BCC第二代模式系统产品对重庆2016年汛期降水预测的动力降尺度评估

Evaluation of dynamical downscaling prediction of BCC second-generation model system product to precipitation over Chongqing during the flood season of 2016

为了解国家气候中心第二代季节预测模式系统(BCCv2)预测产品在重庆地区的动力降尺度效果,利用WRF模式对BCCv2的2016年汛期预测数据进行动力降尺度试验,对比了动力降尺度前后预测的重庆地区汛期平均降水的差异及采用不同边界层参数化方案对降水预测的影响。试验结果表明,动力降尺度能较好地改善BCCv2对重庆地区汛期降水预测整体偏少的情况,且在降水空间分布特征上与观测更加接近。对比采用不同边界层参数化方案(MYJ、MYNN2、YSU和ACM2)后的降水预测结果可知,YSU方案偏差较小;但从整体看,各方案间差异并不明显。对环流场的偏差分析以及进一步利用松弛逼近法进行的试验表明,动力降尺度能较好地改善BCCv2对青藏高原以南和中国南部地区位势高度预测的正偏差,并能改善降尺度前其对高原南部偏西风水汽输送预测的偏弱,但对两广一带向北水汽输送预测依然较弱;边界场在环流形势预测中的偏差可能与动力降尺度预测的重庆地区汛期降水整体偏少存在联系。

To evaluate the dynamical downscaling prediction skill on the averaged precipitation in flood season over Chongqing,using WRF model based on Beijing Climate Center (BCC) second-generation seasonal prediction model system (BCCv2) product,we have conducted a dynamical downscaling prediction test based on the hindcast data over Chongqing during the flood season of 2016 from BCCv2,and compared the effect of different PBL parameterization schemes on the precipitation prediction before and after the dynamical downscaling.The results show that the dynamical downscaling can improve the original prediction in which BCCv2 predicts less precipitation than observation,and the spatial distribution of BCCv2 hindcast data has also been improved by the downscaling.Comparison of precipitation prediction results after using the four PBL parameterization schemes,i.e.MYJ,MYNN2,YSU and ACM2,indicates the YSU has a relative less prediction bias against observations compared to other schemes,although the difference among the four schemes is not very significant.Analysis on the bias of atmosphere circulation between prediction and observation and the nudging test results show that dynamical downscaling can improve the prediction of geopotential height over southern Tibet Plateau and southern China and then weaker moisture transfer by westward wind over southern Tibet Plateau,although the northward moisture transfer over Guangdong and Guangxi is still weak.The bias between boundary field and observation may be connected with the less predicted precipitation than observation over Chongqing during the flood season of 2016 in the dynamical downscaling prediction.