Impact of WRF Model Parameterization Settings on the Quality of Short-Term Weather Forecasts over Poland

Abstract

This research examines the impact of various parameterization settings within the Weather Research and Forecasting (WRF) model on the accuracy of short-term weather forecasts for Poland. The study focuses on the sensitivity of key meteorological variables—namely, air temperature, wind speed, relative humidity, and atmospheric pressure—to different combinations of physical parameterization schemes. Utilizing data from the Global Forecast System (GFS) spanning 2019 to 2022, a series of model simulations were conducted with support from the Poznań Supercomputing and Networking Center (PCSS). To assess the model’s performance across different weather stations, statistical metrics such as the mean absolute error (MAE) and root mean square error (RMSE) were employed. The findings indicate that the configuration labeled “p2” produced the most accurate forecasts for temperature, wind speed, and atmospheric pressure, achieving MAE values of 1.5 °C, 1.6 m/s, and 2 hPa, respectively. However, forecast inaccuracies were notably higher in mountainous regions, particularly regarding wind speed. These results underscore the importance of selecting appropriate parameterization settings tailored to regional characteristics, as different configurations can significantly impact the forecast accuracy, especially in complex terrains. This study contributes to the understanding of short-term weather forecasting models for Central Europe, offering potential pathways for improving localized forecast accuracy.