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Evaluation of Weather Forecasts from AI Big Models over East Asia
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摘要: 针对人工智能气象大模型的500 hPa位势高度、2 m气温、10 m风速、降水以及热带气旋路径等, 从定性和定量两个角度进行评估。结果表明:从定性角度出发, FuXi、Pangu和GraphCast 3个大模型均会响应热带异常加热, 其中Pangu与GraphCast响应强度接近, FuXi响应较弱。从定量角度出发, FuXi整体展现出更高的预报能力, 其最大可用预报日数超过9.75 d, Pangu和GraphCast分别为8.75 d和8.5 d。在2 m气温预报中, FuXi的时间异常相关系数为0.48~0.91, Pangu和GraphCast分别为0.43~0.91和0.38~0.83。此外, 采用TS(threat score)评分对FuXi和GraphCast降水预报进行评估, FuXi在晴雨、小雨和中雨预报中更具优势, 其预报技巧分别为0.22~0.41、0.15~0.24和0.06~0.22, GraphCast在大雨预报中展现更强能力。针对2019年7月29日华北暴雨和2020年8月16—17日乐山暴雨两次极端降水个例进行分析, FuXi和GraphCast均可提前8 d预报降水的空间分布, 但在降水量级预报中存在偏差, 随着预报时效减小, 偏差也逐渐减小。在热带气旋路径预报中, Pangu展现更高精度。Abstract: Reliable medium-range weather forecasts are crucial for both science and society. Although weather predictions primarily rely on numerical weather models, the artificial intelligence (AI) weather big models have shown potential for accurate weather forecasts with reduced computational costs. However, prediction skills of big models remain uncertain, particularly in East Asia, which limits further application of weather AI models. To systematically evaluate predictive capabilities of Pangu, FuXi, and GraphCast models over East Asia, their prediction results are focusing on 500 hPa geopotential height, 2 m air temperature, 10 m wind speed, precipitation, and track of tropical cyclones.ECMWF reanalysis V5 (ERA5) datasets are utilized to provide the initial conditions for big models, and to assess their predictive skill. Additionally, precipitation observations and China Meteorological Administration tropical cyclone datasets are utilized to access big models as well. FuXi shows the highest forecasting skills among 3 big models for 500 hPa geopotential height. The forecast from FuXi is reliable for up to 9.75 days, while the forecasts from Pangu and GraphCast are reliable for 8.75 days and 8.5 days, respectively. For 2 m air temperature forecasting, FuXi presents higher skills with an averaged temporal anomaly correlation coefficient (TCC) ranging from 0.48 to 0.91, while TCCs of Pangu and GraphCast are 0.43-0.91 and 0.38-0.83, respectively. Among 3 models, only FuXi and GraphCast provide precipitation forecasts. FuXi shows higher prediction skill compared to GraphCast in forecasting precipitation, light rain, and moderate rain; however, GraphCast has advantage in heavy rain forecast. As the lead time increases, the threat scores (TSs) of FuXi for rainfall, light rainfall and moderate rainfall are 0.22-0.41, 0.15-0.24 and 0.06-0.22, respectively. The model demonstrates higher skill in the northern and southeastern regions of China. For predicting the track of cyclones, Pangu model demonstrates superior predictive skill. As the lead time increases from 6 hours to 240 hours, biases of Pangu's prediction track increase from 17.5 km to 1850 km.The study focuses on the prediction skill of various AI big models through TCC, spatial anomaly correlation coefficient, and TS. Generally, the performance of FuXi is superior for most elements. And reasonable evaluation of AI model is helpful for the development of AI models.
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图 1 冬季500 hPa位势高度对黑色实线所示区域内0.1 K的异常热带加热在不同积分时间(5 d、10 d和20 d) 的响应
Fig. 1 500 hPa geopotential height responsed to tropical anomalous heating of 0.1 K within region outlined by the black line integrated for 5 d, 10 d and 20 d in winter
图 2 东亚区域500 hPa位势高度预报空间异常相关系数随时效变化
Fig. 2 Spatial anomaly correlation coefficients of 500 hPa geopotential height forecast over East Asia
图 3 不同时效(5 d和10 d) 500 hPa位势高度的时间异常相关系数和标准化均方根误差
Fig. 3 Temporal anomaly correlation coefficients and normalized root mean square errors of 500 hPa geopotential height forecast over East Asia at lead times of 5 d and 10 d
图 4 不同时效(5 d和10 d) 2 m气温与10 m风速预报偏差
Fig. 4 Error distributions of 2 m temperature and 10 m wind speed forecasts at lead times of 5 d and 10 d
图 5 不同时效(5 d和10 d) 2 m气温预报的时间异常相关系数和标准化均方根误差
Fig. 5 Temporal anomaly correlation coefficients and normalized root mean square errors of 2 m temperature forecast at lead times of 5 d and 10 d
图 6 不同时效(5 d和10 d) 10 m风速预报的时间异常相关系数和标准化均方根误差
Fig. 6 Temporal anomaly correlation coefficients and normalized root mean square errors of 10 m wind speed forecast at lead times of 5 d and 10 d
图 7 FuXi和GraphCast晴雨、小雨、中雨和大雨的TS评分随预报时效变化
Fig. 7 TS of rainfall, light rainfall, moderate rainfall, and heavy rainfall forecast based on FuXi and GraphCast at different lead times
图 8 FuXi和GraphCast不同时效预报与观测强降水(大于7.5 mm)
Fig. 8 Heavy rainfall (greater than 7.5 mm) forecast and observation from FuXi and GraphCast at different lead times
图 9 基于台站观测数据的2019年7月29日华北暴雨总降水量与2020年8月16—17日乐山暴雨总降水量
Fig. 9 Heavy rainfall observations of North China on 29 Jul 2019 and of Leshan from 16 Aug to 17 Aug in 2020
图 10 2019年7月29日华北暴雨FuXi和GraphCast不同起报时刻降水预报偏差
Fig. 10 Rainfall forecast biases initiated at different time based on FuXi and GraphCast during heavy rainfall of North China on 29 Jul 2019
图 11 2020年8月16—17日乐山暴雨FuXi和GraphCast不同起报时刻降水预报偏差
Fig. 11 Rainfall forecast biases initiated at different time based on FuXi and GraphCast during heavy rainfall of Leshan from 16 Aug to 17 Aug in 2020
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