基于多模式集成的降水空间结构预报改进研究  

Improvement in Precipitation Spatial Structure Prediction Based on Multimodel Ensemble Forecasting Technology

作　　者：吉璐莹 智协飞[2] 季焱 朱寿鹏 罗其祥 JI Luying;ZHI Xiefei;JI Yan;ZHU Shoupeng;LUO Qixiang(Key Laboratory of Transportation Meteorology of China Meteorological Administration,Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041;Key Laboratory of Meteorological Disasters,Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044;Unit No.31153 of PLA,Nanjing 210000)

机构地区：[1]南京气象科技创新研究院中国气象局交通气象重点开放实验室,南京210041 [2]南京信息工程大学气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心,南京210044 [3]31153部队,南京210000

出　　处：《大气科学》2025年第1期257-278,共22页Chinese Journal of Atmospheric Sciences

基　　金：国家自然科学基金项目42205159;国家重点研发计划项目2017YFC1502000、2018YFC1507200;中国气象科学研究院基本科研项目2022Y017。

摘　　要：分别从“点对点”雨量检验和降水空间结构特征检验两方面对多个数值模式东亚夏季中短期逐日降水集合预报进行评估,结果表明不同模式对降水的不同方面存在不一样的预报能力。借助基于对象的诊断评估方法(method for object-based diagnostic evaluation,简称MODE)提出了基于降水对象的超级集合(Object-based Superensemble,简称OBJSUP)模型,采用观测场和预报场中降水对象空间结构的相似度来分配各个成员模式的权重,有别于利用传统“点对点”误差分析来计算权重的超级集合(Gridpoint-based Superensemble,简称GPSUP)。相比于最优单模式,两种多模式集成预报均有效地提高了中短期降水预报技巧,且OBJSUP模型整体优于GPSUP模型,主要原因在于OBJSUP模型可以较好地改进降水对象的质心位置预报。为进一步检验多模式集成模型对强降水空间结构特征的预报能力,针对2018年夏季广东一次极端强降水事件,多模式集成预报与高分辨率区域模式动力降尺度预报对比表明,多模式集成对强降水的预报不足,但对广东省逐日大雨量级降水和过程累积降水量空间分布预报较好。高分辨率区域模式对此个例中粤东地区发生的强降水具有一定的预报能力,但对广东省其他地区降水量预报偏弱。Daily precipitation ensemble forecasts provided by different numerical models are evaluated via“point-to-point”and spatial verifications.The results show that different numerical models have various forecasting capabilities based on different precipitation aspects.Based on the method for object-based diagnostic evaluation(MODE)technology,an object-based superensemble(OBJSUP)model is proposed,which employs similarities between the spatial structure of precipitation objects in the observation field and the forecast field as the weights of contributing models.This model is different from the gridpoint-based superensemble(GPSUP)that calculates weights using the traditional“point-to-point”error analysis.Compared with the optimal single model,the multimodel ensemble technologies(including OBJSUP and GPSUP)effectively improve the short-and medium-term precipitation forecasting skills,and the OBJSUP model generally performs better than the GPSUP model.This is primarily because the OBJSUP model can better improve predictions regarding the centroid location of the precipitation object.For an extreme heavy-precipitation event that occurred in Guangdong Province in the summer of 2018,the comparisons between the forecasts by OBJSUP,GPSUP,and a high-resolution regional model-Consortium for small-scale modeling(COSMO)demonstrate that OBJSUP and GPSUP underpredict the intensity of extremely heavy precipitation,but the COSMO dynamic downscaling exhibits certain ability to predict heavy precipitation occurring in eastern Guangdong.

关 键 词：基于对象的诊断评估方法 多模式集成 动力降尺度 极端降水 

分 类 号：P456[天文地球—大气科学及气象学]