@misc{10481/108303,
year = {2025},
month = {10},
url = {https://hdl.handle.net/10481/108303},
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.},
organization = {MICIU/AEI/10.13039/501100011033 - FEDER, EU (PID2021-126401OB-I00)},
publisher = {MDPI},
keywords = {WRF},
keywords = {IFS},
keywords = {AIFS},
title = {Sensitivity of WRF Operational Forecasting to AIFS Initialisation: A Case Study on the Implications for Air Pollutant Dispersion},
doi = {10.3390/earth6040132},
author = {Arasa Agudo, Raúl and García-Valdecasas Ojeda, Matilde and Picanyol Sadurní, Miquel and Codina Sánchez, Bernat},
}