Anomalous Features of Extreme Meiyu in 2020 over the Yangtze-Huai River Basin and Attribution to Large-Scale Circulations

Extremely anomalous features of Meiyu in 2020 over the Yangtze-Huai River basin(YHRB)and associated causes in perspective of the large-scale circulation are investigated in this study,based on the Meiyu operational monitoring information and daily data of precipitation,global atmospheric reanalysis,and sea surface temperature(SST).The main results are as follows.(1)The 2020 YHRB Meiyu exhibits extremely anomalous characteristics,which are the most prominent since the 1980 s.The 2020 Meiyu season features the fourth earliest onset,the third latest retreat,the longest duration,the maximum Meiyu rainfall,the strongest mean rainfall intensity,and the maximum number of stations/days with rainstorm.(2)The extremely long duration of the 2020 Meiyu season lies in the farily early onset and late retreat of Meiyu in this particular year.The early onset of Meiyu is due to the earlier-than-normal first northward shift and migration of the key influential systems including the northwestern Pacific subtropical high(NWPSH)and the South Asian high(SAH)along with the East Asian summer monsoon,induced by weak cold air activities from late May to early mid-June.However,the extremely late retreat of Meiyu is because of later-than-normal second northward shift of the associated large-scale circulation systems accompanied with strong cold air activities,and extremely weak and southward located ITCZ over Northwest Pacific in July.(3)The extremely more than normal Meiyu rainfall is represented by its long duration and strong rainfall intensity.The latter is likely attributed to extreme anomalies of water vapor convergence and vertical ascending motion over the YHRB,resulting from the compound effects of the westward extended and enlarged NWPSH,the eastward extended and expanded SAH,and the strong water vapor transport associated with the low-level southerly wind.The extremely warm SST in the tropical Indian Ocean seems to be the key factor to induce the above-mentioned anomalous large-scale circulations.The results from this study serve to improve understanding of formation mechanisms of the extreme Meiyu in China and may help forecasters to extract useful large-scale circulation features from numerical model products to improve medium-extended-range operational forecasts.

Improved forecasting via physics-guided machine learning as exemplified using“21·7”extreme rainfall event in Henan

As a natural disaster,extreme precipitation is among the most destructive and influential,but predicting its occurrence and evolution accurately is very challenging because of its rarity and uniqueness.Taking the example of the“21·7”extreme precipitation event(17–21 July 2021)in Henan Province,this study explores the potential of using physics-guided machine learning to improve the accuracy of forecasting the intensity and location of extreme precipitation.Three physics-guided ways of embedding physical features,fusing physical model forecasts and revised loss function are used,i.e.,(1)analyzing the anomalous circulation and thermodynamical factors,(2)analyzing the multi-model forecast bias and the associated underlying reasons for it,and(3)using professional forecasting knowledge to design the loss function,and the corresponding results are used as input for machine learning to improve the forecasting accuracy.The results indicate that by learning the relationship between anomalous physical features and heavy precipitation,the forecasting of precipitation intensity is improved significantly,but the location is rarely adjusted and more false alarms appear.Possible reasons for this are as follows.The anomalous features used here mainly contain information about large-scale systems and factors which are consistent with the model precipitation deviation;moreover,the samples of extreme precipitation are sparse and so the algorithm used here is simple.However,by combining“good and different”multi models with machine learning,the advantages of each model are extracted and then the location of the precipitation center in the forecast is improved significantly.Therefore,by combining the appropriate anomalous features with multi-model fusion,an integrated improvement of the forecast of the rainfall intensity and location is achieved.Overall,this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability,and provides a reference for the deep fusion of physics and artificial intelligence methods to improve intense rain forecast.