利用1981—2023年NCEP/NCAR逐日再分析资料、NOAA逐日向外长波辐射资料、湖南省97个站点逐日气温及NCEP、CMA两家次季节-季节(sub-seasonal to seasonal,S2S)预测业务模式预报产品,使用卷积神经网络(convolutional neural network,CNN)...利用1981—2023年NCEP/NCAR逐日再分析资料、NOAA逐日向外长波辐射资料、湖南省97个站点逐日气温及NCEP、CMA两家次季节-季节(sub-seasonal to seasonal,S2S)预测业务模式预报产品,使用卷积神经网络(convolutional neural network,CNN)和迁移学习方法,建立了湖南次季节尺度气温预测模型,并与动力模式预报技巧进行对比评估。结果表明:CNN模型在不同起报时间(提前1~10 d)对月气温距平预测的空间相关系数相比两家动力模式具有显著优势,同时时间相关系数、符号一致率和均方根误差也得到一定的提高。可解释性分析显示,热带印度洋地区在深度学习模型中关注度最高,这些区域的预测因子可能是气温预测的重要可预报性来源。展开更多
Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.展开更多
The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predicta...The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predictability remains elusive. This study assessed the real-time one-month-lead prediction skill of the summer 2022 YRV heatwaves using 12operational seasonal forecast systems. Results indicate that most individual forecast systems and their multi-model ensemble(MME) mean exhibited limited skill in predicting the 2022 YRV heatwaves. Notably, after the removal of the linear trend, the predicted 2-m air temperature anomalies were generally negative in the YRV, except for the Met Office Glo Sea6 system, which captured a moderate warm anomaly. While the models successfully simulated the influence of La Ni?a on the East Asian–western North Pacific atmospheric circulation and associated YRV temperature anomalies, only Glo Sea6 reasonably captured the observed relationship between the YRV heatwaves and an atmospheric teleconnection extending from the North Atlantic to the Eurasian mid-to-high latitudes. Such an atmospheric teleconnection plays a crucial role in intensifying the YRV heatwaves. In contrast, other seasonal forecast systems and the MME predicted a distinctly different atmospheric circulation pattern, particularly over the Eurasian mid-to-high latitudes, and failed to reproduce the observed relationship between the YRV heatwaves and Eurasian mid-to-high latitude atmospheric circulation anomalies.These findings underscore the importance of accurately representing the Eurasian mid-to-high latitude atmospheric teleconnection for successful YRV heatwave prediction.展开更多
Based on ground observation data of relative humidity,the prediction performance of STNF and MIFS in each competition area during February 13-26,2024 was tested and evaluated by using two intelligent forecasting metho...Based on ground observation data of relative humidity,the prediction performance of STNF and MIFS in each competition area during February 13-26,2024 was tested and evaluated by using two intelligent forecasting methods(STNF and MIFS).The results show that STNF had better performance in forecasting relative humidity in high-altitude areas,and was suitable for fine forecasting under complex terrain.MIFS improved the short-term forecast of some low-altitude stations,but the long-term reliability was insufficient.STNF method performed better than MIFS during 0-24 h.As the prediction time extended to 24-72 h,the errors of both methods showed a systematic increase trend.STNF had higher precision,lower root mean square error and smaller mean error in most regions under the background of most weather systems,showing its superiority as a forecasting method of relative humidity.However,the precision of MIFS was slightly higher than that of STNF in Liangcheng without system background,revealing that MIFS may also be an effective option in some specific conditions.展开更多
基金primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
基金jointly supported by the National Key Research and Development Program of China (2023YFC3007503)the Joint Research Project for Meteorological Capacity Improvement (22NLTSZ002)+4 种基金the National Natural Science Foundations of China (Grant Nos.42375064, 41975102, 41730964, 42175047)the China Meteorological Administration Key Innovation Team for Climate Prediction (CMA2023ZD03)the Met Office Climate Science for Service Partnership (CSSP) China project under the International Science Partnerships Fund (ISPF)the Special Project of Innovation and Development of China Meteorological Administration (CXFZ2024J004)the China Yangtze Power Co.,Ltd.Research Project (Grant No.2423020054)。
文摘The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predictability remains elusive. This study assessed the real-time one-month-lead prediction skill of the summer 2022 YRV heatwaves using 12operational seasonal forecast systems. Results indicate that most individual forecast systems and their multi-model ensemble(MME) mean exhibited limited skill in predicting the 2022 YRV heatwaves. Notably, after the removal of the linear trend, the predicted 2-m air temperature anomalies were generally negative in the YRV, except for the Met Office Glo Sea6 system, which captured a moderate warm anomaly. While the models successfully simulated the influence of La Ni?a on the East Asian–western North Pacific atmospheric circulation and associated YRV temperature anomalies, only Glo Sea6 reasonably captured the observed relationship between the YRV heatwaves and an atmospheric teleconnection extending from the North Atlantic to the Eurasian mid-to-high latitudes. Such an atmospheric teleconnection plays a crucial role in intensifying the YRV heatwaves. In contrast, other seasonal forecast systems and the MME predicted a distinctly different atmospheric circulation pattern, particularly over the Eurasian mid-to-high latitudes, and failed to reproduce the observed relationship between the YRV heatwaves and Eurasian mid-to-high latitude atmospheric circulation anomalies.These findings underscore the importance of accurately representing the Eurasian mid-to-high latitude atmospheric teleconnection for successful YRV heatwave prediction.
文摘Based on ground observation data of relative humidity,the prediction performance of STNF and MIFS in each competition area during February 13-26,2024 was tested and evaluated by using two intelligent forecasting methods(STNF and MIFS).The results show that STNF had better performance in forecasting relative humidity in high-altitude areas,and was suitable for fine forecasting under complex terrain.MIFS improved the short-term forecast of some low-altitude stations,but the long-term reliability was insufficient.STNF method performed better than MIFS during 0-24 h.As the prediction time extended to 24-72 h,the errors of both methods showed a systematic increase trend.STNF had higher precision,lower root mean square error and smaller mean error in most regions under the background of most weather systems,showing its superiority as a forecasting method of relative humidity.However,the precision of MIFS was slightly higher than that of STNF in Liangcheng without system background,revealing that MIFS may also be an effective option in some specific conditions.
