Work

Comparative evaluation of data‐driven weather forecast models performance for medium‐ to extended‐range weather forecasting and tropical cyclone genesis in 2024

Year: 2026

Type: article

Abstract: This study examines the performance of state‐of‐the‐art artificial intelligence (AI) weather forecasting models in 2024. For tropical cyclone genesis forecasts in the western North Pacific, AI models generally perform better than the physics‐based European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Furthermore, higher‐resolution AI models, such as a fine‐tuned version of Pangu‐Weather, improved anomaly correlation scores and Fengwu‐GHR produced more realistic synoptic‐scale structures for medium‐range forecasts compared with lower‐resolution versions. Moreover, certain AI models could match or outperform ECMWF IFS on daily rainfall forecasts over Hong Kong, especially for heavier rainfall events. These findings highlight the potential of AI‐based methods for operational applications on high‐impact weather.

Source: Weather

DOI: 10.1002/wea.70043

Authors: C. H. Ho, Marco Y. T. Leung, Y. H. He, P. W. Chan

Institution: Hong Kong Observatory

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $1,880