A Fine‐Tuned Pangu Weather Model and Its Performance Based on an Operational Framework in South China

Data‐driven weather models have shown the potential to match the accuracy of state‐of‐the‐art numerical weather predictions (NWPs). However, existing data‐driven forecasting models still have limitations in operational applications. For example, most of them are predominantly trained via fifth‐generation climate reanalysis data (ERA5). However, in actual forecasting operations, the models are usually initiated by analysis fields instead of reanalysis data; this leads to a mismatch between the training data used by machine learning (ML) forecasting models and the actual operational data. To address this issue, we attempt to fine‐tune the data‐driven model with the initiation fields in operation. This study first develops a fine‐tuned Pangu Weather Model (PGW) by integrating forecasting system (IFS) analysis data from 2021 to 2022 and conducts a comprehensive evaluation of its performance. By comparing the fine‐tuned version (PGW_O) with the public version (PGW_P) against IFS models with different resolutions (IFS_L at 0.25° and IFS_H at 0.1°), this research highlights advancements in data‐driven forecasting methodologies. The models are tested on data from South China, a region with dense meteorological observation networks, over a three‐month period, encompassing a detailed case study of Tropical Cyclone Haikui (2023). The findings show that with the forecast activity (FA) level comparable to PGW_P, PGW_O significantly reduces the root mean square error (RMSE) and mean error (ME) across upper atmospheric variables and demonstrates superior accuracy in predicting surface elements. The operational relevance of these models is evaluated through both ERA5 reanalysis and surface observations, revealing that fine‐tuning with IFS data enhances PGW compatibility and forecasting precision, particularly for severe weather events.