Comparative Analysis of Precipitation Forecasts between the ECMWF Artificial Intelligence System (AIFS) and Its Integrated Forecast System (IFS)

作者：Pan Liujie, Hongfang Zhang, Mian Liang, P. R. Li, Congqi Cao, Lili Du, Jing Liu · 发表于：Weather and Forecasting · 年份：2025 · DOI：10.1175/waf-d-24-0227.1 · 被引用次数：7 · 研究领域：Advanced Computational Techniques and Applications、Meteorological Phenomena and Simulations

Abstract This study utilizes precipitation observations from 2415 stations in China and ECMWF’s ERA5 reanalysis data to conduct a detailed comparative analysis of the precipitation forecasting performance of ECMWF’s Artificial Intelligence Forecasting System (AIFS) and Integrated Forecasting System (IFS). The main conclusions are as follows: 1) The RMSE of AIFS’s 1–5-day precipitation forecast is significantly lower than that of IFS. The 120-h RMSE of IFS is roughly equivalent to the 24-h RMSE of AIFS. The mean error (ME) of AIFS precipitation forecasts is generally better than that of IFS. IFS’s cumulative rainfall aligns better with observations in southern China and the Tibetan Plateau. 2) In southern China and the Tibetan Plateau, neither AIFS nor IFS can accurately represent the observed variation characteristics of the standard deviation (STD) of precipitation. AIFS shows less fluctuation in STD across the three regions, underestimates precipitation amounts, and has a weaker ability to forecast extreme values. 3) AIFS consistently overpredicts the frequency of light precipitation, leading to significantly lower equitable threat scores (ETSs). However, it shows better performance in terms of threat score (TS) and ETS for heavy precipitation events exceeding 25.0 mm. In terms of the stable equitable error in probability space (SEEPS), the IFS demonstrates superior performance. 4) The integrated water vapor transport (IWVT) and its STD forecasted by AIFS are relatively wea...