In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RM...In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.展开更多
为了探究云区卫星微波资料的同化应用,利用中国风云三号D星(Fengyun-3D,FY-3D)微波温度计二型(microwave temperature sounder-2,MWTS2)和微波湿度计二型(microwave humidity sounder-2,MWHS2)资料,基于人工神经网络算法研制了云区温湿...为了探究云区卫星微波资料的同化应用,利用中国风云三号D星(Fengyun-3D,FY-3D)微波温度计二型(microwave temperature sounder-2,MWTS2)和微波湿度计二型(microwave humidity sounder-2,MWHS2)资料,基于人工神经网络算法研制了云区温湿廓线反演模型,建立了云区资料的间接同化方案。于2019年6月开展晴空和云区同化试验,评估加入云区MWTS2和MWHS2资料对区域模式预报的影响。试验结果表明:MWTS2和MWHS2资料的同化对温湿度预报场有改善,主要体现在模式中高层均方根误差和平均偏差的减小,云区同化的改善幅度比晴空同化更大;同化MWTS2和MWHS2资料对于提高降水预报技巧有积极影响,云区同化对降水预报的改善主要体现在同化后的12~24 h,较晴空同化更明显。针对强降水个例分析表明,MWTS2和MWHS2资料对温度场、湿度场、水汽通量散度场的调整有利于降水预报的改善,而云区同化能够直接对天气系统的初始场进行调整,降水的位置与强度预报效果更好。展开更多
基金Supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(grant no.2019QZKK0105)the National Key Research and Development Program of China(2018YFC1506603).
文摘In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.
