Sensitivity of WRF Operational Forecasting to AIFS Initialisation: A Case Study on the Implications for Air Pollutant Dispersion

Abstract

The Artificial Intelligence Forecasting System (AIFS), recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF), represents a paradigm shift in global weather prediction by replacing traditional physically based methods with machine learning-based approaches. This study examines the sensitivity of the Weather Research and Forecasting (WRF) model to differentiate initial and boundary conditions, comparing the new AIFS with two well-established global models: IFS and GFS. The analysis focuses on the implications for air quality applications, particularly the influence of each global model on key meteorological variables involved in pollutant dispersion modelling. While overall forecast accuracy is comparable across models, some differences emerge in the spatial pattern of the wind field and vertical profiles of temperature and wind speed, which can lead to divergent interpretations in source attribution and dispersion pathways.