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Timo Schmid
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start new tutorial
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‎doc/user-guide/climada_hazard_HailEurope.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Hazard: Hail in Europe\n",
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"\n",
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"\n",
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"Auth: Timo Schmid \n",
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"Date: March 2026\n",
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"\n",
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"This notebook will give a quick tour of the hail hazard data on the climada data API and its usage for impact modelling"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data overview and usage\n",
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"\n",
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"The climada data API contains hail hazard data, based high-resolution climate simulations with the COSMO model and the HAILCAST hail diagnostic, conducted within the [scClim](https://scclim.ethz.ch) research project. \n",
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"The data has been spatially perturbed to create a small probabilistic event set of 330-year duration, as described in detail in [Schmid et al. (2026)](https://doi.org/10.1016/j.cliser.2025.100630)\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import packages\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"from climada.util.api_client import Client\n",
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"from climada.entity import ImpactFuncSet, ImpactFunc\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#helper functions\n",
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"def get_emanuel_impf(v_thresh=20, v_half=60, scale=1e-3,power=3,\n",
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" impf_id=1, intensity=np.arange(0, 70, 1),\n",
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" intensity_unit='mm',haz_type='HL'):\n",
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" \"\"\"\n",
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" Init TC impact function using the formula of Kerry Emanuel, 2011:\n",
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" https://doi.org/10.1175/WCAS-D-11-00007.1\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" impf_id : int, optional\n",
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" impact function id. Default: 1\n",
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" intensity : np.array, optional\n",
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" intensity array in intensity_unit.\n",
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" v_thresh : float, optional\n",
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" first shape parameter\n",
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" v_half : float, optional\n",
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" second shape parameter\n",
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" scale : float, optional\n",
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" scale parameter, linear scaling of MDD.\n",
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" 0<=scale<=1. Default: 1.0\n",
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" power : int, optional\n",
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" Exponential dependence. Default to 3 (as in Emanuel (2011))\n",
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"\n",
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" Raises\n",
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" ------\n",
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" ValueError\n",
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"\n",
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" Returns\n",
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" -------\n",
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" impf : ImpactFunc\n",
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" Impact function object\n",
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" \"\"\"\n",
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"\n",
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" #Get the function values. Note that invalid input parameters are checked\n",
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" # within get_emanuel_vals(). (e.g. V_half <= V_thresh)\n",
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" v_temp = get_emanuel_vals(intensity,v_thresh,v_half,scale,power)\n",
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"\n",
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" impf = ImpactFunc(haz_type=haz_type, id=impf_id,intensity=intensity,\n",
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" intensity_unit=intensity_unit,name='Emanuel-type')\n",
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" impf.paa = np.ones(intensity.shape)\n",
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" impf.mdd = v_temp\n",
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" return impf\n",
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"def get_emanuel_vals(intensity,v_thresh=20, v_half=60, scale=1e-3,power=3):\n",
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" \"\"\"Get the Emanuel-type impact function values for a given intensity array\"\"\"\n",
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"\n",
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" #Check whether the input parameters are valid\n",
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" if v_half <= v_thresh:\n",
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" raise ValueError('Shape parameters out of range: v_half <= v_thresh.')\n",
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" if v_thresh < 0 or v_half < 0:\n",
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" raise ValueError('Negative shape parameter.')\n",
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" if scale > 1 or scale <= 0:\n",
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" raise ValueError('Scale parameter out of range.')\n",
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"\n",
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" #Calculate the impact function values\n",
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" v_temp = (intensity - v_thresh) / (v_half - v_thresh)\n",
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" v_temp[v_temp < 0] = 0\n",
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" v_temp = v_temp**power / (1 + v_temp**power)\n",
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" v_temp *= scale\n",
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" return v_temp\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'property': 'data_source', 'mandatory': True, 'description': 'radar (Radar-based daily hail hazard data), model (330-year probabilistice event set)'}\n",
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"{'property': 'radar_variable', 'mandatory': False, 'description': 'MESHS (Maximum Expected Severe Hail Size), POH (Probability Of Hail)'}\n",
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"{'property': 'climate_scenario', 'mandatory': False, 'description': 'REF (Reference period (2011-2021)), PGW (degree PGW scenario)'}\n",
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"{'property': 'country_iso3alpha', 'mandatory': False, 'description': 'ISO3 alpha code for country'}\n",
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"{'property': 'res_km', 'mandatory': False, 'description': 'spatial resolution in kilometers'}\n"
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]
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}
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],
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"source": [
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"#Get an overview of the properties of the 'hail' data type on the climada API\n",
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"\n",
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"client = Client()\n",
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"data_types = client.list_data_type_infos()\n",
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"dtf = pd.DataFrame(data_types)\n",
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"\n",
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"\n",
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"#Select the row with data_type 'hail' and print the 'properties' column\n",
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"hail_properties = dtf[dtf['data_type'] == 'hail'].iloc[0]['properties']\n",
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"for row in hail_properties:\n",
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" print(row)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2026-03-07 11:54:21,146 - climada.hazard.io - INFO - Reading C:\\Users\\timschmi\\climada\\data\\hazard\\hail\\hail_FRA_REF\\v2\\hail_FRA_ref.h5\n"
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]
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}
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],
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"source": [
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"#Load the hazard\n",
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"\n",
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"haz_ref = client.get_hazard(\n",
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" \"hail\",\n",
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" properties={\n",
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" \"data_source\": \"model\",\n",
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" \"climate_scenario\": \"REF\",\n",
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" \"country_iso3alpha\": \"FRA\",\n",
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" },\n",
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")\n",
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"\n",
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"haz_fut = client.get_hazard(\n",
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" \"hail\",\n",
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" properties={\n",
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" \"data_source\": \"model\",\n",
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" \"climate_scenario\": \"PGW\",\n",
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" \"country_iso3alpha\": \"FRA\",\n",
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" },\n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Plot overview of both present and future hazard\n",
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"\n",
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"haz_ref"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Set vulnerability functions as described in Schmid et al (2026); Table B.1\n",
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"\n",
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"params_PAA = {'v_thresh': 17.8,'v_half': 39.9,'scale': 1,'power': 4.95}\n",
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"params_MDD = {'a':0.064,'b':3.33e-3}\n",
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"\n",
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"impf_setPAA = ImpactFuncSet([get_emanuel_impf(**params_PAA)])\n",
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"\n",
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"\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "climada_6",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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