Precipitation prediction is essential for disaster prevention,yet it still remains a challenging issue in weather and climate studies.This paper proposes an effective prediction method for summer precipitation over ea...Precipitation prediction is essential for disaster prevention,yet it still remains a challenging issue in weather and climate studies.This paper proposes an effective prediction method for summer precipitation over eastern China(PEC) by combining empirical orthogonal function(EOF) analysis with the interannual increment approach.Three statistical prediction models are individually developed for respective predictions of the three principal components(PCs) corresponding to the three leading EOF modes for the interannual increment of PEC(hereafter DY;EC).Each model is run for the month of March with two previous predictors derived from sea-ice concentration/soil moisture/sea surface temperature/snow depth/sea level pressure over specific regions.The predicted PCs are projected to the EOF modes derived from observations of DY;EC to produce a new DY;EC.This new DY;EC is then added to the observed PEC of the previous year to obtain the final predicted PEC.The spatial features of the predicted PEC are highly consistent with observations,with the anomaly correlation coefficient skill ranging from 0.32 to 0.64 during 2012-2020.The method is applied for real-time prediction of PEC in 2021.And the results indicate two rain belts located over northeastern China and the Yangtze-Huaihe River valley,respectively,although the chance for the occurrence of a "super" mei-yu with a similar intensity to that in 2020 would be rare in 2021.展开更多
基金sponsored by the National Natural Science Foundation of China [grant numbers 420881014199128342025502]。
文摘Precipitation prediction is essential for disaster prevention,yet it still remains a challenging issue in weather and climate studies.This paper proposes an effective prediction method for summer precipitation over eastern China(PEC) by combining empirical orthogonal function(EOF) analysis with the interannual increment approach.Three statistical prediction models are individually developed for respective predictions of the three principal components(PCs) corresponding to the three leading EOF modes for the interannual increment of PEC(hereafter DY;EC).Each model is run for the month of March with two previous predictors derived from sea-ice concentration/soil moisture/sea surface temperature/snow depth/sea level pressure over specific regions.The predicted PCs are projected to the EOF modes derived from observations of DY;EC to produce a new DY;EC.This new DY;EC is then added to the observed PEC of the previous year to obtain the final predicted PEC.The spatial features of the predicted PEC are highly consistent with observations,with the anomaly correlation coefficient skill ranging from 0.32 to 0.64 during 2012-2020.The method is applied for real-time prediction of PEC in 2021.And the results indicate two rain belts located over northeastern China and the Yangtze-Huaihe River valley,respectively,although the chance for the occurrence of a "super" mei-yu with a similar intensity to that in 2020 would be rare in 2021.
