GFS、CMA-GFS、WRF模式与AI气象大模型对台风"麦德姆"预报能力对比分析OA

[Objective]This study evaluates the performance differences among multiple models in forecasting the track,intensity,and structure of Typhoon Matmo(2521),aiming to provide references for operational warning and model improvement.[Method]Based on the China Meteorological Administration(CMA)best track data and European Centre for Medium-Range Weather Forecasts(ECMWF)reanalysis data(ERA5),this study compared the forecast results of two global numerical models,two regional downscaling models,and two artificial intelligence(AI)meteorological large models.The mean absolute error(MAE)and root mean square error(RMSE)were used to quantitatively evaluate track and intensity errors at 24-hour,48-hour,60-hour,and 96-hour forecast horizons.Error mechanisms were also diagnosed through analyses of thermal structure,dynamic structure,and large-scale circulation.[Result]For 24-60-hour track forecasts,the FuXi model demonstrated the highest accuracy,with a 48-h MAE of 25 km;however,the 96-hour RMSE increased to 417.4 km.The China Meteorological Administra-tion Global Forecast System(CMA-GFS)model exhibited larger errors in the short-range forecast period,but its error growth rate slowed as the forecast horizon extended;conversely,the Global Forecast System(GFS)per-formed well in the short term but tended to deviate in the long term.The Weather Research and Forecasting(WRF)models driven by the Global Forecast System(WRF-GFS)and Weather Research and Forecasting model driven by the China Meteorological Administration Global Forecast System(WRF-CMA-GFS)models had relatively large errors in the short range forecast period but demonstrated outstanding stability in the long rangeforecast pe-riod.Regarding intensity forecasts,Pangu and FuXi significantly underestimated intensity throughout the entire track and failed to capture rapid pressure drops near the coast.WRF-CMA-GFS performed best in simulating rap-id intensification and peak intensity,although its forecast quality was significantly dependent on initial conditions.[Conclusion]GFS and CMA-GFS exhibit significant differences in track and intensity forecasts.Regarding tracks,the two models show a reversal of short-term versus long-term capabilities:GFS performs better in the short term but shows a sharp increase in errors over the long term,while CMA-GFS exhibits significant short-term devia-tions but is the most stable over the long term.Intensity forecasts,however,show the opposite pattern.The Weather Research and Forecasting WRF model demonstrates outstanding long-range forecasting performance,with intensity forecasts generally outperforming track forecasts,though forecast quality remains significantly de-pendent on initial conditions.Through a multi-model comparison of Typhoon Matmo—a rapid offshore intensifi-cation case-this study reveals the differentiated performance of various models across different time scales and highlights the critical role of structural simulation.These findings provide quantitative evidence for optimizing typhoon multi-model ensembles and formulating time-scale-dependent strategies,offer directions for integrating physical constraints into AI models and optimizing initial conditions for regional models,and hold practical value for enhancing operational forecasting capabilities for rapidly intensifying typhoons near the coast.