Application Of Radar Data And FY-2E VISSR Radiances In High-resolution Regional Numerical Prediction

Radar and satellite observations are the most widely used remote sensing observations.The Doppler Weather Radar (DWR) is currently the only observation platform capable ofsampling the atmosphere at high enough spatial and temporal resolutions for the explicitinitialization of precipitating weather systems in storm-scale Numerical Weather Prediction(NWP) models. The satellite observation platforms provide widely coverage of brightnesstemperature with high spatial and temporal resolutions. The radiance data are almost the onlyobservation over areas where conventional observations are limited (e.g., the ocean and theplateau). Data assimilation can effectively, reasonably use radar or satellite observations toimprove the model’s initial fields and the accuracy of NWP. Through two real cases study,benefits of assimilating FY-2E radiances, DWR radial wind and reflectivity are examined withWeather Research and Forecasting (WRF) and Gridpoint Statistical Interpolation (GSI).Four DWR data assimilation is applied in WRF+GSI cycling mode to initialize a localtorrential rainstorm on21July2012in Beijing and the WRF quantitative precipitationforecasting (QPF) skills are evaluated for the case. Numerical experiments demonstrate thatDWR data assimilation can improve nowcasting and short-term precipitation forecast, whoseETS score averagely increase0.2. Radar reflectivity data are used primarily in a cloud analysisthat retrieves the amount of hydrometeors and adjusts in-cloud temperature and moisturewhich have directly influence on generating precipitation. Assimilating reflectivity make theroot-mean-square error (RMSE) of geopotential height filed between650and250hPadecrease8geopotential meters (gpm). The direct assimilation of DWR radial wind in GSIexerts a sure influence in mesoscale wind field. Through the quantitative verification of thesimulation results, the forecast with reflectivity assimilation is better than with radial velocityassimilation. This study examines the impact of directly assimilating FY-2E IR1, IR2and WVinfrared channels radiances on the numerical simulation of a local autumn heavy rainfall overGuangdong province, observed13-14October2011. One of the prominent features of GSI is strict quality control on satellite observations, including data thinning, bias correction,underlaying surface check, standard deviation check and gross error check and so on. Thestrict quality control make the distribution of FY-2E observations be closer to Gaussiandistribution. The experiments’ results show, the analysis increment which generate byassimilating IR1, IR2channel data is main confined within the low-level of the troposphere;while the analysis increment of assimilating WV channel data is main confined within thehigh-level of troposphere. This phenomenon is associated with the corresponding weightingfunction peaks. Data assimilation obviously make the analysis field be closer to theobservations, the observation increment of three infrared channels reduce to1.5,1.5and1.0K(from1.9,1.9and1.4K). The forecast with WV channel data assimilation is better than withIR1or IR2channel data assimilation, the ETS score increase0.05at25-and100-mmprecipitation. The ETS results indicate assimilating three infrared channels data is notabsolutely better than only assimilating one infrared channel data.