation/panguweather/ci/generate_synthetic_data.py
new file mode 100644
index 00000000..6b0e81e2
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/ci/generate_synthetic_data.py
@@ -0,0 +1,173 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import os.path as osp
+import sys
+from datetime import datetime, timedelta
+from os.path import exists
+
+import numpy as np
+import xarray as xr
+import zarr
+
+sys.path.insert(1, "/".join(osp.realpath(__file__).split("/")[0:-2]))
+
+# isort: off
+from data import (  # noqa: E402
+    PLEVEL_LIST,
+    PLEVEL_VARIABLES,
+    SURFACE_VARIABLES,
+)
+
+
+def create_mock_era5_zarr(
+    output_path: str = "./data",
+    n_lat: int = 120,
+    n_lon: int = 240,
+    n_samples: int = 10,
+    start_date: str = "2020-01-01",
+    time_step_hours: int = 6,
+    include_constant_masks: bool = True,
+) -> str:
+    """
+    Create a mock ERA5 dataset and store it in a Zarr file format.
+
+    Args:
+        output_path (str): Path where the Zarr store will be created
+        n_samples (int): Number of time samples to generate
+        n_lat (int): Number of latitude points
+        n_lon (int): Number of longitude points
+        start_date (str): Starting date in YYYY-MM-DD format
+        time_step_hours (int): Time step between samples in hours
+        include_constant_masks (bool): Whether to include constant mask variables
+
+    Returns:
+        str: Path to the created Zarr store
+    """
+    if exists(output_path):
+        raise Exception(f"Remove manually {output_path}")
+
+    print(f"Creating mock ERA5 dataset with {n_samples} samples...")
+
+    # Create time coordinates
+    start_datetime = datetime.strptime(start_date, "%Y-%m-%d")
+    time_values = np.array(
+        [
+            start_datetime + timedelta(hours=i * time_step_hours)
+            for i in range(n_samples)
+        ],
+        dtype="datetime64[ns]",
+    )
+
+    # Create lat, lon coordinates
+    lat_values = np.linspace(-90, 90, n_lat)
+    lon_values = np.linspace(-180, 180, n_lon)
+
+    # Create pressure levels
+    plevels = PLEVEL_LIST  # Using the predefined levels from the module
+
+    # Initialize data arrays
+    data_vars = {}
+
+    # Create pressure level variables
+    for i, var_name in enumerate(PLEVEL_VARIABLES):
+        # Extract the ith variable from all samples
+        data = np.random.rand(n_samples, len(plevels), n_lat, n_lon).astype(np.float32)
+        # Create DataArray
+        data_vars[var_name] = xr.DataArray(
+            data,
+            dims=["time", "level", "latitude", "longitude"],
+            coords={
+                "time": time_values,
+                "level": plevels,
+                "latitude": lat_values,
+                "longitude": lon_values,
+            },
+            attrs={"long_name": var_name, "units": "mock_units"},
+        )
+
+    # Create surface variables
+    for i, var_name in enumerate(SURFACE_VARIABLES):
+        # Extract the ith variable from all samples
+        data = np.random.rand(n_samples, n_lat, n_lon).astype(np.float32)
+
+        # Create DataArray
+        data_vars[var_name] = xr.DataArray(
+            data,
+            dims=["time", "latitude", "longitude"],
+            coords={
+                "time": time_values,
+                "latitude": lat_values,
+                "longitude": lon_values,
+            },
+            attrs={"long_name": var_name, "units": "mock_units"},
+        )
+
+    # Add constant masks if requested
+    if include_constant_masks:
+        for mask_name in ["land_sea_mask", "soil_type", "geopotential_at_surface"]:
+            if mask_name == "land_sea_mask":
+                # Land-sea mask is binary (0 for sea, 1 for land)
+                data = np.random.randint(0, 2, (n_lat, n_lon)).astype(np.float32)
+            elif mask_name == "soil_type":
+                # Soil type typically ranges from 0 to 9
+                data = np.random.randint(0, 10, (n_lat, n_lon)).astype(np.float32)
+            elif mask_name == "geopotential_at_surface":
+                # Surface geopotential typically ranges from ~0 to ~9000 m^2/s^2
+                data = np.random.uniform(0, 9000, (n_lat, n_lon)).astype(np.float32)
+            else:
+                data = np.random.rand(n_lat, n_lon).astype(np.float32)
+
+            # Create DataArray
+            data_vars[mask_name] = xr.DataArray(
+                data,
+                dims=["latitude", "longitude"],
+                coords={"latitude": lat_values, "longitude": lon_values},
+                attrs={"long_name": mask_name, "units": "mock_units"},
+            )
+
+    # Create dataset
+    ds = xr.Dataset(
+        data_vars=data_vars,
+        attrs={
+            "title": "Mock ERA5 Dataset",
+            "creation_date": str(datetime.now()),
+            "description": "Synthetic ERA5 data created for testing purposes",
+        },
+    )
+
+    # Set encoding for efficient storage
+    encoding = {
+        var: {"compressor": zarr.Blosc(cname="zstd", clevel=3)} for var in ds.variables
+    }
+
+    # Ensure directory exists
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+    # Write to zarr
+    print(f"Writing dataset to Zarr store at {output_path}...")
+    ds.to_zarr(output_path, encoding=encoding, consolidated=True)
+
+    print(f"Mock ERA5 Zarr dataset created successfully at {output_path}")
+    return output_path
+
+
+if __name__ == "__main__":
+    zarr_path = "./data"
+    create_mock_era5_zarr(
+        output_path=zarr_path,
+        n_samples=10,
+        start_date="2020-01-01",
+        time_step_hours=6,
+        include_constant_masks=True,
+    )
diff --git a/weather-forecast/nwp-emulation/panguweather/ci/run.sh b/weather-forecast/nwp-emulation/panguweather/ci/run.sh
new file mode 100644
index 00000000..8d217529
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/ci/run.sh
@@ -0,0 +1,30 @@
+#!/usr/bin/bash
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+RUNNER="python3"
+
+while [[ $# -gt 0 ]]; do
+  case $1 in
+    -r|--runner)
+      RUNNER="$2"
+      shift # past argument
+      shift # past value
+      ;;
+    -*|--*)
+      echo "Unknown option $1"
+      exit 1
+      ;;
+  esac
+done
+
+${RUNNER} main.py -c ./ci/configs/pangu_test.yaml test || exit 1
\ No newline at end of file
diff --git a/weather-forecast/nwp-emulation/panguweather/configs/config_pangu.yaml b/weather-forecast/nwp-emulation/panguweather/configs/config_pangu.yaml
new file mode 100644
index 00000000..9e303882
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/configs/config_pangu.yaml
@@ -0,0 +1,80 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+fit:
+    seed_everything: 1978
+
+    model:
+        class_path: transformer_module.PanguModule
+        init_args:
+            plevel_patch_size: [2, 4, 4]
+            checkpoint_activation: True
+            mean_file: ./data/era5_mean_from_graphcast_to_pangu.nc
+            stddev_file: ./data/era5_stddev_from_graphcast_to_pangu.nc
+            lr: 0.0005
+
+    data:
+        class_path: data.ERA5DataModule
+        init_args:
+            zarr_store: ./data/era5-2007-2021-13-24h-0p25deg-chunk-1.zarr-v2
+            train_time: slice('2007-01-01', '2017-12-31')
+            val_time: slice('2019-01-01', '2019-12-31')
+            test_time: ['2018', '2020', '2021']
+            train_time_step: 24
+            test_time_step: 72
+            constant_mask_list: ['land_sea_mask', 'soil_type', 'geopotential_at_surface']
+            batch_size: 1
+            num_workers: 4
+            
+    
+    trainer:
+        max_epochs: 100
+        accelerator: "gpu"
+        devices: 4
+        accumulate_grad_batches: 48
+        strategy: "ddp"
+        log_every_n_steps: 1
+        callbacks:
+            - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+              init_args: 
+                logging_interval: epoch
+                log_weight_decay: True
+                
+test:
+    model:
+        class_path: transformer_module.PanguModule
+        init_args:
+            plevel_patch_size: [2, 4, 4]
+            checkpoint_activation: False
+            mean_file: ./data/era5_mean_from_graphcast_to_pangu.nc
+            stddev_file: ./data/era5_stddev_from_graphcast_to_pangu.nc
+
+    data:
+        class_path: data.ERA5DataModule
+        init_args:
+            zarr_store: ./data/era5-2007-2021-13-24h-0p25deg-chunk-1.zarr-v2
+            train_time: slice('2007-01-01', '2017-12-31')
+            val_time: slice('2019-01-01', '2019-12-31')
+            test_time: ['2018', '2020', '2021']
+            train_time_step: 24
+            test_time_step: 72
+            constant_mask_list: ['land_sea_mask', 'soil_type', 'geopotential_at_surface']
+            batch_size: 16
+            num_workers: 16
+            
+    
+    trainer:
+        max_epochs: 1
+        accelerator: "gpu"
+        devices: 1
+        
diff --git a/weather-forecast/nwp-emulation/panguweather/configs/config_pangu_lite.yaml b/weather-forecast/nwp-emulation/panguweather/configs/config_pangu_lite.yaml
new file mode 100644
index 00000000..4fbca6e1
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/configs/config_pangu_lite.yaml
@@ -0,0 +1,98 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+fit:
+    seed_everything: 1978
+
+    model:
+        class_path: transformer_module.PanguModule
+        init_args:
+            plevel_patch_size: [2, 8, 8]
+            checkpoint_activation: False
+            mean_file: ./data/era5_mean_from_graphcast_to_pangu.nc
+            stddev_file: ./data/era5_stddev_from_graphcast_to_pangu.nc
+            lr: 0.001
+            lam: False
+            norm_cst_mask: True
+            use_scheduler: True
+            add_const2: True
+
+
+    data:
+        class_path: data.ERA5DataModule
+        init_args:
+            zarr_store: ./data/era5-2007-2021-13-24h-0p25deg-chunk-1.zarr-v2
+            train_time: slice('2007-01-01', '2017-12-31')
+            val_time: slice('2019-01-01', '2019-12-31')
+            test_time: ['2018', '2020', '2021']
+            train_time_step: 24
+            test_time_step: 72
+            constant_mask_list: ['land_sea_mask', 'soil_type', 'geopotential_at_surface']
+            batch_size: 2
+            num_workers: 8            
+    
+    trainer:
+        max_epochs: 100
+        accelerator: "gpu"
+        devices: 4
+        strategy: "ddp"
+        log_every_n_steps: 50
+        callbacks:
+            - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+              init_args: 
+                logging_interval: epoch
+                log_weight_decay: True
+        logger:
+            class_path: lightning.pytorch.loggers.TensorBoardLogger
+            init_args:
+                save_dir: ./data/lightning_logs
+                name: pangu_lite_bs8_const2_lrscheduler_lr1e-3
+                version: 0
+                    
+test:
+    model:
+        class_path: transformer_module.PanguModule
+        init_args:
+            plevel_patch_size: [2, 8, 8]
+            checkpoint_activation: False
+            mean_file: ./data/era5_mean_from_graphcast_to_pangu.nc
+            stddev_file: ./data/era5_stddev_from_graphcast_to_pangu.nc
+            lam: False
+            norm_cst_mask: False
+            use_scheduler: True
+            add_const2: True
+
+    data:
+        class_path: data.ERA5DataModule
+        init_args:
+            zarr_store: ./data/era5-2007-2021-13-24h-0p25deg-chunk-1.zarr-v2
+            train_time: slice('2007-01-01', '2017-12-31')
+            val_time: slice('2019-01-01', '2019-12-31')
+            test_time: ['2018', '2020', '2021']
+            train_time_step: 24
+            test_time_step: 72
+            constant_mask_list: ['land_sea_mask', 'soil_type', 'geopotential_at_surface']
+            batch_size: 4
+            num_workers: 16            
+    
+    trainer:
+        max_epochs: 1
+        accelerator: "gpu"
+        devices: 1
+        logger:
+            class_path: lightning.pytorch.loggers.TensorBoardLogger
+            init_args:
+                save_dir: ./data/lightning_logs
+                name: pangu_lite_bs8_const2_lrscheduler_nonormcst_test_72h
+                version: 0
+        
diff --git a/weather-forecast/nwp-emulation/panguweather/configs/config_pangu_lite_mlflow.yaml b/weather-forecast/nwp-emulation/panguweather/configs/config_pangu_lite_mlflow.yaml
new file mode 100644
index 00000000..5b0155ce
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/configs/config_pangu_lite_mlflow.yaml
@@ -0,0 +1,112 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+fit:
+    seed_everything: 1978
+
+    model:
+        class_path: transformer_module.PanguModule
+        init_args:
+            plevel_patch_size: [2, 8, 8]
+            checkpoint_activation: False
+            mean_file: ${oc.decode:${oc.env:STAT_DIR}}/era5_mean_from_graphcast_to_pangu.nc
+            stddev_file: ${oc.decode:${oc.env:STAT_DIR}}/era5_stddev_from_graphcast_to_pangu.nc
+            lr: 0.001
+            # plevel_variable_weights: [0.2, 0.2, 0.2, 0.2, 0.2]
+            # surface_variable_weights: [0.25, 0.25, 0.25, 0.25]
+            lam: False
+            norm_cst_mask: True
+            use_scheduler: True
+            add_const2: True
+
+
+    data:
+        class_path: data.ERA5DataModule
+        init_args:
+            zarr_store: ${oc.decode:${oc.env:ZARR_STORE}}
+            train_time: slice('2007-01-01', '2007-01-03')
+            val_time: slice('2019-01-01', '2019-01-02')
+            test_time: ['2018', '2020', '2021']
+            train_time_step: 24
+            test_time_step: 72
+            constant_mask_list: ['land_sea_mask', 'soil_type', 'geopotential_at_surface']
+            # constant_mask_list: null
+            batch_size: 1
+            num_workers: 8            
+    
+    trainer:
+        max_epochs: 1
+        accelerator: "gpu"
+        devices: 4
+        # To match paper training settings
+        # accumulate_grad_batches: 12
+        strategy: "ddp"
+        # profiler: "pytorch"
+        log_every_n_steps: 50
+        callbacks:
+            - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+              init_args: 
+                logging_interval: epoch
+                log_weight_decay: True
+        logger:             
+            - class_path: lightning.pytorch.loggers.MLFlowLogger
+              init_args:
+                    save_dir: ${oc.decode:${oc.env:LOGDIR}}/mlflow_logs
+                    experiment_name: ai4sim-pangu
+                    log_model: True
+            # - class_path: lightning.pytorch.loggers.TensorBoardLogger
+            #   init_args:
+            #         save_dir: ${oc.decode:${oc.env:LOGDIR}}
+            #         name: pangu_lite_bs8_const2_lrscheduler_lr1e-3        
+
+
+test:
+    model:
+        class_path: transformer_module.PanguModule
+        init_args:
+            plevel_patch_size: [2, 8, 8]
+            checkpoint_activation: False
+            mean_file: ${oc.decode:${oc.env:STAT_DIR}}/era5_mean_from_graphcast_to_pangu.nc
+            stddev_file: ${oc.decode:${oc.env:STAT_DIR}}/era5_stddev_from_graphcast_to_pangu.nc
+            # plevel_variable_weights: [0.2, 0.2, 0.2, 0.2, 0.2]
+            # surface_variable_weights: [0.25, 0.25, 0.25, 0.25]
+            lam: False
+            norm_cst_mask: False
+            use_scheduler: True
+            add_const2: True
+
+    data:
+        class_path: data.ERA5DataModule
+        init_args:
+            zarr_store: ${oc.decode:${oc.env:ZARR_STORE}}
+            train_time: slice('2007-01-01', '2017-12-31')
+            val_time: slice('2019-01-01', '2019-12-31')
+            test_time: ['2018', '2020', '2021']
+            train_time_step: 24
+            test_time_step: 72
+            constant_mask_list: ['land_sea_mask', 'soil_type', 'geopotential_at_surface']
+            # constant_mask_list: null
+            batch_size: 4
+            num_workers: 16            
+    
+    trainer:
+        max_epochs: 1
+        accelerator: "gpu"
+        devices: 1
+        logger:
+            - class_path: lightning.pytorch.loggers.MLFlowLogger
+              init_args:
+                    save_dir: ${oc.decode:${oc.env:LOGDIR}}/mlflow_logs
+                    experiment_name: ai4sim-pangu
+                    log_model: True
+        
diff --git a/weather-forecast/nwp-emulation/panguweather/const2.npy b/weather-forecast/nwp-emulation/panguweather/const2.npy
new file mode 100644
index 00000000..67d0f41b
Binary files /dev/null and b/weather-forecast/nwp-emulation/panguweather/const2.npy differ
diff --git a/weather-forecast/nwp-emulation/panguweather/data.py b/weather-forecast/nwp-emulation/panguweather/data.py
new file mode 100644
index 00000000..9b4bfdd4
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/data.py
@@ -0,0 +1,470 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import Any, Dict, List, Union
+
+import numpy as np
+import torch
+import xarray
+from lightning import LightningDataModule
+from torch import Tensor
+from torch.utils.data import ConcatDataset, DataLoader, Dataset
+
+AVAILABLE_ERA5_MASK = (
+    "land_sea_mask",
+    "soil_type",
+    "geopotential_at_surface",
+    "low_vegetation_cover",
+    "high_vegetation_cover",
+    "lake_cover",
+    "lake_depth",
+    "type_of_high_vegetation",
+    "type_of_low_vegetation",
+)
+
+PLEVEL_VARIABLES = [
+    "geopotential",
+    "specific_humidity",
+    "temperature",
+    "u_component_of_wind",
+    "v_component_of_wind",
+]
+SURFACE_VARIABLES = [
+    "mean_sea_level_pressure",
+    "10m_u_component_of_wind",
+    "10m_v_component_of_wind",
+    "2m_temperature",
+]
+PLEVEL_LIST = [50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000]
+
+
+class ERA5DataModule(LightningDataModule):
+    """Data module to load ERA5 cartesian data. See `ERA5CartesianZarr` for a
+    description of the arguments.
+    """
+
+    def __init__(
+        self,
+        zarr_store: str,
+        train_time: Union[slice, List, str] = slice("1979", "2017"),
+        val_time: Union[slice, List, str] = slice("2019"),
+        test_time: Union[slice, List[str], str] = ["2018", "2020", "2021"],
+        train_time_step: int = 6,
+        test_time_step: int = 6,
+        surface_variables: List[str] = SURFACE_VARIABLES,
+        plevel_variables: List[str] = PLEVEL_VARIABLES,
+        plevels: List[int] = PLEVEL_LIST,
+        constant_mask_list: Union[List[str], None] = [
+            "land_sea_mask",
+            "soil_type",
+            "geopotential_at_surface",
+        ],
+        chunks: Union[dict, str] = "auto",
+        batch_size: int = 16,
+        num_workers: int = 16,
+    ) -> None:
+        super().__init__()
+        self.zarr_store = zarr_store
+        self.train_time = train_time
+        self.val_time = val_time
+        self.test_time = test_time
+        self.train_time_step = train_time_step
+        self.test_time_step = test_time_step
+        self.surface_variables = surface_variables
+        self.plevel_variables = plevel_variables
+        self.plevels = plevels
+        self.constant_mask_list = constant_mask_list
+        self.chunks = chunks
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.train_dataset: Dataset
+        self.val_dataset: Dataset
+        self.test_dataset: Dataset
+
+        data = xarray.open_dataset(self.zarr_store, engine="zarr", chunks=chunks)
+        self.data_latitude = data.latitude.size
+        self.data_longitude = data.longitude.size
+        self.constant_masks = load_era5_constant_mask_from_zarr(
+            self.zarr_store, mask_list=self.constant_mask_list
+        )
+
+    def setup(self, stage: str) -> None:
+        if stage == "fit":
+            self.train_dataset = prepare_zarr_dataset(
+                zarr_store=self.zarr_store,
+                time=self.train_time,
+                time_step=self.train_time_step,
+                surface_variables=self.surface_variables,
+                plevel_variables=self.plevel_variables,
+                plevels=self.plevels,
+                chunks=self.chunks,
+            )
+            self.val_dataset = prepare_zarr_dataset(
+                zarr_store=self.zarr_store,
+                time=self.val_time,
+                time_step=self.train_time_step,
+                surface_variables=self.surface_variables,
+                plevel_variables=self.plevel_variables,
+                plevels=self.plevels,
+                chunks=self.chunks,
+            )
+        if stage == "test":
+            self.test_dataset = prepare_zarr_dataset(
+                zarr_store=self.zarr_store,
+                time=self.test_time,
+                time_step=self.test_time_step,
+                surface_variables=self.surface_variables,
+                plevel_variables=self.plevel_variables,
+                plevels=self.plevels,
+                chunks=self.chunks,
+            )
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+            multiprocessing_context="spawn",
+            persistent_workers=True,
+        )
+
+    def val_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            multiprocessing_context="spawn",
+            persistent_workers=True,
+        )
+
+    def test_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            multiprocessing_context="spawn",
+        )
+
+
+class ERA5CartesianZarr(Dataset):
+    """Dataset to load ERA5 data in the zarr format, WeatherBench2 version.
+    The zarr file contains all the data points, that can be loaded by chunk.
+    The pressure levels have already been filtered to the 13 used by
+    PanguWeather. The time is defined hourly.
+    13 pressure levels: 50hPa, 100hPa, 150hPa, 200hPa, 250hPa, 300hPa, 400hPa,
+    500hPa, 600hPa, 700hPa, 850hPa, 925hPa, and 1000hPa
+    five upper-air atmospheric variables: geopotential, specific humidity,
+    temperature, ucomponent and v-component of wind speed
+    four surface weather variables: 2m temperature, u-component and
+    v-component 10m wind speed, and mean sea level pressure
+
+            Args:
+                zarr_store (str): file containing pressure level data
+                time (slice[str]], optional): time to extract from the
+                original dataset. Defaults to slice('1979', '2017') for
+                PanguWeather training.
+                time_step (int, optional): time step in hours between input
+                and label. Defaults to 6.
+                surface_variables (list[str], optional): list of surface
+                variables to select. Defaults to SURFACE_VARIABLES.
+                plevel_variables (list[str], optional): list of plevel
+                variables to select. Defaults to PLEVEL_VARIABLES.
+                plevels (list[int], optional): list of pressure levels to
+                select. Defaults to None, keeping all the 13 pressure levels.
+                chunk (_type_, optional):  Chunk of data to load. Defaults to
+                {'time': 2}.
+    """
+
+    def __init__(
+        self,
+        zarr_store: str,
+        time: str | slice = slice("1979", "2017"),
+        time_step: int = 6,
+        surface_variables: List[str] = SURFACE_VARIABLES,
+        plevel_variables: List[str] = PLEVEL_VARIABLES,
+        plevels: List[int] = PLEVEL_LIST,
+        chunks: Union[dict, str] = "auto",
+    ) -> None:
+
+        super().__init__()
+        data = xarray.open_dataset(zarr_store, engine="zarr", chunks=chunks)
+        data = data.sel(level=plevels)
+        self.dataset = data.sel(time=time)
+        self.time_step = np.timedelta64(time_step, "h")
+        self.times = self.dataset.time.to_numpy().astype("datetime64[s]")
+        self.data_time_span = self.times[1] - self.times[0]
+        assert (
+            self.time_step >= self.data_time_span
+        ), f"time_step must at least equal to the time step in the data but \
+        time_step={time_step} and data time step={self.data_time_span}"
+        self.surface_variables = sorted(surface_variables)
+        self.plevel_variables = sorted(plevel_variables)
+        self.plevels = sorted(plevels)
+
+    def __len__(self) -> int:
+        offset = self.time_step // self.data_time_span
+        return len(self.dataset.time) - offset
+
+    def __getitem__(self, idx: int) -> dict[str, Tensor]:
+        data_time = self.times[idx]
+        label_time = data_time + self.time_step
+        data_point = self.dataset.sel(time=data_time)
+        label_point = self.dataset.sel(time=label_time)
+        plevel_data_list = []
+        plevel_labels_list = []
+        surface_data_list = []
+        surface_labels_list = []
+        for plevel_variable in self.plevel_variables:
+            plevel_data_list.append(
+                torch.from_numpy(data_point[plevel_variable].to_numpy())
+            )
+            plevel_labels_list.append(
+                torch.from_numpy(label_point[plevel_variable].to_numpy())
+            )
+        for surface_variable in self.surface_variables:
+            surface_data_list.append(
+                torch.from_numpy(data_point[surface_variable].to_numpy())
+            )
+            surface_labels_list.append(
+                torch.from_numpy(label_point[surface_variable].to_numpy())
+            )
+        plevel_data = torch.stack(plevel_data_list)
+        plevel_labels = torch.stack(plevel_labels_list)
+        surface_data = torch.stack(surface_data_list)
+        surface_labels = torch.stack(surface_labels_list)
+
+        return {
+            "plevel_data": plevel_data,
+            "surface_data": surface_data,
+            "data_time": data_time.astype(np.float32),
+            "plevel_labels": plevel_labels,
+            "surface_labels": surface_labels,
+            "label_time": label_time.astype(np.float32),
+            "longitudes": data_point.longitude.to_numpy(),
+            "latitudes": data_point.latitude.to_numpy(),
+        }
+
+
+class MockERA5DataModule(LightningDataModule):
+    """Data module to load ERA5 cartesian data. See `ERA5CartesianZarr` for a
+    description of the arguments.
+    """
+
+    def __init__(
+        self,
+        n_samples: int = 100,
+        batch_size: int = 16,
+        num_workers: int = 16,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__()
+        self.n_samples = n_samples
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.train_dataset: Dataset
+        self.val_dataset: Dataset
+        self.test_dataset: Dataset
+
+        self.prepare_data()
+
+    def prepare_data(self) -> None:
+        self.train_dataset = MockERA5Cartesian(n_samples=self.n_samples)
+        self.val_dataset = MockERA5Cartesian(n_samples=self.n_samples)
+        self.test_dataset = MockERA5Cartesian(n_samples=self.n_samples)
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+        )
+
+    def val_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+        )
+
+    def test_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+        )
+
+
+class MockERA5Cartesian(Dataset):
+    """Mock ERA5 dataset for testing purpose.
+
+    Args:
+        n_samples (int, optional): number of samples.
+    """
+
+    def __init__(
+        self,
+        n_samples: int = 100,
+    ) -> None:
+
+        super().__init__()
+        self.n_samples = n_samples
+
+    def __len__(self) -> int:
+        return self.n_samples
+
+    def __getitem__(self, idx: int) -> dict[str, Tensor]:
+        return {
+            "plevel_data": torch.rand(5, 13, 721, 1440),
+            "surface_data": torch.rand(4, 721, 1440),
+            "data_time": torch.rand(1),
+            "plevel_labels": torch.rand(5, 13, 721, 1440),
+            "surface_labels": torch.rand(4, 721, 1440),
+            "label_time": torch.rand(1),
+            "longitudes": torch.rand(1440),
+            "latitudes": torch.rand(721),
+        }
+
+
+def load_era5_constant_mask_from_necdf(
+    data_path: str, mask_list: list[str] | None = None
+) -> dict[str, dict] | None:
+    """Load constant masks: e. g., topography, land-sea and soil type. Each
+    mask is in a dedicated netcdf file.
+
+    Args:
+        data_path (str): constant mask file location
+        mask_list (list[str], optional): List of the required constant mask.
+        Defaults to None.
+
+    Returns:
+        dict[xarray.Dataset]: list of constant data
+    """
+    # Check that constant masks in the list are known
+    if mask_list is not None:
+        mask_list = check_era5_constant_masks(mask_list=mask_list)
+        mask_list.sort()
+        constant_masks = dict()
+        for mask in mask_list:
+            mask_file = os.path.join(data_path, "constant_masks", mask + ".nc")
+            mask_data = xarray.load_dataset(mask_file)
+            assert (
+                len(list(mask_data.keys())) == 1
+            ), "Can't handle mask file containing several masks..."
+            key = list(mask_data.keys())[0]
+            constant_masks[mask] = {
+                "longitude": mask_data.longitude,
+                "latitude": mask_data.latitude,
+                "mask": mask_data[key].to_numpy(),
+            }
+        return constant_masks
+    else:
+        return None
+
+
+def load_era5_constant_mask_from_zarr(
+    zarr_store: str, mask_list: list[str] | None = None
+) -> dict[str, dict] | None:
+    """Load constant masks: e. g., topography, land-sea and soil type
+
+    Args:
+        zarr_store (str): zarr store containing the constant masks
+        mask_list (list[str], optional): List of the required constant mask.
+        Defaults to None.
+
+    Returns:
+        dict[str, dict]: list of constant data
+    """
+    # open zarr storage
+    data = xarray.open_zarr(zarr_store)
+    # Check that constant masks in the list are known
+    if mask_list is not None:
+        mask_list = check_era5_constant_masks(mask_list=mask_list)
+        mask_list.sort()
+        constant_masks = dict()
+        for mask in mask_list:
+            mask_data = data[mask]
+            constant_masks[mask] = {
+                "longitude": data.longitude.to_numpy(),
+                "latitude": data.latitude.to_numpy(),
+                "mask": mask_data.to_numpy(),
+            }
+
+        return constant_masks
+    else:
+        return None
+
+
+def check_era5_constant_masks(mask_list: list) -> list:
+    """Check that constant masks in the list exist. If not, withdraw from the
+    list.
+
+    Args:
+        mask_list (list): constant masks required
+
+    Returns:
+        list: curated list of constant masks
+    """
+    curated_list = []
+    for mask in mask_list:
+        if mask in AVAILABLE_ERA5_MASK:
+            curated_list.append(mask)
+        else:
+            print(
+                f"Constant mask {mask} doesn't exist in the data, it will be discarded"
+            )
+    return curated_list
+
+
+def prepare_zarr_dataset(
+    zarr_store: str,
+    time: Union[slice, str, List[str]],
+    time_step: int,
+    surface_variables: List[str],
+    plevel_variables: List[str],
+    plevels: List[int],
+    chunks: Union[dict, str],
+) -> Dataset[Dict[str, Tensor]]:
+    assert isinstance(
+        time, (slice, str, list)
+    ), f"time argument type must be in [slice, str, list], but is {type(time)}"
+    if isinstance(time, list):
+        return ConcatDataset(
+            [
+                ERA5CartesianZarr(
+                    zarr_store=zarr_store,
+                    time=t,
+                    time_step=time_step,
+                    surface_variables=surface_variables,
+                    plevel_variables=plevel_variables,
+                    plevels=plevels,
+                    chunks=chunks,
+                )
+                for t in time
+            ]
+        )
+    else:
+        return ERA5CartesianZarr(
+            zarr_store=zarr_store,
+            time=time,
+            time_step=time_step,
+            surface_variables=surface_variables,
+            plevel_variables=plevel_variables,
+            plevels=plevels,
+            chunks=chunks,
+        )
diff --git a/weather-forecast/nwp-emulation/panguweather/env.yaml b/weather-forecast/nwp-emulation/panguweather/env.yaml
new file mode 100644
index 00000000..8bce1bdc
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/env.yaml
@@ -0,0 +1,6 @@
+# This file defines the global environment dependencies for this model project.
+# It is read by the CI/CD workflows in charge of the tests (ai4sim-ci-cd.yaml)
+# and the build of the Docker image (images-ci-cd.yaml) to set the environment.
+ubuntu_base_image:
+  name: nvidia/cuda
+  tag: 12.4.1-cudnn-runtime-ubuntu22.04
\ No newline at end of file
diff --git a/weather-forecast/nwp-emulation/panguweather/main.py b/weather-forecast/nwp-emulation/panguweather/main.py
new file mode 100644
index 00000000..f444cf1d
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/main.py
@@ -0,0 +1,100 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import Any
+
+import torch
+from jsonargparse.typing import register_type
+from lightning import Trainer
+from lightning.pytorch.cli import LightningArgumentParser, LightningCLI
+from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
+
+from callback import GlobalSaveConfigCallback
+from utils import slice_deserializer
+
+torch.set_float32_matmul_precision("high")
+
+
+class CustomTrainer(Trainer):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+    @property
+    def log_dir(self) -> str:
+        """This function patch the default behaviour of the pytorch trainer.py
+        It gets the directory for the current experiment associated with the
+        logger.
+
+        Returns:
+          dirpath : the path where the logs are stored.
+        """
+        if len(self.loggers) > 0:
+            dirpath = os.getenv("LOGDIR")
+
+            for logger in self.loggers:
+                if isinstance(logger, (TensorBoardLogger, CSVLogger)):
+                    dirpath = logger.log_dir
+                    break
+        else:
+            dirpath = self.default_root_dir
+
+        dirpath = self.strategy.broadcast(dirpath)
+        assert dirpath is not None
+        # needed as mypy considers os.getenv() to return a Optional[str],
+        # and log_dir needs to return a str
+        return dirpath
+
+
+class MyLightningCLI(LightningCLI):
+    def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
+        parser.link_arguments(
+            "data.data_latitude", "model.init_args.latitude", apply_on="instantiate"
+        )
+        parser.link_arguments(
+            "data.data_longitude", "model.init_args.longitude", apply_on="instantiate"
+        )
+        parser.link_arguments(
+            "data.surface_variables",
+            "model.init_args.surface_variables",
+            apply_on="instantiate",
+        )
+        parser.link_arguments(
+            "data.plevel_variables",
+            "model.init_args.plevel_variables",
+            apply_on="instantiate",
+        )
+        parser.link_arguments(
+            "data.plevels", "model.init_args.plevels", apply_on="instantiate"
+        )
+        parser.link_arguments(
+            "data.constant_masks",
+            "model.init_args.constant_masks",
+            apply_on="instantiate",
+        )
+        parser.link_arguments(
+            "data.train_time_step", "model.init_args.time_step", apply_on="instantiate"
+        )
+
+
+def cli_main() -> None:
+    MyLightningCLI(
+        save_config_kwargs={"overwrite": True},
+        save_config_callback=GlobalSaveConfigCallback,
+        trainer_class=CustomTrainer,
+        parser_kwargs={"parser_mode": "omegaconf"},
+    )
+
+
+if __name__ == "__main__":
+    register_type(slice, str, slice_deserializer)
+    cli_main()
diff --git a/weather-forecast/nwp-emulation/panguweather/metrics.py b/weather-forecast/nwp-emulation/panguweather/metrics.py
new file mode 100644
index 00000000..197a1a1e
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/metrics.py
@@ -0,0 +1,109 @@
+# Copyright The Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any
+
+import torch
+from torch import Tensor, tensor
+from torchmetrics.metric import Metric
+
+
+class LatitudeWeightedMeanSquaredError(Metric):
+    r"""Compute `mean squared error`_ (MSE).
+
+    .. math:: \text{MSE} = \frac{1}{N}\sum_i^N(y_i - \hat{y_i})^2
+
+    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
+
+    As input to ``forward`` and ``update`` the metric accepts the following input:
+
+    - ``preds`` (:class:`~torch.Tensor`): Predictions from model
+    - ``target`` (:class:`~torch.Tensor`): Ground truth values
+
+    As output of ``forward`` and ``compute`` the metric returns the following output:
+
+    - ``mean_squared_error`` (:class:`~torch.Tensor`): A tensor with the mean squared error
+
+    Args:
+        squared: If True returns MSE value, if False returns RMSE value.
+        num_outputs: Number of outputs in multioutput setting
+        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
+
+    Example::
+        Single output mse computation:
+
+        >>> from torch import tensor
+        >>> from torchmetrics.regression import MeanSquaredError
+        >>> target = tensor([2.5, 5.0, 4.0, 8.0])
+        >>> preds = tensor([3.0, 5.0, 2.5, 7.0])
+        >>> mean_squared_error = MeanSquaredError()
+        >>> mean_squared_error(preds, target)
+        tensor(0.8750)
+
+    Example::
+        Multioutput mse computation:
+
+        >>> from torch import tensor
+        >>> from torchmetrics.regression import MeanSquaredError
+        >>> target = tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
+        >>> preds = tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
+        >>> mean_squared_error = MeanSquaredError(num_outputs=3)
+        >>> mean_squared_error(preds, target)
+        tensor([1., 4., 9.])
+
+    """
+
+    is_differentiable = True
+    higher_is_better = False
+    full_state_update = False
+
+    sum_squared_error: Tensor
+    total: Tensor
+
+    def __init__(
+        self,
+        squared: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+
+        if not isinstance(squared, bool):
+            raise ValueError(
+                f"Expected argument `squared` to be a boolean but got {squared}"
+            )
+        self.squared = squared
+
+        self.add_state("sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
+
+    def update(self, preds: Tensor, target: Tensor, latitudes: Tensor) -> None:
+        """Update state with predictions and targets."""
+        latitudes = latitudes[0] if latitudes.ndim > 1 else latitudes
+        lat_weights = latitudes.shape[0] * (
+            torch.cos(torch.pi * latitudes / 180)
+            / torch.cos(torch.pi * latitudes / 180).sum()
+        )
+        # preds and targets are of shape B, lat, long
+        squared_error = (preds - target) ** 2
+        weighted_squared_error = squared_error * lat_weights.unsqueeze(0).unsqueeze(-1)
+        swse = weighted_squared_error.mean(dim=(1, 2))
+        self.sum_squared_error += swse.sum()
+        self.total += len(preds)
+
+    def compute(self) -> Tensor:
+        """Compute mean squared error over state."""
+        return (
+            self.sum_squared_error / self.total
+            if self.squared
+            else torch.sqrt(self.sum_squared_error / self.total)
+        )
diff --git a/weather-forecast/nwp-emulation/panguweather/mypy.ini b/weather-forecast/nwp-emulation/panguweather/mypy.ini
new file mode 100644
index 00000000..b89e2cc7
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/mypy.ini
@@ -0,0 +1,8 @@
+[mypy]
+
+mypy_path = weather-forecast/nwp-emulation/panguweather
+
+# Override mypy default value `ignore_missing_imports = false` to `true` for modules with no typing
+# Has the effect of silencing `import-untyped` error
+[mypy-zarr]
+ignore_missing_imports = true
\ No newline at end of file
diff --git a/weather-forecast/nwp-emulation/panguweather/noxfile.py b/weather-forecast/nwp-emulation/panguweather/noxfile.py
new file mode 100644
index 00000000..517a6f3c
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/noxfile.py
@@ -0,0 +1,35 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This module simply load all the nox targets defined in the reference noxfile
+and make them available for the model project.
+This file can be enriched by model project specific targets.
+"""
+
+import inspect
+import os
+import sys
+
+import nox
+
+# Insert the tools/nox folder to the python path to fetch the nox_ref_file.py content
+current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+common_dir = os.path.dirname(os.path.dirname(os.path.dirname(current_dir)))
+build_ref_dir = os.path.join(common_dir, "tools", "nox")
+sys.path.insert(0, build_ref_dir)
+
+# Fetch the nox_ref_file.py content
+from nox_ref_file import *
+
+# Insert below the model project specific targets
+nox.needs_version = "==2026.2.9"
diff --git a/weather-forecast/nwp-emulation/panguweather/pangu.py b/weather-forecast/nwp-emulation/panguweather/pangu.py
new file mode 100644
index 00000000..b9456327
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/pangu.py
@@ -0,0 +1,992 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import pathlib
+from typing import Any
+
+import numpy as np
+import torch
+from timm.layers import DropPath
+from torch import Tensor, nn
+from torch.nn import (
+    GELU,
+    ConstantPad2d,
+    ConstantPad3d,
+    Conv2d,
+    Conv3d,
+    ConvTranspose2d,
+    ConvTranspose3d,
+    Dropout,
+    LayerNorm,
+    Linear,
+    Softmax,
+)
+from torch.utils.checkpoint import checkpoint
+
+from utils import (
+    cat_constant_masks,
+    define_3d_earth_position_index,
+    generate_3d_attention_mask,
+)
+
+
+class PanguModel(nn.Module):
+    """PanguWeather network as described in http://arxiv.org/abs/2211.02556
+    and https://www.nature.com/articles/s41586-023-06185-3.
+    This implementation follows the official pseudo code here:
+    https://github.com/198808xc/Pangu-Weather
+
+        Args:
+            plevel_patch_size (tuple[int]): Patch size for the pressure level
+            data. Default is (2, 4, 4). Setting (2, 8, 8) leads to Pangu Lite.
+            constant_mask_list (list[str]): list of the constant masks to add
+            to the surface data as additional channels (e. g., soil type).
+            token_size (int): Size of the tokens (equivalent to channel size)
+            of the first layer. Default is 192.
+            layer_depth (tuple[int, int]): Number of blocks in layers. Default
+            is (2, 6), meaning that the first and fourth layers contain 2
+            blocks, and the second and third contain 6.
+            num_heads (tuple[int, int]): Number of heads in attention layers.
+            Default is (6, 12), corresponding to respectively first and fourth
+            layers, and second and third.
+    """
+
+    def __init__(
+        self,
+        latitude: int,
+        longitude: int,
+        surface_variables: int,
+        plevel_variables: int,
+        plevels: int,
+        plevel_patch_size: tuple[int, int, int] = (2, 4, 4),
+        constant_masks: dict[str, dict] | None = None,
+        token_size: int = 192,
+        layer_depth: tuple[int, int] = (2, 6),
+        num_heads: tuple[int, int] = (6, 12),
+        add_const2: bool = False,
+        **kwargs: Any,
+    ) -> None:
+
+        super().__init__()
+
+        self.plevel_patch_size = plevel_patch_size
+
+        # load constant mask, e. g., soil type
+        if constant_masks is not None:
+            self.register_buffer(
+                "constant_masks",
+                torch.stack(
+                    [torch.from_numpy(m["mask"]) for m in constant_masks.values()]
+                ),
+            )
+            surface_channels = surface_variables + len(constant_masks)
+        else:
+            surface_channels = surface_variables
+            self.constant_masks = None
+
+        # Special plevel const from onnx version of pangu, level 0 == close to the ground,
+        # shape = 1, 1, 1, 13, 721, 1440
+        if add_const2:
+            self.register_buffer(
+                "const2",
+                torch.flip(
+                    torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                pathlib.Path(__file__).parent.resolve(), "const2.npy"
+                            )
+                        ).squeeze(0)
+                    ),
+                    dims=(2,),
+                ),
+            )
+            plevel_size = torch.Size(
+                (plevel_variables + 1, plevels, latitude, longitude)
+            )
+        else:
+            plevel_size = torch.Size((plevel_variables, plevels, latitude, longitude))
+
+        # Set data size. Needed to compute the size of earth specific bias
+        surface_size = torch.Size((surface_channels, latitude, longitude))
+
+        # Drop path rate is linearly increased as the depth increases
+        drop_path_list = torch.linspace(0, 0.2, 8)
+
+        # Patch embedding
+        self.embedding_layer = PatchEmbedding(
+            token_size,
+            self.plevel_patch_size,
+            plevel_size=plevel_size,
+            surface_size=surface_size,
+        )
+        embedding_size = self.embedding_layer.embedding_size
+
+        # Upsample and downsample
+        self.downsample = DownSample(embedding_size, token_size)
+        downsampled_size = self.downsample.downsampled_size
+        self.upsample = UpSample(token_size * 2, token_size)
+
+        # Four Earth specific layers
+        self.layer1 = EarthSpecificLayer(
+            layer_depth[0],
+            embedding_size,
+            token_size,
+            drop_path_list[:2],
+            num_heads[0],
+            **kwargs,
+        )
+        self.layer2 = EarthSpecificLayer(
+            layer_depth[1],
+            downsampled_size,
+            token_size * 2,
+            drop_path_list[2:],
+            num_heads[1],
+            **kwargs,
+        )
+        self.layer3 = EarthSpecificLayer(
+            layer_depth[1],
+            downsampled_size,
+            token_size * 2,
+            drop_path_list[2:],
+            num_heads[1],
+            **kwargs,
+        )
+        self.layer4 = EarthSpecificLayer(
+            layer_depth[0],
+            embedding_size,
+            token_size,
+            drop_path_list[:2],
+            num_heads[0],
+            **kwargs,
+        )
+
+        # Patch Recovery
+        self._output_layer = PatchRecovery(token_size * 2, self.plevel_patch_size)
+
+    def forward(
+        self, input_plevel: Tensor, input_surface: Tensor
+    ) -> tuple[Tensor, Tensor]:
+        # Add constant masks to surface data if any
+        if self.constant_masks is not None:
+            surface_data = cat_constant_masks(input_surface, self.constant_masks)
+        else:
+            surface_data = input_surface
+
+        # Concat the special const2
+        if hasattr(self, "const2"):
+            assert isinstance(self.const2, Tensor), "const2 is not a tensor"
+            input_plevel = torch.cat(
+                [
+                    input_plevel,
+                    self.const2.expand(
+                        (input_plevel.shape[0],) + tuple(self.const2.shape[1:])
+                    ),
+                ],
+                dim=1,
+            )
+
+        # Embed the input fields into patches
+        x, embedding_shape = self.embedding_layer(input_plevel, surface_data)
+
+        # Encoder, composed of two layers
+        x = self.layer1(x, embedding_shape)
+
+        # Store the tensor for skip-connection
+        skip = x
+
+        # Downsample from (8, 181, 360) to (8, 91, 180)
+        x, downsampled_shape = self.downsample(x, embedding_shape)
+
+        # Layer 2, shape (8, 91, 180, 2C), C = 192 as in the original paper
+        x = self.layer2(x, downsampled_shape)
+
+        # Decoder, composed of two layers
+        # Layer 3, shape (8, 91, 180, 2C), C = 192 as in the original paper
+        x = self.layer3(x, downsampled_shape)
+
+        # Upsample from (8, 91, 180) to (8, 181, 360)
+        x = self.upsample(x, embedding_shape)
+
+        # Layer 4, shape (8, 181, 360, 2C), C = 192 as in the original paper
+        x = self.layer4(x, embedding_shape)
+
+        # Skip connect, in last dimension(C from 192 to 384)
+        x = torch.cat([x, skip], dim=-1)
+
+        # Recover the output fields from patches
+        output_plevel, output_surface = self._output_layer(x, embedding_shape)
+
+        # Crop the output to remove zero-paddings
+        padded_z, padded_h, padded_w = output_plevel.shape[2:5]
+        (
+            padding_left,
+            padding_right,
+            padding_top,
+            padding_bottom,
+            padding_front,
+            padding_back,
+        ) = self.embedding_layer.pad_plevel_data.padding
+        output_plevel = output_plevel[
+            :,
+            :,
+            padding_front : padded_z - padding_back,
+            padding_top : padded_h - padding_bottom,
+            padding_left : padded_w - padding_right,
+        ]
+        output_surface = output_surface[
+            :,
+            :,
+            padding_top : padded_h - padding_bottom,
+            padding_left : padded_w - padding_right,
+        ]
+
+        return output_plevel, output_surface
+
+
+class CustomPad3d(ConstantPad3d):
+    """Custom 3d padding based on token embedding patch size. Padding
+    direction is center.
+
+    Args:
+        data_size (torch.Size): data size
+        patch_size (tuple[int]): patch size for the token embedding operation
+        value (float, optional): padding value. Defaults to 0.
+    """
+
+    def __init__(
+        self,
+        data_size: torch.Size,
+        patch_size: tuple[int, int, int],
+        value: float = 0.0,
+    ) -> None:
+        # Compute paddings, starts from the last dim and goes backward
+        assert (
+            len(data_size) == 3
+        ), f"This padding class is for 3d data, but data has {len(data_size)} dimension(s)"
+        assert (
+            len(patch_size) == 3
+        ), f"Patch should be 3d, but has {len(patch_size)} dimension(s)"
+        padding_lon = (
+            patch_size[-1] - (data_size[-1] % patch_size[-1])
+            if (data_size[-1] % patch_size[-1]) > 0
+            else 0
+        )
+        padding_left = padding_lon // 2
+        padding_right = padding_lon - padding_left
+        padding_lat = (
+            patch_size[-2] - (data_size[-2] % patch_size[-2])
+            if (data_size[-2] % patch_size[-2]) > 0
+            else 0
+        )
+        padding_top = padding_lat // 2
+        padding_bottom = padding_lat - padding_top
+        padding_level = (
+            patch_size[-3] - (data_size[-3] % patch_size[-3])
+            if (data_size[-3] % patch_size[-3]) > 0
+            else 0
+        )
+        padding_front = padding_level // 2
+        padding_back = padding_level - padding_front
+        super().__init__(
+            padding=(
+                padding_left,
+                padding_right,
+                padding_top,
+                padding_bottom,
+                padding_front,
+                padding_back,
+            ),
+            value=value,
+        )
+        self.padded_size: torch.Size = torch.Size(
+            [
+                data_size[0] + padding_level,
+                data_size[1] + padding_lat,
+                data_size[2] + padding_lon,
+            ]
+        )
+
+
+class CustomPad2d(ConstantPad2d):
+    """Custom 2d padding based on token embedding patch size. Padding direction is center.
+
+    Args:
+        data_size (torch.Size): data size
+        patch_size (tuple[int]): patch size for the token embedding operation
+        value (float, optional): padding value. Defaults to 0.
+    """
+
+    def __init__(
+        self, data_size: torch.Size, patch_size: tuple[int, int], value: float = 0.0
+    ) -> None:
+        # Compute paddings, starts from the last dim and goes backward
+        assert (
+            len(data_size) == 2
+        ), f"This padding class is for 2d data, but data has {len(data_size)} dimension(s)"
+        assert (
+            len(patch_size) == 2
+        ), f"Patch should be 2d, but has {len(patch_size)} dimension(s)"
+        padding_lon = (
+            patch_size[-1] - (data_size[-1] % patch_size[-1])
+            if (data_size[-1] % patch_size[-1]) > 0
+            else 0
+        )
+        padding_left = padding_lon // 2
+        padding_right = padding_lon - padding_left
+        padding_lat = (
+            patch_size[-2] - (data_size[-2] % patch_size[-2])
+            if (data_size[-2] % patch_size[-2]) > 0
+            else 0
+        )
+        padding_top = padding_lat // 2
+        padding_bottom = padding_lat - padding_top
+        super().__init__(
+            padding=(padding_left, padding_right, padding_top, padding_bottom),
+            value=value,
+        )
+        self.padded_size = torch.Size(
+            [data_size[0] + padding_lat, data_size[1] + padding_lon]
+        )
+
+
+class PatchEmbedding(nn.Module):
+    """Patch embedding operation. Apply a linear projection for
+    patch_size[0]*patch_size[1]*patch_size[2] patches,
+    patch_size = (2, 4, 4) in the original paper
+
+    Args:
+        c_dim (int): embeeding channel size
+        patch_size (tuple[int, int, int]): patch size for pressure level data
+        plevel_size (torch.Size): pressure level data size
+        surface_size (torch.Size): surface data size
+    """
+
+    def __init__(
+        self,
+        c_dim: int,
+        patch_size: tuple[int, int, int],
+        plevel_size: torch.Size,
+        surface_size: torch.Size,
+    ) -> None:
+        super().__init__()
+
+        # Here we use convolution to partition data into cubes
+        self.conv_surface = Conv2d(
+            in_channels=surface_size[0],
+            out_channels=c_dim,
+            kernel_size=patch_size[1:],
+            stride=patch_size[1:],
+        )
+        self.conv_plevel = Conv3d(
+            in_channels=plevel_size[0],
+            out_channels=c_dim,
+            kernel_size=patch_size,
+            stride=patch_size,
+        )
+
+        # init padding
+        self.pad_plevel_data = CustomPad3d(plevel_size[-3:], patch_size)
+        self.pad_surface_data = CustomPad2d(surface_size[-2:], patch_size[1:])
+
+        # Compute output size
+        plevel_padded_size = self.pad_plevel_data.padded_size
+        embedding_size = [
+            plevel_dim // patch_dim
+            for plevel_dim, patch_dim in zip(plevel_padded_size, patch_size)
+        ]
+        embedding_size[0] += 1
+        self.embedding_size = torch.Size(embedding_size)
+
+    def forward(
+        self, input_plevel: Tensor, input_surface: Tensor
+    ) -> tuple[Tensor, torch.Size]:
+        # Zero-pad the input
+        plevel_data = self.pad_plevel_data(input_plevel)
+        surface_data = self.pad_surface_data(input_surface)
+
+        # Project to embedding space
+        plevel_tokens = self.conv_plevel(plevel_data)
+        surface_tokens = self.conv_surface(surface_data)
+
+        # Concatenate the input in the pressure level, i.e., in Z dimension
+        x = torch.cat([plevel_tokens, surface_tokens.unsqueeze(2)], dim=2)
+
+        # Reshape x for calculation of linear projections
+        x = x.permute((0, 2, 3, 4, 1))
+        embedding_shape = x.shape
+        x = x.reshape(shape=(x.shape[0], -1, x.shape[-1]))
+
+        return x, embedding_shape
+
+
+class PatchRecovery(nn.Module):
+    """Patch recovery operation. The inverse operation of the patch embedding
+    operation.
+
+    Args:
+        dim (int): number of channels
+        patch_size (tuple[int, int, int]): pressure level patch size, e. g., (2, 4, 4)
+        as in the original paper
+        plevel_channels (int, optional): pressure level data channel size
+        surface_channels (int, optional): surface data channel size
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        patch_size: tuple[int, int, int],
+        plevel_channels: int = 5,
+        surface_channels: int = 4,
+    ) -> None:
+        super().__init__()
+        # Here, we use two transposed convolutions to recover data
+        self.conv_surface = ConvTranspose2d(
+            in_channels=dim,
+            out_channels=surface_channels,
+            kernel_size=patch_size[1:],
+            stride=patch_size[1:],
+        )
+        self.conv = ConvTranspose3d(
+            in_channels=dim,
+            out_channels=plevel_channels,
+            kernel_size=patch_size,
+            stride=patch_size,
+        )
+
+    def forward(self, x: Tensor, embedding_shape: torch.Size) -> tuple[Tensor, Tensor]:
+        # Reshape x back to three dimensions
+        x = x.reshape(
+            x.shape[0], embedding_shape[1], embedding_shape[2], embedding_shape[3], -1
+        )
+        x = x.permute(0, 4, 1, 2, 3)
+
+        # Call the transposed convolution
+        output_plevel: Tensor = self.conv(x[:, :, :-1, :, :].contiguous())
+        output_surface: Tensor = self.conv_surface(x[:, :, -1, :, :].contiguous())
+
+        return output_plevel, output_surface
+
+
+class DownSample(nn.Module):
+    """Down-sampling operation. The number of tokens is divided by 4 while
+    their size in multiplied by 2.
+    E. g., from (8x360x181) tokens of size 192 to (8x180x91) tokens of size
+    384.
+
+        Args:
+            dim (int): initial size of the tokens
+    """
+
+    def __init__(self, data_size: torch.Size, dim: int) -> None:
+        super().__init__()
+        # A linear function and a layer normalization
+        self.linear = Linear(in_features=4 * dim, out_features=2 * dim, bias=False)
+        self.norm = LayerNorm(normalized_shape=4 * dim)
+        self.pad = CustomPad3d(data_size[-3:], (1, 2, 2))
+        padded_size = self.pad.padded_size
+        self.downsampled_size = torch.Size(
+            [padded_size[0], padded_size[1] // 2, padded_size[2] // 2]
+        )
+
+    def forward(
+        self, x: Tensor, embedding_shape: torch.Size
+    ) -> tuple[Tensor, torch.Size]:
+        # Reshape x to three dimensions for downsampling
+        x = x.reshape(shape=embedding_shape)
+
+        # Padding the input to facilitate downsampling
+        x = x.permute((0, 4, 1, 2, 3))
+        x = self.pad(x)
+        x = x.permute((0, 2, 3, 4, 1))
+
+        # Reorganize x to reduce the resolution: simply change the order and
+        # downsample from (8, 182, 360) to (8, 91, 180)
+        z, h, w = x.shape[1:4]
+        # Reshape x to facilitate downsampling
+        x = x.reshape(shape=(x.shape[0], z, h // 2, 2, w // 2, 2, x.shape[-1]))
+        # Change the order of x
+        x = x.permute((0, 1, 2, 4, 3, 5, 6))
+        # Reshape to get a tensor of resolution (8, 91, 180) -> 4 times less
+        # tokens of 4 times bigger size
+        x = x.reshape(shape=(x.shape[0], z * (h // 2) * (w // 2), -1))
+
+        # Call the layer normalization
+        x = self.norm(x)
+
+        # Decrease the size of the tokens to reduce computation cost
+        x = self.linear(x)
+        return x, torch.Size([x.shape[0], z, (h // 2), (w // 2), x.shape[-1]])
+
+
+class UpSample(nn.Module):
+    def __init__(self, input_dim: int, output_dim: int) -> None:
+        """Up-sampling operation. The number of tokens is mutiplied by 4.
+
+        Args:
+            input_dim (int): input token size
+            output_dim (int): output token size
+        """
+        super().__init__()
+        # Linear layers without bias to increase channels of the data
+        self.linear1 = Linear(input_dim, output_dim * 4, bias=False)
+
+        # Linear layers without bias to mix the data up
+        self.linear2 = Linear(output_dim, output_dim, bias=False)
+
+        # Normalization
+        self.norm = LayerNorm(output_dim)
+
+    def forward(self, x: Tensor, embedding_shape: torch.Size) -> Tensor:
+        assert (
+            x.shape[-1] % 4 == 0
+        ), f"The token size must be divisible by 4, but is {x.shape[-1]}"
+        # Z, H, W represent the desired output shape
+        h_d = embedding_shape[2] // 2 + embedding_shape[2] % 2
+        w_d = embedding_shape[3] // 2 + embedding_shape[3] % 2
+
+        # Call the linear functions to increase channels of the data
+        x = self.linear1(x)
+
+        # Reorganize x to increase the resolution: simply change the order and
+        # upsample from (8, 91, 180) to (8, 182, 360)
+        # Reshape x to facilitate upsampling.
+        x = x.reshape(shape=(x.shape[0], embedding_shape[1], h_d, w_d, 2, 2, -1))
+        # Change the order of x
+        x = x.permute((0, 1, 2, 4, 3, 5, 6))
+        # Reshape to get Tensor with a resolution of (8, 182, 360)
+        x = x.reshape(shape=(x.shape[0], embedding_shape[1], h_d * 2, w_d * 2, -1))
+
+        # Crop the output to the input shape of the network
+        x = x[:, :, : embedding_shape[2], : embedding_shape[3], :]
+
+        # Reshape x back
+        x = x.reshape(shape=(x.shape[0], -1, x.shape[-1]))
+
+        # Call the layer normalization
+        x = self.norm(x)
+
+        # Mixup normalized tensors
+        x = self.linear2(x)
+        return x
+
+
+class EarthSpecificLayer(nn.Module):
+    """Basic layer of our network, contains 2 or 6 blocks
+
+    Args:
+        depth (int): number of blocks
+        data_size (torch.Size): see EarthSpecificBlock
+        dim (int): see EarthSpecificBlock
+        drop_path_ratio_list (Tensor): see EarthSpecificBlock
+        num_heads (int): see EarthSpecificBlock
+    """
+
+    def __init__(
+        self,
+        depth: int,
+        data_size: torch.Size,
+        dim: int,
+        drop_path_ratio_list: Tensor,
+        num_heads: int,
+        **kwargs: Any,
+    ) -> None:
+
+        super().__init__()
+        self.blocks = nn.ModuleList()
+
+        # Construct basic blocks
+        for i in range(depth):
+            self.blocks.append(
+                EarthSpecificBlock(
+                    data_size, dim, drop_path_ratio_list[i].item(), num_heads, **kwargs
+                )
+            )
+
+    def forward(
+        self, x: Tensor, embedding_shape: torch.Size, *args: Any, **kwargs: Any
+    ) -> Tensor:
+        for i, block in enumerate(self.blocks):
+            # Roll the input every two blocks
+            if i % 2 == 0:
+                x = block(x, embedding_shape, roll=False, *args, **kwargs)
+            else:
+                x = block(x, embedding_shape, roll=True, *args, **kwargs)
+        return x
+
+
+class EarthSpecificBlock(nn.Module):
+    """3D transformer block with Earth-Specific bias and window attention,
+    see https://github.com/microsoft/Swin-Transformer for the official
+    implementation of 2D window attention.
+    The major difference is that we expand the dimensions to 3 and replace the
+    relative position bias with Earth-Specific bias.
+
+    Args:
+        data_size (torch.Size): data size in terms of plevel, latitude, longitude
+        dim (int): token size
+        drop_path_ratio (float): ratio to apply to drop path
+        num_heads (int): number of attention heads
+        window_size (tuple[int], optional): window size for the sliding window
+        attention. Defaults to (2, 6, 12).
+        dropout_rate (float, optional): dropout rate in the MLP. Defaults to 0..
+        checkpoint_activation (bool): whether to use checkpointing for the
+        activation function. Defaults to False.
+        lam (bool): activates the limited-area model masks. Defaults to False.
+    """
+
+    def __init__(
+        self,
+        data_size: torch.Size,
+        dim: int,
+        drop_path_ratio: float,
+        num_heads: int,
+        window_size: tuple[int, int, int] = (2, 6, 12),
+        dropout_rate: float = 0.0,
+        checkpoint_activation: bool = False,
+        lam: bool = False,
+    ) -> None:
+        super().__init__()
+
+        self.checkpoint_activation = checkpoint_activation
+        self.lam = lam
+        # Define the window size of the neural network
+        self.window_size = window_size
+        assert all(
+            [w_s % 2 == 0 for w_s in window_size]
+        ), "Window size must be divisible by 2"
+        self.shift_size = tuple([w_size // 2 for w_size in window_size])
+
+        # Initialize serveral operations
+        self.drop_path = DropPath(drop_prob=drop_path_ratio)
+        self.norm1 = LayerNorm(dim)
+        self.pad3D = CustomPad3d(data_size[-3:], self.window_size)
+        self.attention = EarthAttention3D(
+            self.pad3D.padded_size, dim, num_heads, dropout_rate, self.window_size
+        )
+        self.norm2 = LayerNorm(dim)
+        self.mlp = MLP(dim, dropout_rate=dropout_rate)
+
+    def forward(self, x: Tensor, embedding_shape: torch.Size, roll: bool) -> Tensor:
+        # Save the shortcut for skip-connection
+        shortcut = x
+
+        # Reshape input to three dimensions to calculate window attention
+        # x = x.view((x.shape[0], Z, H, W, -1))
+        x = x.view(embedding_shape)
+
+        # Zero-pad input if needed
+        # reshape data for padding, from B, Z, H, W, C to B, C, Z, H, W
+        x = x.permute((0, 4, 1, 2, 3))
+        x = self.pad3D(x)
+
+        # back to previous shape
+        x = x.permute((0, 2, 3, 4, 1))
+
+        b, padded_z, padded_h, padded_w, c = x.shape
+
+        if roll:
+            # Roll x for half of the window for 3 dimensions
+            x = x.roll(
+                shifts=(-self.shift_size[0], -self.shift_size[1], -self.shift_size[2]),
+                dims=(1, 2, 3),
+            )
+            # Generate mask of attention masks
+            # If two pixels are not adjacent, then mask the attention between them
+            # Your can set the matrix element to -1000 when it is not adjacent,
+            # then add it to the attention
+            assert len(self.shift_size) == 3, "Shift size should be 3D"
+            mask = generate_3d_attention_mask(
+                x, self.window_size, self.shift_size, self.lam
+            )
+        else:
+            # e.g., zero matrix when you add mask to attention
+            mask = None
+
+        # Reorganize data to calculate window attention
+        x_window = x.reshape(
+            shape=(
+                x.shape[0],
+                padded_z // self.window_size[0],
+                self.window_size[0],
+                padded_h // self.window_size[1],
+                self.window_size[1],
+                padded_w // self.window_size[2],
+                self.window_size[2],
+                -1,
+            )
+        )
+        x_window = x_window.permute((0, 1, 3, 5, 2, 4, 6, 7))
+
+        # Get data stacked in 3D cubes, which will further be used to
+        # calculated attention among each cube
+        x_window = x_window.reshape(
+            shape=(
+                -1,
+                self.window_size[0] * self.window_size[1] * self.window_size[2],
+                c,
+            )
+        )
+
+        # Apply 3D window attention with Earth-Specific bias
+        if self.checkpoint_activation:
+            x_window = checkpoint(
+                self.attention,
+                x_window,
+                mask,
+                b,
+                padded_z,
+                padded_h,
+                use_reentrant=False,
+            )
+        else:
+            x_window = self.attention(x_window, mask, b, padded_z, padded_h)
+
+        # Reorganize data to original shapes
+        x = x_window.reshape(
+            shape=(
+                b,
+                padded_z // self.window_size[0],
+                padded_h // self.window_size[1],
+                padded_w // self.window_size[2],
+                self.window_size[0],
+                self.window_size[1],
+                self.window_size[2],
+                -1,
+            )
+        )
+        x = x.permute((0, 1, 4, 2, 5, 3, 6, 7))
+
+        # Reshape the tensor back to its original shape
+        x = x.reshape(shape=(b, padded_z, padded_h, padded_w, -1))
+
+        if roll:
+            # Roll x back for half of the window
+            x = x.roll(
+                shifts=(self.shift_size[0], self.shift_size[1], self.shift_size[2]),
+                dims=(1, 2, 3),
+            )
+
+        # Crop the zero-padding
+        (
+            padding_left,
+            padding_right,
+            padding_top,
+            padding_bottom,
+            padding_front,
+            padding_back,
+        ) = self.pad3D.padding
+        x = x[
+            :,
+            padding_front : padded_z - padding_back,
+            padding_top : padded_h - padding_bottom,
+            padding_left : padded_w - padding_right,
+            :,
+        ]
+
+        # Reshape the tensor back to the input shape
+        x = x.reshape(shape=(b, -1, c))
+
+        # Main calculation stages
+        x = shortcut + self.drop_path(self.norm1(x))
+        x = x + self.drop_path(self.norm2(self.mlp(x)))
+        return x
+
+
+class EarthAttention3D(nn.Module):
+    """3D sliding window attention with the Earth-Specific bias,
+    see https://github.com/microsoft/Swin-Transformer for the official
+    implementation of 2D sliding window attention.
+
+    Args:
+        data_size (torch.Size): data size in terms of plevel, latitude, longitude
+        dim (int): token size
+        num_heads (int): number of heads
+        dropout_rate (float): dropout rate
+        window_size (tuple[int]): window size (z, h ,w)
+    """
+
+    def __init__(
+        self,
+        data_size: torch.Size,
+        dim: int,
+        num_heads: int,
+        dropout_rate: float,
+        window_size: tuple[int, int, int],
+    ) -> None:
+        super().__init__()
+
+        # Store several attributes
+        self.head_number = num_heads
+        self.dim = dim
+        self.scale = (dim // num_heads) ** -0.5
+        self.window_size = window_size
+
+        # Construct position index to reuse self.earth_specific_bias
+        self.register_buffer(
+            "position_index", define_3d_earth_position_index(window_size)
+        )
+
+        # Init earth specific bias
+        # only pressure level and latitude have absolute bias, longitude is cyclic
+        # data_size is plevel, latitude, longitude
+        self.num_windows = (data_size[0] // self.window_size[0]) * (
+            data_size[1] // self.window_size[1]
+        )
+
+        # For each window, we will construct a set of parameters according to the paper
+        # Inside a window, plevel and latitude positions are absolute while longitude are relative
+        self.earth_specific_bias = nn.Parameter(
+            torch.zeros(
+                (2 * self.window_size[2] - 1)
+                * self.window_size[1] ** 2
+                * self.window_size[0] ** 2,
+                self.num_windows,
+                self.head_number,
+            )
+        )
+
+        # Initialize several operations
+        self.linear1 = Linear(dim, dim * 3, bias=True)
+        self.linear2 = Linear(dim, dim)
+        self.softmax = Softmax(dim=-1)
+        self.dropout = Dropout(p=dropout_rate)
+
+        # Initialize the tensors using Truncated normal distribution
+        torch.nn.init.trunc_normal_(self.earth_specific_bias, mean=0.0, std=0.02)
+
+    def forward(self, x: Tensor, mask: Tensor, b: int, z: int, h: int) -> Tensor:
+        # Record the original shape of the input (b*num_windows, window_size, dim)
+        original_shape = x.shape
+
+        # Linear layer to create query, key and value
+        x = self.linear1(x)
+
+        # reshape the data to calculate multi-head attention
+        qkv = x.reshape(
+            shape=(
+                x.shape[0],
+                x.shape[1],
+                3,
+                self.head_number,
+                self.dim // self.head_number,
+            )
+        )
+        query, key, value = qkv.permute((2, 0, 3, 1, 4))
+        # 3, b*num_windows, head_number, window_size, dim_head
+
+        # Scale the attention
+        query = query * self.scale
+
+        # Calculated the attention, a learnable bias is added to fix the nonuniformity of the grid.
+        self.attention = query @ key.mT
+        # B*num_windows_lon*num_windows, head_number, window_size, window_size
+
+        # self.earth_specific_bias is a set of neural network parameters to optimize.
+        assert isinstance(self.position_index, Tensor)
+        earth_specific_bias = self.earth_specific_bias[self.position_index]
+
+        # Reshape the learnable bias to the same shape as the attention matrix
+        earth_specific_bias = earth_specific_bias.reshape(
+            shape=(
+                self.window_size[0] * self.window_size[1] * self.window_size[2],
+                self.window_size[0] * self.window_size[1] * self.window_size[2],
+                self.num_windows,
+                self.head_number,
+            )
+        )
+        earth_specific_bias = earth_specific_bias.permute((2, 3, 0, 1))
+        earth_specific_bias = earth_specific_bias.unsqueeze(0)
+        # 1, num_windows, head_number, window_size, window_size
+
+        # Add the Earth-Specific bias to the attention matrix
+        attention_shape = self.attention.shape
+        # Reshape and permute the lon dim to match the shape of earth_specific_bias
+        attention = self.attention.reshape(
+            b,
+            self.num_windows,
+            -1,
+            self.head_number,
+            attention_shape[-2],
+            attention_shape[-1],
+        )
+        attention = attention.permute(0, 2, 1, 3, 4, 5)
+        attention = attention.reshape(
+            -1,
+            self.num_windows,
+            self.head_number,
+            attention_shape[-2],
+            attention_shape[-1],
+        )
+        # add bias
+        attention = attention + earth_specific_bias
+        # Reshape the attention matrix back
+        attention = attention.reshape(
+            b,
+            -1,
+            self.num_windows,
+            self.head_number,
+            attention_shape[-2],
+            attention_shape[-1],
+        )
+        attention = attention.permute(0, 2, 1, 3, 4, 5)
+        attention = attention.reshape(attention_shape)
+
+        # Mask the attention between non-adjacent pixels, e.g., simply add
+        # -100 to the masked element.
+        if mask is not None:
+            attention = attention.view(
+                b, -1, self.head_number, original_shape[1], original_shape[1]
+            )
+            attention = attention + mask.unsqueeze(1).unsqueeze(0)
+            attention = attention.view(
+                -1, self.head_number, original_shape[1], original_shape[1]
+            )
+        attention = self.softmax(attention)
+        attention = self.dropout(attention)
+
+        # Calculated the tensor after spatial mixing.
+        x = attention @ value
+        # B*num_windows, head_number, window_size, dim_head
+
+        # Reshape tensor to the original shape
+        x = x.permute((0, 2, 1, 3))  # B*num_windows, window_size, head_number, dim_head
+        x = x.reshape(shape=original_shape)  # B*num_windows, window_size, dim
+
+        # Linear layer to post-process operated tensor
+        x = self.linear2(x)
+        x = self.dropout(x)
+        return x
+
+
+class MLP(nn.Module):
+    """MLP layers, same as most vision transformer architectures.
+
+    Args:
+        dim (int): input and output token size
+        dropout_rate (float): dropout rate applied after each linear layer
+    """
+
+    def __init__(self, dim: int, dropout_rate: float) -> None:
+        super().__init__()
+        self.linear1 = Linear(dim, dim * 4)
+        self.linear2 = Linear(dim * 4, dim)
+        self.activation = GELU()
+        self.drop = Dropout(p=dropout_rate)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.linear1(x)
+        x = self.activation(x)
+        x = self.drop(x)
+        x = self.linear2(x)
+        x = self.drop(x)
+        return x
diff --git a/weather-forecast/nwp-emulation/panguweather/pyproject.toml b/weather-forecast/nwp-emulation/panguweather/pyproject.toml
new file mode 100644
index 00000000..eded8f53
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/pyproject.toml
@@ -0,0 +1,54 @@
+[project]
+name = "nwp-emulation-panguweather"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    "dask==2024.10.0",
+    "jsonargparse[signatures]==4.39.0",
+    "lightning==2.5.1",
+    "omegaconf==2.3.0",
+    "timm==1.0.15",
+    "torch==2.8.0+cu128",
+    "xarray==2024.10.0",
+    "zarr==2.18.3"
+]
+
+# Nox will find here the python version that will be used
+classifiers = [
+  "Programming Language :: Python :: 3.10",
+]
+
+[dependency-groups]
+tests = [
+    "pytest-cov==7.0.0"
+]
+coverage = [
+    "coverage==7.13.4"
+]
+black = [
+    "black==26.1.0"
+]
+isort = [
+    "isort==7.0.0"
+]
+mypy = [
+    "mypy==1.19.1"
+]
+lint = [
+    "flake8==7.3.0",
+    "flake8-docstrings==1.7.0",
+    "flake8-use-fstring==1.4",
+    "flake8-variables-names==0.0.6",
+    "pep8-naming==0.15.1"
+]
+data = []
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch" },
+]
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl"
+explicit = true
diff --git a/weather-forecast/nwp-emulation/panguweather/tests/__init__.py b/weather-forecast/nwp-emulation/panguweather/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/weather-forecast/nwp-emulation/panguweather/tests/test_data.py b/weather-forecast/nwp-emulation/panguweather/tests/test_data.py
new file mode 100644
index 00000000..a7c68a83
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/tests/test_data.py
@@ -0,0 +1,42 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest import TestCase
+
+from data import check_era5_constant_masks
+
+
+class TestData(TestCase):
+
+    def setUp(self) -> None:
+        self.correct_era5_mask_list = [
+            "geopotential_at_surface",
+            "soil_type",
+            "high_vegetation_cover",
+        ]
+        self.wrong_era5_mask_list = ["topography", "soil_type", "high_vegetation_cover"]
+        self.curated_era5_mask_list = ["soil_type", "high_vegetation_cover"]
+
+    def test_check_era5_constant_masks(self) -> None:
+        self.assertEqual(
+            check_era5_constant_masks(self.correct_era5_mask_list),
+            self.correct_era5_mask_list,
+        )
+        self.assertEqual(
+            check_era5_constant_masks(self.wrong_era5_mask_list),
+            self.curated_era5_mask_list,
+        )
+
+
+if __name__ == "main":
+    unittest.main()
diff --git a/weather-forecast/nwp-emulation/panguweather/tests/test_models.py b/weather-forecast/nwp-emulation/panguweather/tests/test_models.py
new file mode 100644
index 00000000..a88988aa
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/tests/test_models.py
@@ -0,0 +1,127 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest import TestCase
+
+import numpy as np
+import torch
+
+from panguweather.pangu import (  # type: ignore[import]
+    CustomPad2d,
+    CustomPad3d,
+    DownSample,
+    PanguModel,
+    PatchEmbedding,
+    UpSample,
+)
+
+
+class TestPangu(TestCase):
+
+    def setUp(self) -> None:
+        self.mock_3d_data = torch.rand(2, 5, 13, 120, 240)
+        self.patch_size_3d = (2, 8, 8)
+
+        self.mock_2d_data = torch.rand(2, 7, 120, 240)
+        self.patch_size_2d = (8, 8)
+
+        self.mock_2d_data_no_mask = torch.rand(2, 4, 120, 240)
+        self.mock_constant_mask = {
+            "mask1": {"mask": np.random.rand(120, 240).astype(np.float32)},
+            "mask2": {"mask": np.random.rand(120, 240).astype(np.float32)},
+            "mask3": {"mask": np.random.rand(120, 240).astype(np.float32)},
+        }
+
+        self.mock_token = torch.rand(2, 8 * 181 * 360, 192)
+        self.mock_token_upsample = torch.rand(2, 8 * 91 * 180, 384)
+
+    def test_custom_pad_3d(self) -> None:
+        pad = CustomPad3d(self.mock_3d_data.shape[-3:], self.patch_size_3d)
+        padded_data = pad(self.mock_3d_data)
+        self.assertEqual(padded_data.shape, torch.Size((2, 5, 14, 120, 240)))
+
+        with self.assertRaises(AssertionError):
+            CustomPad3d(self.mock_2d_data.shape[-2:], self.patch_size_3d)
+        with self.assertRaises(AssertionError):
+            CustomPad3d(self.mock_3d_data.shape[-3:], self.patch_size_2d)
+
+    def test_custom_pad_2d(self) -> None:
+        pad = CustomPad2d(self.mock_2d_data.shape[-2:], self.patch_size_2d)
+        padded_data = pad(self.mock_2d_data)
+        self.assertEqual(padded_data.shape, torch.Size((2, 7, 120, 240)))
+
+        with self.assertRaises(AssertionError):
+            CustomPad2d(self.mock_2d_data.shape[-2:], self.patch_size_3d)
+        with self.assertRaises(AssertionError):
+            CustomPad2d(self.mock_3d_data.shape[-3:], self.patch_size_2d)
+
+    def test_patch_embedding(self) -> None:
+        embed = PatchEmbedding(
+            c_dim=32,
+            patch_size=(2, 8, 8),
+            plevel_size=torch.Size([5, 13, 120, 240]),
+            surface_size=torch.Size([7, 120, 240]),
+        )
+        self.assertEqual(
+            embed(self.mock_3d_data, self.mock_2d_data)[0].shape,
+            torch.Size([2, 8 * 15 * 30, 32]),
+        )
+
+    def test_down_sample(self) -> None:
+        down_sample = DownSample(torch.Size([8, 181, 360]), 192)
+        self.assertEqual(
+            down_sample(self.mock_token, (2, 8, 181, 360, 192))[0].shape,
+            torch.Size([2, 8 * 91 * 180, 384]),
+        )
+
+    def test_up_sample(self) -> None:
+        up_sample = UpSample(384, 192)
+        self.assertEqual(
+            up_sample(self.mock_token_upsample, (2, 8, 181, 360, 384)).shape,
+            torch.Size([2, 8 * 181 * 360, 192]),
+        )
+
+    def test_pangu(self) -> None:
+        with torch.no_grad():
+            model = PanguModel(
+                latitude=120,
+                longitude=240,
+                surface_variables=4,
+                plevel_variables=5,
+                plevels=13,
+                plevel_patch_size=(2, 8, 8),
+            )
+            out_plevel, out_surface = model(
+                self.mock_3d_data, self.mock_2d_data_no_mask
+            )
+        self.assertEqual(out_plevel.shape, self.mock_3d_data.shape)
+        self.assertEqual(out_surface.shape, self.mock_2d_data_no_mask.shape)
+        with torch.no_grad():
+            model = PanguModel(
+                latitude=120,
+                longitude=240,
+                surface_variables=4,
+                plevel_variables=5,
+                plevels=13,
+                plevel_patch_size=(2, 8, 8),
+                constant_masks=self.mock_constant_mask,
+            )
+            out_plevel, out_surface = model(
+                self.mock_3d_data, self.mock_2d_data_no_mask
+            )
+        self.assertEqual(out_plevel.shape, self.mock_3d_data.shape)
+        self.assertEqual(out_surface.shape, self.mock_2d_data_no_mask.shape)
+
+
+if __name__ == "main":
+    unittest.main()
diff --git a/weather-forecast/nwp-emulation/panguweather/tests/test_utils.py b/weather-forecast/nwp-emulation/panguweather/tests/test_utils.py
new file mode 100644
index 00000000..999a331f
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/tests/test_utils.py
@@ -0,0 +1,98 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest import TestCase
+
+import torch
+
+from utils import (
+    cat_constant_masks,
+    define_3d_earth_position_index,
+    define_3d_relative_position_index,
+    generate_2d_swin_masks,
+    generate_modified_2d_swin_masks,
+    slice_deserializer,
+)
+
+
+class TestUtils(TestCase):
+
+    def setUp(self) -> None:
+        self.mock_surface_data = torch.rand(2, 4, 721, 1440)
+        self.mock_constant_masks = torch.rand(2, 721, 1440)
+        self.slice1 = slice("2019-01-01", "2020-03-25")
+        self.slice1_str1 = "slice('2019-01-01', '2020-03-25')"
+        self.slice1_str2 = "slice('2019-01-01', '2020-03-25', None)"
+        self.slice2 = slice("2019-01", "2020-03", "12h")
+
+    def test_cat_constant_masks(self) -> None:
+        cat_data = cat_constant_masks(self.mock_surface_data, self.mock_constant_masks)
+        self.assertEqual(cat_data.shape, torch.Size([2, 6, 721, 1440]))
+
+    def test_2d_attention_masks(self) -> None:
+        pad_h = 360
+        pad_w = 180
+        window_size = (12, 6)
+        shift_size = (6, 3)
+        num_windows = (pad_h // window_size[0]) * (pad_w // window_size[1])
+        self.assertTrue(
+            torch.all(
+                torch.eq(
+                    generate_2d_swin_masks(
+                        pad_h, pad_w, window_size, shift_size, num_windows
+                    ),
+                    generate_modified_2d_swin_masks(
+                        pad_h, pad_w, window_size, shift_size, num_windows
+                    ),
+                )
+            )
+        )
+
+    def test_define_3d_relative_position_index(self) -> None:
+        window_size = (2, 6, 12)
+        positional_bias_length = (
+            (2 * window_size[0] - 1)
+            * (2 * window_size[1] - 1)
+            * (2 * window_size[2] - 1)
+        )
+        index = define_3d_relative_position_index(window_size)
+        self.assertEqual(positional_bias_length, len(index.unique()))
+        self.assertTrue(
+            torch.all(torch.arange(positional_bias_length) == index.unique())
+        )
+        self.assertEqual(
+            (window_size[0] * window_size[1] * window_size[2]) ** 2, len(index)
+        )
+
+    def test_define_3d_earth_position_index(self) -> None:
+        window_size = (2, 6, 12)
+        positional_bias_length = (
+            window_size[0] ** 2 * window_size[1] ** 2 * (2 * window_size[2] - 1)
+        )
+        index = define_3d_earth_position_index(window_size)
+        self.assertEqual(positional_bias_length, len(index.unique()))
+        self.assertTrue(
+            torch.all(torch.arange(positional_bias_length) == index.unique())
+        )
+        self.assertEqual(
+            (window_size[0] * window_size[1] * window_size[2]) ** 2, len(index)
+        )
+
+    def test_slice_deserializer(self) -> None:
+        self.assertEqual(self.slice1, slice_deserializer(self.slice1_str1))
+        self.assertEqual(self.slice1, slice_deserializer(self.slice1_str2))
+        self.assertEqual(self.slice2, slice_deserializer(str(self.slice2)))
+
+
+if __name__ == "main":
+    unittest.main()
diff --git a/weather-forecast/nwp-emulation/panguweather/trainer.py b/weather-forecast/nwp-emulation/panguweather/trainer.py
new file mode 100644
index 00000000..d25076ba
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/trainer.py
@@ -0,0 +1,33 @@
+"""This module proposes Pytorch style a Trainer class for the gwd use-case."""
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from lightning.pytorch.cli import LightningArgumentParser, LightningCLI
+
+
+class MyLightningCLI(LightningCLI):
+    def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
+        parser.link_arguments(
+            "data.data_path", "model.init_args.data_path", apply_on="instantiate"
+        )
+
+
+def main() -> None:
+    """Instantiate the CLI and launch the trainer."""
+    cli = MyLightningCLI(run=False, parser_kwargs={"parser_mode": "omegaconf"})
+    cli.trainer.fit(model=cli.model, datamodule=cli.datamodule)
+    cli.trainer.test(model=cli.model, datamodule=cli.datamodule)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/weather-forecast/nwp-emulation/panguweather/transformer_module.py b/weather-forecast/nwp-emulation/panguweather/transformer_module.py
new file mode 100644
index 00000000..34d366c9
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/transformer_module.py
@@ -0,0 +1,440 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from functools import cached_property
+from typing import Any, List, Type, Union
+
+import numpy as np
+import torch
+import xarray
+from lightning import LightningModule
+from lightning.pytorch.loggers import MLFlowLogger
+from lightning.pytorch.utilities.types import (
+    OptimizerConfig,
+    OptimizerLRSchedulerConfig,
+)
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+from torch import Tensor, nn, optim
+
+from data import PLEVEL_LIST, PLEVEL_VARIABLES, SURFACE_VARIABLES
+from metrics import LatitudeWeightedMeanSquaredError
+from pangu import PanguModel
+
+PLEVEL_VARIABLE_WEIGHTS = {
+    "geopotential": 3.0,
+    "specific_humidity": 0.6,
+    "temperature": 1.5,
+    "u_component_of_wind": 0.77,
+    "v_component_of_wind": 0.54,
+}
+SURFACE_VARIABLE_WEIGHTS = {
+    "mean_sea_level_pressure": 1.5,
+    "10m_u_component_of_wind": 0.77,
+    "10m_v_component_of_wind": 0.66,
+    "2m_temperature": 3.0,
+}
+
+
+class PanguModule(LightningModule):
+    """Base transformer module to train PanguWeather.
+    Args:
+        criterion (nn.Module): Loss function. Default is mean absolute error (L1).
+        loss_weight (float): Weight of the surface variable loss. Default is 0.25.
+        lr (float): Learning rate. Default is 5e-4.
+        plevel_variable_weights (tuple): Weight of each plevel variable in the loss.
+        Default is (3.0, 0.6, 1.5, 0.77, 0.54) for Z, Q, T, U and V, respectively.
+        surface_variable_weights (tuple): Weight of each surface variable in the loss.
+        Default is (1.5, 0.77, 0.66, 3.0) for MSLP, U10, V10 and T2M, respectively.
+        time_step (int): timeframe of one forecast iteration in hour.
+    """
+
+    def __init__(
+        self,
+        constant_masks: dict,
+        latitude: int,
+        longitude: int,
+        criterion: Type[nn.Module] = nn.L1Loss,
+        loss_weight: float = 0.25,
+        lr: float = 5e-4,
+        weight_decay: float = 3e-6,
+        plevel_variable_weights: tuple | None = None,
+        surface_variable_weights: tuple | None = None,
+        surface_variables: List = SURFACE_VARIABLES,
+        plevel_variables: List = PLEVEL_VARIABLES,
+        plevels: List = PLEVEL_LIST,
+        time_step: int = 6,
+        mean_file: str | None = None,
+        stddev_file: str | None = None,
+        wl_norm: bool = False,
+        norm_cst_mask: bool = False,
+        use_scheduler: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__()
+
+        self.save_hyperparameters()
+        self.criterion = criterion()
+        self.loss_weight = loss_weight
+        self.lr = lr
+        self.weight_decay = weight_decay
+        if plevel_variable_weights is None:
+            plevel_variable_weights = tuple(
+                v for _, v in sorted(PLEVEL_VARIABLE_WEIGHTS.items())
+            )
+        assert plevel_variable_weights is not None
+        self.plevel_variable_weights = plevel_variable_weights
+        if surface_variable_weights is None:
+            surface_variable_weights = tuple(
+                v for _, v in sorted(SURFACE_VARIABLE_WEIGHTS.items())
+            )
+        assert surface_variable_weights is not None
+        self.surface_variable_weights = surface_variable_weights
+        self.time_step = time_step
+        self.use_scheduler = use_scheduler
+
+        self.plevel_variables = sorted(plevel_variables)
+        self.surface_variables = sorted(surface_variables)
+        self.plevels = sorted(plevels)
+        # Compute RMSE for Z500, T850, T2M and U10
+        self.rmse_z500 = LatitudeWeightedMeanSquaredError(squared=False)
+        self.rmse_t850 = LatitudeWeightedMeanSquaredError(squared=False)
+        self.rmse_t2m = LatitudeWeightedMeanSquaredError(squared=False)
+        self.rmse_u10 = LatitudeWeightedMeanSquaredError(squared=False)
+
+        # Load static data
+        if mean_file is not None and stddev_file is not None:
+            era5_means = xarray.load_dataset(mean_file)
+            file_surface_means = np.stack(
+                [
+                    era5_means[v].to_numpy().astype(np.float32)
+                    for v in self.surface_variables
+                ]
+            )
+            surface_means_tensor = torch.from_numpy(file_surface_means)
+            self.surface_means: Tensor
+            self.register_buffer(
+                "surface_means",
+                surface_means_tensor.unsqueeze(-1).unsqueeze(-1).unsqueeze(0),
+            )
+            file_plevel_means = np.stack(
+                [
+                    era5_means[v].to_numpy().astype(np.float32)
+                    for v in self.plevel_variables
+                ]
+            )
+            plevel_means_tensor = torch.from_numpy(file_plevel_means)
+            self.plevel_means: Tensor
+            self.register_buffer(
+                "plevel_means",
+                plevel_means_tensor.unsqueeze(-1).unsqueeze(-1).unsqueeze(0),
+            )
+            era5_stddev = xarray.load_dataset(stddev_file)
+            file_surface_stddev = np.stack(
+                [
+                    era5_stddev[v].to_numpy().astype(np.float32)
+                    for v in self.surface_variables
+                ]
+            )
+            surface_stddev_tensor = torch.from_numpy(file_surface_stddev)
+            self.surface_stddev: Tensor
+            self.register_buffer(
+                "surface_stddev",
+                surface_stddev_tensor.unsqueeze(-1).unsqueeze(-1).unsqueeze(0),
+            )
+            file_plevel_stddev = np.stack(
+                [
+                    era5_stddev[v].to_numpy().astype(np.float32)
+                    for v in self.plevel_variables
+                ]
+            )
+            plevel_stddev_tensor = torch.from_numpy(file_plevel_stddev)
+            self.plevel_stddev: Tensor
+            self.register_buffer(
+                "plevel_stddev",
+                plevel_stddev_tensor.unsqueeze(-1).unsqueeze(-1).unsqueeze(0),
+            )
+            self.normalize_data = True
+        else:
+            self.normalize_data = False
+
+        if norm_cst_mask and constant_masks is not None:
+            for cst_data in constant_masks.values():
+                cst_data["mask"] = (
+                    cst_data["mask"] - cst_data["mask"].mean()
+                ) / cst_data["mask"].std()
+
+        self.model = PanguModel(
+            constant_masks=constant_masks,
+            latitude=latitude,
+            longitude=longitude,
+            surface_variables=len(surface_variables),
+            plevel_variables=len(plevel_variables),
+            plevels=len(plevels),
+            **kwargs,
+        )
+
+    def forward(self, input_plevel: Tensor, input_surface: Tensor) -> dict:
+        return self.model(input_plevel, input_surface)
+
+    def normalize(
+        self, plevel_data: Tensor, surface_data: Tensor
+    ) -> tuple[Tensor, Tensor]:
+        plevel_data_normlized = (plevel_data - self.plevel_means) / self.plevel_stddev
+        surface_data_normlized = (
+            surface_data - self.surface_means
+        ) / self.surface_stddev
+        return plevel_data_normlized, surface_data_normlized
+
+    def denormalize(
+        self, plevel_data: Tensor, surface_data: Tensor
+    ) -> tuple[Tensor, Tensor]:
+        plevel_data_denormlized = plevel_data * self.plevel_stddev + self.plevel_means
+        surface_data_denormlized = (
+            surface_data * self.surface_stddev + self.surface_means
+        )
+        return plevel_data_denormlized, surface_data_denormlized
+
+    def common_step(
+        self, batch: dict, stage: str, batch_idx: int | None = None
+    ) -> STEP_OUTPUT:
+        plevel_data = batch["plevel_data"]
+        surface_data = batch["surface_data"]
+        plevel_labels = batch["plevel_labels"]
+        surface_labels = batch["surface_labels"]
+
+        if self.normalize_data:
+            plevel_data, surface_data = self.normalize(plevel_data, surface_data)
+            plevel_labels, surface_labels = self.normalize(
+                plevel_labels, surface_labels
+            )
+        plevel_output, surface_output = self(plevel_data, surface_data)
+        # plevel data has shape (B, variables, levels, lat, long)
+        loss_plevel = torch.sum(
+            torch.stack(
+                [
+                    self.criterion(plevel_output[:, i], plevel_labels[:, i]) * weight
+                    for i, weight in enumerate(self.plevel_variable_weights)
+                ]
+            )
+        )
+        # surface data has shape (B, variables, lat, long)
+        loss_surface = torch.sum(
+            torch.stack(
+                [
+                    self.criterion(surface_output[:, i], surface_labels[:, i]) * weight
+                    for i, weight in enumerate(self.surface_variable_weights)
+                ]
+            )
+        )
+        loss = loss_plevel + loss_surface * self.loss_weight
+
+        self.log(
+            f"{stage}_loss",
+            loss,
+            prog_bar=True,
+            on_step=True,
+            batch_size=len(plevel_data),
+            sync_dist=True,
+        )
+
+        if self.global_rank == 0:
+            nn_norm = 0.0
+            for p in self.parameters():
+                nn_norm += (p.clone().to("cpu") ** 2).sum().item()
+            self.log("Model norm", nn_norm, prog_bar=True, on_step=True)
+
+        if stage == "val":
+            if self.normalize_data:
+                plevel_output, surface_output = self.denormalize(
+                    plevel_output, surface_output
+                )
+                plevel_labels, surface_labels = self.denormalize(
+                    plevel_labels, surface_labels
+                )
+            # Compute RMSE for Z500, T850, T2M and U10
+            z500_index = (
+                self.plevel_variables.index("geopotential"),
+                self.plevels.index(500),
+            )
+            t850_index = (
+                self.plevel_variables.index("temperature"),
+                self.plevels.index(850),
+            )
+            t2m_index = self.surface_variables.index("2m_temperature")
+            u10_index = self.surface_variables.index("10m_u_component_of_wind")
+            self.rmse_z500(
+                plevel_output[:, z500_index[0], z500_index[1]],
+                plevel_labels[:, z500_index[0], z500_index[1]],
+                batch["latitudes"],
+            )
+            self.rmse_t850(
+                plevel_output[:, t850_index[0], t850_index[1]],
+                plevel_labels[:, t850_index[0], t850_index[1]],
+                batch["latitudes"],
+            )
+            self.rmse_t2m(
+                surface_output[:, t2m_index],
+                surface_labels[:, t2m_index],
+                batch["latitudes"],
+            )
+            self.rmse_u10(
+                surface_output[:, u10_index],
+                surface_labels[:, u10_index],
+                batch["latitudes"],
+            )
+            # Log RMSEs
+            self.log(f"{stage}_rmse_z500", self.rmse_z500, on_epoch=True)
+            self.log(f"{stage}_rmse_t850", self.rmse_t850, on_epoch=True)
+            self.log(f"{stage}_rmse_t2m", self.rmse_t2m, on_epoch=True)
+            self.log(f"{stage}_rmse_u10", self.rmse_u10, on_epoch=True)
+
+        if stage == "train":
+            esb_norm = {}
+            for n, p in self.named_parameters():
+                if "earth_specific_bias" in n:
+                    esb_norm[n] = torch.linalg.norm(p).item()
+            self.log_dict(esb_norm, on_step=True, rank_zero_only=True)
+
+        return loss
+
+    def training_step(self, batch: dict, batch_idx: int) -> STEP_OUTPUT:
+        return self.common_step(batch=batch, stage="train", batch_idx=batch_idx)
+
+    def validation_step(self, batch: dict, batch_idx: int) -> STEP_OUTPUT | None:
+        return self.common_step(batch=batch, stage="val", batch_idx=batch_idx)
+
+    def test_step(self, batch: dict, batch_idx: int) -> STEP_OUTPUT | None:
+        plevel_data = batch["plevel_data"]
+        surface_data = batch["surface_data"]
+        plevel_labels = batch["plevel_labels"]
+        surface_labels = batch["surface_labels"]
+        if self.normalize_data:
+            plevel_data, surface_data = self.normalize(plevel_data, surface_data)
+        data_time = batch["data_time"]
+        label_time = batch["label_time"]
+
+        # Compute data time step
+        l_time = label_time[0].cpu().numpy().astype(np.int64)
+        d_time = data_time[0].cpu().numpy().astype(np.int64)
+        time_horizon = np.timedelta64(l_time - d_time, "s")
+        num_iterations = time_horizon // np.timedelta64(self.time_step, "h")
+        try:
+            time_horizon % np.timedelta64(self.time_step, "h") == 0
+        except ValueError:
+            print(
+                "The time horizon is not divisible by the model time step, it will amount to: ",
+                num_iterations * self.time_step,
+            )
+        last_plevel_predictions = plevel_data
+        last_surface_predictions = surface_data
+        for _ in range(num_iterations):
+            last_plevel_predictions, last_surface_predictions = self(
+                last_plevel_predictions, last_surface_predictions
+            )
+        if self.normalize_data:
+            last_plevel_predictions, last_surface_predictions = self.denormalize(
+                last_plevel_predictions, last_surface_predictions
+            )
+        # Compute RMSE for Z500, T850, T2M and U10
+        z500_index = (
+            self.plevel_variables.index("geopotential"),
+            self.plevels.index(500),
+        )
+        t850_index = (
+            self.plevel_variables.index("temperature"),
+            self.plevels.index(850),
+        )
+        t2m_index = self.surface_variables.index("2m_temperature")
+        u10_index = self.surface_variables.index("10m_u_component_of_wind")
+        self.rmse_z500(
+            last_plevel_predictions[:, z500_index[0], z500_index[1]],
+            plevel_labels[:, z500_index[0], z500_index[1]],
+            batch["latitudes"],
+        )
+        self.rmse_t850(
+            last_plevel_predictions[:, t850_index[0], t850_index[1]],
+            plevel_labels[:, t850_index[0], t850_index[1]],
+            batch["latitudes"],
+        )
+        self.rmse_t2m(
+            last_surface_predictions[:, t2m_index],
+            surface_labels[:, t2m_index],
+            batch["latitudes"],
+        )
+        self.rmse_u10(
+            last_surface_predictions[:, u10_index],
+            surface_labels[:, u10_index],
+            batch["latitudes"],
+        )
+
+        # Log RMSEs
+        self.log("test_rmse_z500", self.rmse_z500, on_epoch=True)
+        self.log("test_rmse_t850", self.rmse_t850, on_epoch=True)
+        self.log("test_rmse_t2m", self.rmse_t2m, on_epoch=True)
+        self.log("test_rmse_u10", self.rmse_u10, on_epoch=True)
+
+        return None
+
+    def predict_step(self, batch: dict, batch_idx: int) -> dict:
+        plevel_data = batch["plevel_data"]
+        surface_data = batch["surface_data"]
+        if self.normalize_data:
+            plevel_data, surface_data = self.normalize(plevel_data, surface_data)
+        data_time = batch["data_time"]
+        plevel_output, surface_output = self(plevel_data, surface_data)
+        if self.normalize_data:
+            plevel_output, surface_output = self.denormalize(
+                plevel_output, surface_output
+            )
+        prediction_time = data_time + self.time_step
+        return {
+            "plevel_predictions": plevel_output,
+            "surface_predictions": surface_output,
+            "prediction_times": prediction_time,
+        }
+
+    def configure_optimizers(self) -> OptimizerConfig | OptimizerLRSchedulerConfig:
+        optimizer = optim.Adam(
+            self.parameters(), lr=self.lr, weight_decay=self.weight_decay
+        )
+        optim_dict: Union[OptimizerConfig, OptimizerLRSchedulerConfig]
+        if self.use_scheduler:
+            max_epochs = self.trainer.max_epochs
+
+            if max_epochs is None:
+                raise ValueError("self.trainer.max_epochs is not set, \
+                    but it's required for CosineAnnealingLR scheduler.")
+            optim_dict = dict(
+                optimizer=optimizer,
+                lr_scheduler=optim.lr_scheduler.CosineAnnealingLR(
+                    optimizer, max_epochs
+                ),
+            )
+        else:
+            optim_dict = dict(optimizer=optimizer)
+        return optim_dict
+
+    def on_before_optimizer_step(self, optimizer: optim.Optimizer) -> None:
+        from lightning.pytorch.utilities import grad_norm
+
+        # Compute the 2-norm for each layer
+        # If using mixed precision, the gradients are already unscaled here
+        norms = grad_norm(self, norm_type=2)
+        self.log_dict(norms, on_step=True, rank_zero_only=True)
+
+    @cached_property
+    def mlflow_logger(self) -> Union[MLFlowLogger, None]:
+        """Get the MLFlowLogger if it has been set."""
+        return next(
+            iter([o for o in self.loggers if isinstance(o, MLFlowLogger)]), None
+        )
diff --git a/weather-forecast/nwp-emulation/panguweather/utils.py b/weather-forecast/nwp-emulation/panguweather/utils.py
new file mode 100644
index 00000000..de3775f3
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/utils.py
@@ -0,0 +1,319 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+from torch import Tensor
+
+
+def check_same_coordinates(
+    longitude_1: Tensor, latitude_1: Tensor, longitude_2: Tensor, latitude_2: Tensor
+) -> bool:
+    """Check that 2 coordinate systems match, i. e., the longitudes and latitudes are the same
+
+    Args:
+        longitude_1 (Tensor): reference longitude
+        latitude_1 (Tensor): reference latitude
+        longitude_2 (Tensor): longitude to check
+        latitude_2 (Tensor): latitude to check
+
+    Returns:
+        bool
+    """
+    longitude_match: bool = bool(torch.all(longitude_1 == longitude_2).item())
+    latitude_match: bool = bool(torch.all(latitude_1 == latitude_2).item())
+    return longitude_match and latitude_match
+
+
+def cat_constant_masks(surface_data: Tensor, constant_masks: Tensor) -> Tensor:
+    """Concatenate constant masks to surface data.
+
+    Args:
+        surface_data (Tensor): surface data
+        constant_masks (Tensor): Tensor of masks
+
+    Returns:
+        Tensor: constant masks concatenated with surface data
+    """
+    batch_size = surface_data.shape[0]
+    mask_data = constant_masks.expand(batch_size, *constant_masks.shape)
+    surface_data = torch.cat([surface_data, mask_data], dim=1)
+    return surface_data
+
+
+def generate_3d_attention_mask(
+    x: Tensor,
+    window_size: tuple[int, int, int],
+    shift_size: tuple[int, int, int],
+    lam: bool = False,
+) -> Tensor:
+    """Generate attention mask for sliding window attention in the context of 3D data.
+    Based on:
+    https://pytorch.org/vision/main/_modules/torchvision/models/swin_transformer.html#swin_s
+
+    Args:
+        x (Tensor): input data, used to generate the mask on the same device
+        window_size (tuple[int, int, int]): size of the sliding window
+        shift_size (tuple[int, int, int]): size of the shift for the sliding window
+
+    Returns:
+        Tensor: attention mask
+    """
+    assert x.dim() == 5, f"Data must be 3D, but has {x.dim()} dimension(s)"
+    _, pad_z, pad_h, pad_w, _ = x.shape
+    assert (
+        pad_z % window_size[0] == 0
+        and pad_h % window_size[1] == 0
+        and pad_w % window_size[2] == 0
+    ), "the data size must divisible by window size"
+    assert (
+        window_size[0] % shift_size[0] == 0
+        and window_size[1] % shift_size[1] == 0
+        and window_size[2] % shift_size[2] == 0
+    ), "the window size must divisible by shift size"
+
+    # Create the attention mask from the data to have same type and device
+    attention_mask = x.new_zeros((pad_z, pad_h, pad_w))
+    z_slices = ((0, -shift_size[0]), (-shift_size[0], None))
+    h_slices = ((0, -shift_size[1]), (-shift_size[1], None))
+
+    w_slices: tuple[tuple[int, None]] | tuple[tuple[int, int], tuple[int, None]]
+    if lam:
+        w_slices = ((0, -shift_size[2]), (-shift_size[2], None))
+    else:
+        w_slices = ((0, None),)
+
+    count = 0
+    for z in z_slices:
+        for h in h_slices:
+            for w in w_slices:
+                attention_mask[z[0] : z[1], h[0] : h[1], w[0] : w[1]] = count
+                count += 1
+
+    attention_mask = attention_mask.reshape(
+        pad_z // window_size[0],
+        window_size[0],
+        pad_h // window_size[1],
+        window_size[1],
+        pad_w // window_size[2],
+        window_size[2],
+    )
+    num_windows = (
+        (pad_z // window_size[0])
+        * (pad_h // window_size[1])
+        * (pad_w // window_size[2])
+    )
+    attention_mask = torch.permute(attention_mask, (0, 2, 4, 1, 3, 5)).reshape(
+        num_windows, -1
+    )
+    attention_mask = attention_mask.unsqueeze(1) - attention_mask.unsqueeze(2)
+    attention_mask.masked_fill_(attention_mask != 0, -100.0)
+    return attention_mask
+
+
+def generate_2d_swin_masks(
+    pad_h: int,
+    pad_w: int,
+    window_size: tuple[int, int],
+    shift_size: tuple[int, int],
+    num_windows: int,
+) -> Tensor:
+    """Original method to generate attention mask for sliding window attention of Swin transformer.
+    See https://pytorch.org/vision/main/_modules/torchvision/models/swin_transformer.html#swin_s
+
+    Args:
+        pad_h (int): height size of the data after padding
+        pad_w (int): width size of the data after padding
+        window_size (tuple[int, int]): size of the sliding window
+        shift_size (tuple[int, int]): size of the shift for the sliding window
+        num_windows (int): number of windows
+
+    Returns:
+        Tensor: attention mask
+    """
+    # generate attention mask
+    attn_mask = torch.zeros((pad_h, pad_w))
+    h_slices = (
+        (0, -window_size[0]),
+        (-window_size[0], -shift_size[0]),
+        (-shift_size[0], None),
+    )
+    w_slices = (
+        (0, -window_size[1]),
+        (-window_size[1], -shift_size[1]),
+        (-shift_size[1], None),
+    )
+    count = 0
+    for h in h_slices:
+        for w in w_slices:
+            attn_mask[h[0] : h[1], w[0] : w[1]] = count
+            count += 1
+    attn_mask = attn_mask.view(
+        pad_h // window_size[0], window_size[0], pad_w // window_size[1], window_size[1]
+    )
+    attn_mask = attn_mask.permute(0, 2, 1, 3).reshape(
+        num_windows, window_size[0] * window_size[1]
+    )
+    attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2)
+    attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+        attn_mask == 0, float(0.0)
+    )
+    return attn_mask
+
+
+def generate_modified_2d_swin_masks(
+    pad_h: int,
+    pad_w: int,
+    window_size: tuple[int, int],
+    shift_size: tuple[int, int],
+    num_windows: int,
+) -> Tensor:
+    """Generate attention mask for sliding window attention of Swin transformer.
+    Based on
+    https://pytorch.org/vision/main/_modules/torchvision/models/swin_transformer.html#swin_s,
+    but simplified
+
+    Args:
+        pad_h (int): height size of the data after padding
+        pad_w (int): width size of the data after padding
+        window_size (tuple[int, int]): size of the sliding window
+        shift_size (tuple[int, int]): size of the shift for the sliding window
+        num_windows (int): number of windows
+
+    Returns:
+        Tensor: attention mask
+    """
+    # generate attention mask
+    attn_mask = torch.zeros((pad_h, pad_w))
+    h_slices = ((0, -shift_size[0]), (-shift_size[0], None))
+    w_slices = ((0, -shift_size[1]), (-shift_size[1], None))
+    count = 0
+    for h in h_slices:
+        for w in w_slices:
+            attn_mask[h[0] : h[1], w[0] : w[1]] = count
+            count += 1
+    attn_mask = attn_mask.view(
+        pad_h // window_size[0], window_size[0], pad_w // window_size[1], window_size[1]
+    )
+    attn_mask = attn_mask.permute(0, 2, 1, 3).reshape(
+        num_windows, window_size[0] * window_size[1]
+    )
+    attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2)
+    attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+        attn_mask == 0, float(0.0)
+    )
+    return attn_mask
+
+
+def define_3d_relative_position_index(window_size: tuple[int, int, int]) -> Tensor:
+    """Build the index for the relative positional bias of sliding attention
+    windows in the context of 3D data.
+
+    Args:
+        window_size (tuple[int, int, int]): size of the sliding window
+
+    Returns:
+        Tensor: index
+    """
+    assert (
+        len(window_size) == 3
+    ), f"Data must be 3D, but window has {len(window_size)} dimension(s)"
+    # generate 3D coordinates
+    coords_z = torch.arange(window_size[0])
+    coords_h = torch.arange(window_size[1])
+    coords_w = torch.arange(window_size[2])
+    coords = torch.stack(torch.meshgrid(coords_z, coords_h, coords_w, indexing="ij"))
+    # flatten coordinates 3, Wz*Wh*Ww
+    coords_flatten = torch.flatten(coords, 1)
+    # build relative positions for each voxel ; 3, Wz*Wh*Ww, Wz*Wh*Ww
+    relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
+    # rearrange relative positions to Wz*Wh*Ww, W*Wh*Ww, 3
+    relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+    # shift to start from 0
+    relative_coords[:, :, 0] += window_size[0] - 1
+    relative_coords[:, :, 1] += window_size[1] - 1
+    relative_coords[:, :, 2] += window_size[2] - 1
+    # shift z and h relative position because the positional bias is vector
+    relative_coords[:, :, 0] *= (2 * window_size[1] - 1) * (2 * window_size[2] - 1)
+    relative_coords[:, :, 1] *= 2 * window_size[2] - 1
+    # and sum to get the index of that vector
+    relative_position_index = relative_coords.sum(-1).flatten()
+
+    return relative_position_index
+
+
+def define_3d_earth_position_index(window_size: tuple[int, int, int]) -> Tensor:
+    """Build the index for the Earth specific positional bias of sliding
+    attention windows from PanguWeather.
+    See http://arxiv.org/abs/2211.02556
+
+    Args:
+        window_size (tuple[int, int, int]): size of the sliding window
+
+    Returns:
+        Tensor: index
+    """
+    assert (
+        len(window_size) == 3
+    ), f"Data must be 3D, but window has {len(window_size)} dimension(s)"
+
+    # Index in the pressure level of query matrix
+    coords_zi = torch.arange(window_size[0])
+    # Index in the pressure level of key matrix
+    coords_zj = -torch.arange(window_size[0]) * window_size[0]
+
+    # Index in the latitude of query matrix
+    coords_hi = torch.arange(window_size[1])
+    # Index in the latitude of key matrix
+    coords_hj = -torch.arange(window_size[1]) * window_size[1]
+
+    # Index in the longitude of the key-value pair
+    coords_w = torch.arange(window_size[2])
+
+    # Change the order of the index to calculate the index in total
+    coords_1 = torch.stack(torch.meshgrid([coords_zi, coords_hi, coords_w]))
+    coords_2 = torch.stack(torch.meshgrid([coords_zj, coords_hj, coords_w]))
+    coords_flatten_1 = torch.flatten(coords_1, start_dim=1)
+    coords_flatten_2 = torch.flatten(coords_2, start_dim=1)
+    coords = coords_flatten_1[:, :, None] - coords_flatten_2[:, None, :]
+    coords = coords.permute(1, 2, 0).contiguous()
+
+    # Shift the index for each dimension to start from 0
+    coords[:, :, 2] += window_size[2] - 1
+    coords[:, :, 1] *= 2 * window_size[2] - 1
+    coords[:, :, 0] *= (2 * window_size[2] - 1) * window_size[1] * window_size[1]
+
+    # Sum up the indexes in three dimensions
+    position_index = coords.sum(dim=-1)
+
+    # Flatten the position index to facilitate further indexing
+    position_index = torch.flatten(position_index)
+
+    return position_index
+
+
+def slice_deserializer(value: str) -> slice:
+    """Retrieve a slice from a string (serialized slice).
+    Mandatory for jsonargparse to support
+    slice in yaml config file.
+
+    Args:
+        value (str): serialized slice, e.g., "slice('2019', '2021')"
+
+    Returns:
+        slice
+    """
+    assert "slice" in value, "Not a slice, can't deserialize."
+    arg_list = value[value.find("(") + 1 : value.find(")")].split(", ")
+    assert len(arg_list) <= 3, "Slice takes 3 arguments at maximum."
+    assert len(arg_list) > 0, "You must at least define the end of the slice."
+    new_arg_list = [None if arg == "None" else arg.replace("'", "") for arg in arg_list]
+    return slice(*new_arg_list)
diff --git a/weather-forecast/nwp-emulation/panguweather/uv.lock b/weather-forecast/nwp-emulation/panguweather/uv.lock
new file mode 100644
index 00000000..1e66a4b5
--- /dev/null
+++ b/weather-forecast/nwp-emulation/panguweather/uv.lock
@@ -0,0 +1,2720 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11'",
+]
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