摘要
为提高CMA-MESO 3 km系统降水预报能力,采用二维离散余弦变换对2018年6月2日至8月31日3个月格点背景误差样本结合模式分辨率和天气系统尺度范围划分进行3种尺度分离,并对这3种尺度背景误差样本分别进行水平协相关尺度拟合,通过采用3个不同水平特征相关尺度的递归滤波器在CMA-MESO三维变分同化系统中实现3种拟合水平协相关尺度应用,替代业务测试系统单一水平特征相关尺度,开展个例和连续试验分析。研究结果表明,采用二维离散余弦变换尺度分离背景误差样本的3种水平特征相关尺度垂直结构相似,水平特征尺度的水平尺度相隔几十至几百千米。拟合水平特征相关尺度在CMA-MESO 3 km系统应用结果显示,3种水平特征相关尺度试验对u风、v风、湿度分析有明显正影响,分析更接近实况,对温度分析影响较小;对降水预报有改善,冷启动预报前6 h的TS评分提高明显,偏差(Bias)减小向1靠近,暖启动24 h逐6 h降水预报TS评分都小幅提升,Bias差异不大。
In order to improve the precipitation forecasting skill of CMA-MESO(China Meteorological Administration Mesoscale Model)at 3 km resolution,three different horizontal correlation characteristic scales of background error covariance are obtained with 2-D discrete cosine transform filter from three months’(2 June to 31 August 2018)background error samples.The three horizontal co-correlation scales are fitted and implemented in CMA-MESO operational testing system with recursive filter of three different scales,so as to replace the single-scaled recursive filtering.The results show that the profiles of three horizontal co-correlation scales with height are similar,with tens to hundreds km apart.The analysis qualities and verification of precipitation forecasting between the control experiments(single-scaled recursive filterings)and sensitivity experiments(three different scaled recursive filterings)with CMA-MESO system at 3 km resolution are compared.The numerical results indicate that the wind and relative humidity analyses are more close to observation in sensitivity experiments.The increment difference of temperature analysis is very small.In addition,the precipitation forecast skill is improved in sensitivity experiments.The first 6 h precipitation forecast TS value over 1-31 July 2018 with cold start is higher and the bias value is more close to 1 in sensitivity experiments.Meanwhile,the TS value of every 6 h precipitation in 24 h forecasting term with warm start is improved too.
作者
徐枝芳
王瑞春
XU Zhifang;WANG Ruichun(Center for Earth System Modeling and Prediction of CMA,Beijing 100081;State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081;National Meteorological Centre,Beijing 100081)
出处
《气象》
CSCD
北大核心
2022年第12期1525-1538,共14页
Meteorological Monthly
基金
国家重点研发计划(2018YFF0300103)
国家自然科学基金项目(41275105)共同资助。
