华南暴雨区域集合预报中不同同化方案的影响试验研究  

EXPERIMENTAL STUDY ON INFLUENCE OF DIFFERENT ASSIMILATION SCHEMES ON ENSEMBLE FORECAST OF TORRENTIAL RAIN IN SOUTH CHINA

作　　者：张凯锋 王东海 张宇 张敏 张少婷 ZHANG Kaifeng;WANG Donghai;ZHANG Yu;ZHANG Min;ZHANG Shaoting(Meteorological Bureau of Foshan,Foshan 528000,China;School of Atmospheric Sciences/Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies/Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Sun Yat-sen University,Zhuhai 519082,China;South China Sea Institute of Marine Meteorology,Guangdong Ocean University,Zhanjiang,Guangdong 524088,China;Guangdong Meteorological Observation Data Center,Guangzhou 510641,China)

机构地区：[1]佛山市气象局,广东佛山528000 [2]中山大学大气科学学院/广东省气候变化与自然灾害研究重点实验室/南方海洋科学与工程广东省实验室,广东珠海519082 [3]广东海洋大学南海海洋气象研究院,广东湛江524088 [4]广东省气象探测数据中心,广东广州510641

出　　处：《热带气象学报》2022年第1期145-160,共16页Journal of Tropical Meteorology

基　　金：国家重点研发计划(2019YFC1510400);国家自然科学基金项目(91837204);广东省基础与应用基础研究重大项目(2020B0301030004)共同资助。

摘　　要：基于全球集合预报系统(GEFS)资料,利用WRF中尺度模式及GEFS动力降尺度获取区域集合预报初值场,通过对同化后的分析场进行模式积分实现华南前汛期区域集合预报。对2019年6月10日的一次华南前汛期暴雨过程进行不同同化方案的试验:混合同化(Hybrid)、三维变分(3Dvar)、集合卡尔曼滤波(EnKF)和对比试验(Ctrl)四组试验的对比分析,探讨具有不同背景误差协方差矩阵的同化方案对区域集合预报集合扰动和集合离散随时间演变特征的影响,评估不同试验的降水模拟效果。(1)Hybrid对模式初始场有较好的改善作用,而3DVar和EnKF对初始场的改善作用不明显。(2)对风场、温度场和湿度场,在前期预报中Hybrid的预报误差小于3DVar和EnKF,在中后期的预报中,3DVar和EnKF的预报误差得到改善,且好于Hybrid。同样,集合扰动能量,Hybrid和Ctrl在前期预报发展好于3DVar和EnKF,而在中后期的预报3DVar和EnKF好于Hybrid和Ctrl。(3)从24h累积降水评分中,整体上同化试验好于Ctrl,3DVar和EnKF好于Hybrid,且3DVar对大中雨级别的降水评分较好,而EnKF对暴雨以上级别的降水评分较好。(4)对于集合统计检验分析,同化试验的AUC值都大于Ctrl的AUC值,24h累积降水量阈值在10~100mm的AUC值,3DVar最好;而125mm阈值的AUC值,EnKF最好。Based on the Global Ensemble Forecast System data,the present study uses the WRF model and the GEFS dynamic downscaling method to obtain the regional ensemble forecast initial states.Moreover,the assimilated analysis field is integrated to achieve the reginal ensemble forecast for a precipitation event during annually first rainy season in South China.Four tests,namely Hybrid,3DVar,EnKF,and Ctrl,are carried out for a torrential rain process on June 10,2019 in South China.We also explore the evolution characteristics of the ensemble disturbance and ensemble spread for the assimilation schemes with difference background error covariance matrices and evaluate the precipitation simulation performance of different tests.The results show that:(1)Hybrid can improve the initial field of the model,while 3DVar and EnKF failed.(2)For wind,temperature,and relative humidity,the forecast error of Hybrid in the early forecast is less than that of 3DVar and EnKF.In the middle and late forecast,the forecast error of 3DVar and EnKF is reduced and is smaller than that of Hybrid.As for the ensemble disturbance energy,Hybrid and Ctrl are better than 3DVar and EnKF in the early forecast,and 3DVar and EnKF are better than Hybrid and Ctrl in the middle and late forecasts.(3)According to the 24-hour cumulative precipitation scores,the assimilation test is better than Ctrl,3DVar and EnKF are better than Hybrid;3DVar scores the best in heavy and moderate rainfall and EnKF scores the best in torrential rain and above.(4)For 24-hour cumulative precipitation ensemble statistical analysis,the area under the curve value of the assimilation test is greater than that of Ctrl;3DVar performs the best in the 10mm~100mm cumulative precipitation threshold and EnKF performs the best in the 125mm cumulative precipitation threshold.

关 键 词：资料同化方法 华南前汛期 区域集合预报 循环同化 

分 类 号：P456.7[天文地球—大气科学及气象学]