Health-relevant Heat Indices for Urban Areas: A Machine Learning Approach with Downscaled Climate Data and City Measurement Networks

In a warming climate, Furthermore, with an increasing world population over the course of this century, more and more people will be at risk. Additionally, urbanization is expected to continue increasing, and due to the urban heat island (UHI) effect, heat will be intensified in the most densely populated areas.  In this project, we investigate the severity of hot spells with the help of health-relevant heat indices, namely wet bulb temperature, the universal thermal climate index (UTCI) and the amount of tropical nights, among others. We process reanalysis data from ERA5-Land from 1950 onwards and simulation data from the downscaled EURO-CORDEX simulations for various climate scenarios until 2100 to generate these indicators for Europe at a resolution of 0.1°. We train a shallow machine learning model (XGBoost) to downscale reanalysis and simulation data to the city level at a resolution of 100m for select European cities. For model validation, temperature series from 12 European cities’ urban measurement networks of are used. The indicators will be applied to four pilot cities (Oslo, Bern, Lyon and Naples), as part of the EU project healthRiskADAPT under the framework of Horizon Europe. In a subsequent phase, weather forecast data may be downscaled with the same algorithm to predict urban heatwaves. Furthermore, advanced modeling techniques such as Weather Research and Forecasting (WRF) models or computational fluid dynamics (CFD) may be applied to better understand the compound effects of heat and pollution in cities.