A skillful method for precipitation prediction over eastern China
Yanyan Huang, Huijun Wang, Peiyi Zhang
Abstract
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_PEC). 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_PEC to produce a new DY_PEC. This new DY_PEC 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. 摘要 降水作为全球水循环的重要组成, 与人们的生产生活密切相关. 有效的降水预测对于防灾减灾, 以及经济的可持续发展至关重要.然而, 由于影响降水过程的复杂性, 当前降水预测还存在诸多挑战. 针对我国东部夏季降水, 我们提出年际增量结合经验正交分解的新统计预测方法. 首先计算降水年际增量的主模态, 然后针对主模态时间序列构建预测模型, 用预测的时间序列叠加观测空间场得到重构的降水年际增量, 最后将预测的降水年际增量加上前一年的观测降水, 得到最终预测的东部降水. 针对1990–2020年的东部夏季降水, 该方法在每年三月构建的预测模型预测效果稳定, 对于2021的实时预测亦展现了可观的预测水平。