基于伴随敏感性的风廓线雷达和地基微波辐射计观测对模式预报的影响评估研究  

Using the Adjoint-Based Forecast Sensitivity Method to Evaluate the Observations of Wind Profile Radar and Microwave Radiometer Impacts on a Model Forecast

作　　者：唐兆康 鲍艳松[1,2] 顾英杰 范水勇 齐亚杰 崔伟[4] 陈强[4] Zhaokang TANG;Yansong BAO;Yingjie GU;Shuiyong FAN;Yajie QI;Wei CUI;Qiang CHEN(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044;Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration,Nanjing University of Information Science&Technology,Nanjing 210044;Beijing Institute of Urban Meteorology,Beijing 100089;Shanghai Institute of Satellite Engineering,Shanghai 201109;Nanjing NARI Water Resources and Hydropower'Technology Company Limited,Nanjing 211000)

机构地区：[1]南京信息工程大学气象灾害预报预警与评估协同创新中心,南京210044 [2]南京信息工程大学中国气象局气溶胶—云—降水重点开放实验室,南京210044 [3]北京城市气象研究院,北京100089 [4]上海卫星工程研究所,上海201109 [5]南京南瑞水利水电科技有限公司,南京211000

出　　处：《大气科学》2022年第4期775-787,共13页Chinese Journal of Atmospheric Sciences

基　　金：国家重点研发计划项目2017YFC1501704;上海航天科技创新基金资助项目SAST2019-046;国家自然科学基金项目41975046。

摘　　要：同化大量观测资料可以有效地改进模式预报结果,但不同观测对预报的影响有着显著差异,合理评估观测对预报的贡献是数值模式中最具挑战性的诊断之一。本文采用基于伴随的预报对观测的敏感性(Forecast Sensitivity to Observation,简称FSO)方法,构建WRFDA(Weather Research and Forecasting model’sData Assimilation)框架下的WRFDA-FSO系统。基于2019年9月超大城市项目在北京地区获取的风廓线雷达(Wind Profile Radar,简称WPR)和地基微波辐射计(Microwave Radiometer,简称MWR)观测数据,利用WRFDA-FSO系统,开展观测对WRF模式12 h预报的影响试验,并分析风温湿观测对预报的贡献。结果表明:(1)同化的观测资料(MWR、WPR、Sound、Synop和Geoamv)均减小了WRF模式12 h预报误差,对预报为正贡献,其中MWR观测对预报的影响最大,WPR风场观测对预报的改进效果优于Sound的风场观测。(2)WPR的U、V观测和MWR的T、Q观测中,V观测和T观测对预报的正贡献值更高,对预报的改进效果更优。(3)WPR和MWR多数高度层的观测均减小了预报误差,对预报为正贡献,其中MWR的T观测对预报的正贡献主要位于近地面800 h Pa以下。Many assimilated observations can effectively improve the results of a model forecast. However, there are significant differences in the effects of various observations on the forecast. It is one of the most challenging diagnostics in numerical models to reasonably evaluate the observation contribution to the forecast. In this paper, the weather research and forecasting model’s data assimilation(WRFDA) and forecast sensitivity to observation(FSO) system was constructed in WRFDA by the method of adjoint-based FSO. Based on wind profile radar(WPR) and ground-based microwave radiometer(MWR) data obtained by the mega city project in Beijing in September 2019, the experiments on the impact of observations on the 12 h forecast of the WRF model are carried out using the WRFDA-FSO system. The contribution of wind, temperature, and humidity observations to the forecast is analyzed. The results show the following:(1) In general,the assimilated observations(MWR, WPR, Sound, Synop, and Geoamv) reduce the 12 h forecast error of the WRF model and make a positive contribution to the forecast. Among these, MWR observations have the greatest impact on the forecast, and the improvement of WPR observations on the forecast is better than that of wind field observations of sound.(2) Among the U and V observations of WPR and temperature and specific humidity observations of MWR, the positive contribution value of V observations and temperature observations to the forecast is higher, and the effect of improving the forecast is better.(3) The WPR and MWR observations, at most levels, reduce the forecast error and are a positive contribution to the forecast. The positive contribution of temperature observations is mainly below 800 hPa near the ground.

关 键 词：数值模式 资料同化 预报对观测的敏感性 影响试验 

分 类 号：P435[天文地球—大气科学及气象学]