江南初夏降水集中期降水变化及其统计预测模型

利用1979-2018年252个台站逐日降水资料和NCEP/NCAR再分析等资料,分析了江南初夏降水集中期降水(Rainfall in the Precipitation Concentrated Period,RPCP)的时空演变及其环流特征,并通过寻找其前期物理因子建立统计预测模型。结果表明:(1) 6月10-29日为江南初夏降水集中阶段,其降水呈现全区一致的空间分布结构。20世纪90年代初—21世纪初江南地区为多雨期,在此期间RPCP强度存在3~5 a的周期振荡;(2)与异常偏多的RPCP显著相关的热带海温异常从EP El Ni1o转为CP La Ni1a,即RPCP异常偏强通常发生在EP El Ni1o衰减阶段,随后将出现CP La Ni1a发展事件;(3)前期冬春季ENSO海温信号、赤道中太平洋SLP异常及印度洋—海洋性大陆SLP趋势均可通过增强菲律宾海反气旋对RPCP异常变化产生影响。基于这3个物理因子建立统计预测模型,预测与观测结果相关系数达0.63,说明该模型可为江南初夏降水的季节性预测提供有用工具。

The Variations of Rainfall in Early Summer Precipitation Concentrated Period over the South of Yangtze River Valley and its Statistical Prediction Model

Based on the daily precipitation data of 252 stations and NCEP/NCAR reanalysis datasets from 1979 to 2018, this paper analyzes the temporal-spatial evolution and related circulation characteristics of the rainfall in early summer precipitation concentrated period(RPCP) over the South of Yangtze River Valley, and the statistical prediction model to predict the RPCP, is established by looking for RPCP’s previous physical factors. The results show that June 10 to 29 is the period of precipitation concentration in early summer of the South of Yangtze River Valley, and the rainfall presents a same spatial distribution structure in the whole region. From the early 1990 s to the early 21 st century, there was a rainy period in this region, during which the RPCP had a prominent 3~5 a periodical oscillation. In addition, tropical SST anomalies significantly associated with the intensified RPCP change from EP El Ni1 o to CP La Ni1 a, that is, anomalous increased RPCP usually occur in the decaying phase of EP El Ni1 o and followed by CP La Ni1 a development events. Finally, in early winter and spring, the ENSO SST signal, the SLP anomalies in the equatorial central pacific and the SLP tendency in the Indian Ocean-Maritime Continent can affect PRCP anoamalies. Based on the three physical factors,the statistical model is established, and its correlation coefficient between the prediction and the observation reaching 0.63. This indicates that this physical-based model may provide a useful tool for the seasonal prediction of the early summer precipitation over the South of Yangtze River Valley.