Assessing Physics Parameterizations Using Evolutionary Computation

Abstract

Hailstorms are intense, localized weather phenomena that can severely impact agriculture, infrastructure, and property, making precise forecasting essential for risk management. The Weather Research and Forecasting (WRF) model is widely used for numerical weather prediction, offering numerous physical parameterization options to represent atmospheric processes. However, due to the large number of possible configurations, identifying the most suitable configuration is a challenge. This research uses a genetic algorithm (GA) to systematically refine WRF physics schemes for hail prediction in Central Europe, specifically for the hail events of June 2022. Within this framework, WRF configurations are treated as individuals in a population that evolves through selection, crossover, and mutation over multiple iterations. Fitness is evaluated using the F2 score. This methodology allows to evaluate more than 2.4 million possible setups improving the WRF model’s capacity to accurately represent hailstorms. This strategy provides a robust framework for testing a wide range of setups, proving its value in refining parameterizations to better forecast impactful weather phenomena.