0. ReadMe

Before you start using the esd package

We highly recommend reading the esd.pdf user guide. This guide contains a comprehensive description of all the functions included in the esd package, along with several examples to help you get started.

If you are unable to download the help file, you can easily generate it yourself using the following command in your terminal:

$ R CMD Rd2pdf esd

where esd refers to either a symbolic link or the full path to the esd folder where the files are stored.

This command will generate a PDF file that provides detailed documentation of the package's functions and usage.

Reading the MET Report 11/2015 - The Empirical-Statistical Downscaling tool & its visualisation capabilities

We also highly recommend reading the 'esd' - The Empirical-Statistical Downscaling tool & its visualisation capabilities met report. The MET Report 11/2015 provides a comprehensive overview of the esd R package, highlighting its purpose, key features, and its alignment with open science principles.

The MET Report 11/2015 provides a comprehensive overview of the esd R package, highlighting its purpose, key features, and its alignment with open science principles. Below is a summary of the report's main points:

• Purpose of the esd Package:
  The report introduces the esd package as a powerful tool for climate data processing and empirical-statistical downscaling. It is specifically designed to handle both climate station and field data, offering streamlined workflows for tasks such as subsetting, aggregation, anomaly computation, regridding, and downscaling. These capabilities make it an essential resource for climate data analysis and modeling.

• Key Features and Applications:
  The esd package integrates metadata directly into data objects, ensuring traceability, reproducibility, and transparency in climate data analysis. It adheres to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), promoting high-quality scientific practices. The package supports a wide range of applications, including the downscaling of global climate model projections (e.g., CMIP5/CMIP6) and the analysis of historical and observational climate data, enabling users to address a variety of research needs.

• Commitment to Open Science:
  The report underscores the package's open-source development approach, which fosters reproducibility, collaboration, and transparency. By encouraging users to adapt and extend the package, the esd project aligns itself with the broader goals of open science, enabling the climate science community to share tools, methods, and insights more effectively.

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Key Changes and Revisions in the esd R Package

While the esd package is still evolving, here are examples of the types of changes commonly made:

1. New Features:

   • Addition of new functions for downscaling, data visualization, or metadata handling.
   • Integration of support for CMIP6 data or new observational datasets.

2. Enhancements:

   • Improved performance of core functions like subset, PCA, or DSensemble.
   • Expanded metadata handling to support additional attributes or formats.

3. Bug Fixes:

   • Fixes for errors in handling specific data structures (e.g., issues with trajectory objects or field objects).
   • Resolved compatibility issues with newer versions of R or dependencies like ncdf4.

4. Documentation:

   • Updated examples for functions in the manual.
   • Migration to Roxygen2 for inline documentation.

5. Code Refactoring:

   • Simplified or optimized code for better readability and maintainability.
   • Removal of deprecated functions or unused code.

6. New Datasets:

   • Addition of new built-in datasets for testing and demonstrations (e.g., additional station data or reanalysis datasets).

7. Pipeline Enhancements:

   • Improved workflows for downscaling CMIP5/CMIP6 data.
   • Enhanced support for gridded data formats, such as NetCDF.

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