不同资料同化对四川一次暴雨过程数值模拟的对比分析.pdf

暴雨灾害TORRENTIAL RAIN AND DISASTERSVol.42 No.3Jun.2023第42卷 第3期2023年6月Comparative analysis of simulation of a heavy rain in SichuanProvince with different data assimilationWEN Ying1,2,FENG Caiyun1,4,YU Lian3(1.Chengdu University of Information Technology,Plateau Atmosphere and Environment Key Laboratory of Sichuan Province,Chengdu 610225;2.Ocean University of China,Qingdao 266100;3.Institute of Plateau Meteorology,CMA,Heavy Rainand Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province,Chengdu 610072;4.Key Laboratory for Cloud Physics of China Meteorological Administration,Beijing 100081)Abstract:In order to evaluate the influence of the assimilation of different observational data such as conventional ground observations,radiosonde and radar radial wind on the meso-scale model of heavy rain forecast in Sichuan Province,a heavy rainstorm process in Sichuanfrom 14 to 18 June,2020 is used as an example.Using Weather Research And Forecasting(WRF)model and Grid Point Statistical Interpolation(GSI)assimilation system,we assimilated the conventional and radar data respectively and simultaneously，and compared the results ofthree assimilation experiments qualitatively and quantitatively.The results show that the WRF model combined with the GSI assimilation system can simulate the rainstorm well.For the 21-h cumulative precipitation forecast,assimilating conventional observation data can better improve the trend of rain belt and the fall area of the rainstorm.The assimilated radar data showed better performance in precipitation intensity,rainstorm range and the light to moderate rain forecast,The average ETS score of the light to moderate rain was increased by 0.05.Assimilation of both the conventional observation and radar data improved ETS,POD,FAR and BIAS scores for heavy rain.For the12-h cumulativeprecipitation forecast,the simulation performance of the precipitation trend is the best with the assimilation of radar data,and the experiment文影,封彩云,余莲.2023.不同资料同化对四川一次暴雨过程数值模拟的对比分析J.暴雨灾害,42(3):260-272.WEN Ying,FENG Cai-yun,YU Lian.2023.Comparative analysis of simulation of a heavy rain in Sichuan Province with different data assimilation J.TorrentialRain and Disasters,42(3):260-272(in Chinese).doi:10.12406/byzh.2022-120不同资料同化对四川一次暴雨过程数值模拟的对比分析文影1,2，封彩云1,4，余莲3(1.成都信息工程大学 高原大气与环境四川省重点实验室，成都 610225；2.中国海洋大学，青岛 266100；3.中国气象局成都高原气象研究所 高原与盆地暴雨旱涝灾害四川省重点实验室，成都 610072；4.中国气象局云雾物理环境重点开放实验室，北京 100081)摘要：为评估中尺度模式同化常规地面、探空和雷达径向风等不同观测资料对四川暴雨预报性能的影响，以2020年6月1418日四川一次暴雨过程为例，利用WRF(Weather Research And Forecasting)模式和GSI(Grid Point Statistical Interpo-lation)同化系统，对常规观测资料和雷达资料分别和同时进行循环同化，开展数值模拟试验，定性和定量地对比分析三组同化试验的降水模拟效果。结果表明：WRF模式结合GSI同化系统对此次暴雨有较好的模拟。针对21 h累积降水模拟，同化常规观测资料较好地改善了暴雨雨带的走向和暴雨的落区；同化雷达资料对降水强度、暴雨范围和小到中雨预报表现较好，小到中雨的ETS评分平均提升0.05；同时同化两种资料对大雨的ETS、POD、FAR和BIAS评分都有改善。针对半日累积降水预报，同化雷达资料对降水趋势的模拟表现最好，同化包括雷达资料的试验对降水落区有较好的改善。针对3 h累积降水预报，同化试验对降水演变均有改善，同化雷达资料表现最好。模式对夜间降水的模拟普遍优于白天，同化试验的改善时段也主要集中在夜间，同化常规资料表现显著。综合21 h、半日和3 h累积降水预报评分结果，同时同化多种资料的降水预报效果不绝对优于仅同化一种资料的降水预报，但至少优于一种资料同化的降水预报评分结果。关键词：资料同化；数值模拟；常规观测资料；雷达资料；暴雨中图法分类号：P435文献标志码：ADOI:10.12406/byzh.2022-120收稿日期：2022-06-21；定稿日期：2023-03-01资助项目：第二次青藏高原综合科学考察项目(2019QZKK010401)；国家自然科学基金项目(42075019)；中国气象局云雾物理环境重点开放实验室开放课题(2020Z007)；中国气象局创新发展专项(CXFZ2022J057)；四川省科技计划项目(2022YFS0545)第一作者：文影，主要从事数值天气预报方法研究。E-mail:通信作者：封彩云，主要从事灾害性天气、云降水物理及数值模拟研究。E-mail:第3期文影，等：不同资料同化对四川一次暴雨过程数值模拟的对比分析involving the assimilation of radar data has better improvement on the precipitation area.For the 3-h cumulative precipitation forecast,theassimilation experiment improved the precipitation evolution,and the assimilation of radar data showed the best performance.The simulationof precipitation at night was generally better than that in the daytime,and the improvement period of assimilation experiment was mainly concentrated in the nighttime,and the assimilation of conventional observation data showed significant performance improvement.Based on thescores of 21-h,12-h and 3-h cumulative precipitation forecast,the precipitation forecast effect of assimilating multiple data is not absolutely better than those of assimilating only one data,but the assimilation of multiple data can achieve better scores than those of assimilating only one data.Key words:data assimilation;numerical simulation;conventional observations;radar data;rainstorm引言在全球气候变暖的大背景下，暴雨等灾害性降水天气发生的频率逐年增加(周昊，2012)。暴雨和暴雨引发的洪涝、滑坡、泥石流等次生自然灾害，对人们的生产生活以及当地的发展造成了严重影响(樊运晓等，2000)。利用数值模式模拟分析暴雨是研究和预报暴雨的重要手段(罗雨等，2010；沈菲菲等，2020)。然而，大气是一个高度非线性系统，数值模式的预报结果对初始场误差十分敏感，并且在模式积分时，这种误差将随着时间累积，最终导致预报结果失真(李泽椿等，2014；廖文超等，2016)。资料同化技术是利用某一时间窗内(一般612 h)所有可利用的大气相关信息，结合最优统计方法，产生一个更接近实况的大气状态描述，为数值天气预报模式提供初值(段华等，2015)。目前常用的同化系统主要包括WRF-DA、WRF-ETKF、GSI、MM5模式的三维和四维变分资料同化系统、GRAPES同化系统、SSI同化系统等。同化方法的使用有效地改善了暴雨的预报。四川地处我国西南内陆地区，西临高耸的青藏高原和横断山脉，南接山峦重叠、丘陵起伏的云贵高原，北依秦岭、大巴山脉，东连湘鄂西山地。由于四川地区复杂多样的地形地貌和天气系统，同时河流纵横，使得暴雨预报极其困难。因暴雨造成的次生灾害，如滑坡、山洪、泥石流等，造成的损失每年数以亿计，其中对长江流域的影响巨大(陈鹏等，2015；廖文超等，2016；王佳津等，2019)。针对四川暴雨的预报，前人采用了 WRF 3D-Var、WRF 4D-Var、GRAPES 3D-Var 和SSI 3D-Var等同化方法对其进行了研究(张利红，2006；