苏皖地区强降水过程双雨带漏报原因分析

Analysis for the Cause of Missing-prediction of Double Rain Belt in Heavy Precipitation Process in Jiangsu and Anhui Areas

基于降水量观测数据、ECMWF模式降水预报数据和ERA5再分析数据,研究了2022年3月19—20日苏皖地区一次降水过程中双雨带漏报个例,并对比分析了一次双雨带成功预报个例.结果表明:①漏报个例的北雨带中,16个站降水量进入近30年3月日降水量前10名,两个站降雨量刷新3月份的历史极值,是一次极端性较强的降水过程.②ECMWF模式未能准确预报北雨带冷平流,未反映实况中出现的对流活动,且预报的对流层低层相对湿度始终偏小,两个原因直接导致北雨带降水量预报值显著偏少.③成功预报个例中,过程开始阶段,ECMWF模式有700hPa高湿区预报范围偏大、850hPa高湿区预报位置偏南的情况,以后逐渐调整为与实况一致,较好地预报了苏皖地区对流层低层湿度条件,佐证了湿度预报偏差是造成雨带预报偏差的重要原因.

Based on precipitation observation data,ECMWF model precipitation prediction data and ERA5 reanalysis data,this paper studies a case of missing-forecast of double rain belt in a heavy precipitation process in Jiangsu and Anhui areas from March 19 to 20,2022,and compares a case of successful prediction of double rain belt.The conclusions are as follows:①In the north rain belt of the missing-prediction case,the precipitation of 16 stations ranks the top 10 in the daily precipitation in March of recent 30 years,and the precipitation of 2 stations refreshes the historical extreme value in March,which is a precipitation process with strong extremes.②ECMWF model fails to accurately predict the cold advection in the northern rain belt,which leads to the model not reflecting the convection activities in the actual situation.At the same time,the relative humidity in the lower troposphere predicted by the model is always lower than reanalysis data.The above two reasons directly lead to the significantly low forecast value of precipitation in the northern rain belt.③In successful-prediction case,at the beginning of the process,the prediction range of 700 hPa high humidity area by ECMWF model is too large,and the prediction position of 850 hPa high humidity area by ECMWF model is southerly.Later,it is gradually adjusted to be consistent with the actual situation.Therefore,the model can better predict the humidity conditions in the lower troposphere in Jiangsu and Anhui areas,which proves that the humidity prediction deviation is an important reason for the rain belt prediction deviation.