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-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 Detection,简称WPRD)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12h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、WPRD、Sound、Synop和Geoamv)均减小了WRF模式12h预报误差,对预报为正贡献,其中MWR观测对预报的影响最大,WPRD风场观测对预报的改进效果优于Sound的风场观测。(2)WPRD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)WPRD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 hPa以下。
A large number of observations assimilated can effectively improve the results of model forecast. However, there are significant differences in the effects of different observations on the forecast. It is one of the most challenging diagnostics in numerical models to reasonably evaluate the contribution of observations to the forecast. In this paper, the WRFDA-FSO system is constructed by the method of adjoint-based forecast sensitivity to observation(FSO). Based on the wind profile radar detection(WPRD) 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 12h forecast of WRF model are carried out by using WRFDA-FSO system, and the contribution of wind, temperature and humidity observations to the forecast is analyzed. The results show that: (1) In general, the observations(MWR, WPRD, Sound, Synop and Geoamv) assimilated all reduce the 12h forecast error of WRF model, and make positive contribution to the forecast. Among them, MWR observations have the greatest impact on the forecast, and the improvement of WPRD observations on forecast is better than that of wind field observations of Sound. (2) Among the U and V observations of WPRD 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 observations of WPRD and MWR at most levels reduce the forecast error and are positive contribution to forecast, and the positive contribution of temperature observations is mainly below 800 hPa near the ground.