Assimilation Of Radar Reflectivity And Pseudo-observation For Convective-scale NWP In A Variational Framework

How to accurately initialize the convective-scale numerical weather prediction(NWP)model is one of the most important things to improve severe weather prediction.At present,radar observation can provide high temporal and spatial resolution information for the storm scale weather system.Therefore,it is of great significance to study the assimilation of radar data into convective-scale NWP to improve the prediction of severe convective weather and to reduce the life and property loss of people.Compared to the more advanced 4DVAR,En KF and hybrid data assimilation methods,3DVAR is chosen because it is simple and easy to operate,need less computer resource,and can obtain efficiency and fast results.Water vapor and temperature information play an important role in the development and maintenance of convection weather system,while the observation of high-resolution water vapor and temperature is very sparse.In order to effectively alleviate this problem and improve severe thunderstorm prediction,in this study,(I)First,a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales.The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water(VIL)derived from three-dimensional radar reflectivity fields.Once VIL is obtained,pseudo-water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection.Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast.The third step is to assimilate the derived pseudo-water vapor observations into a convective-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during data assimilation cycles.The performance of this method is examined for two selected high-impact severe thunderstorm events.(II)Second,the cloud temperature derived by the moist adiabatic initialization scheme in ARPS cloud analysis method is used as the pseudoin-cloud temperature observation.The performance of assimilation of pseudo-temperature observation together with radar data and pseudo-water vapor is tested for a tornado supercell.(III)Finally,considering the difference of latent heat heating profile in stratiform precipitation,deep convective precipitation and shallow convective region,a new in-cloud potential temperature adjustment scheme is designed to combine latent heat scheme and adiabatic initialization scheme by using the VIL Convective/Stratiform separation algorithm.The effect of the new assimilation scheme is preliminarily examined by using a Meiyu Front heavy rain event.The main conclusions of this paper are as following:(1)The results of assimilation of pseudo-water shows that there is a positive water vapor increment in the observed strong echo area,while the negative water vapor increment mainly appears in the spurious convection of background.(2)Assimilating the pseudo-potential temperature indicates that the positive potential temperature increment can warm model atmosphere and is conducive to counteracting the negative buoyancy related to condensate and evaporation cooling of hydrometeros,and is conducive to the development and maintenance of convection.With more interval number of cycling assimilation,the increment of temperature for adiabatic initialization scheme decreases obviously,while the increment of temperature for the new combined potential temperature scheme increase,because the latent heating dominant the temperature adjustment.(3)Compared with only assimilating radar radial velocity and reflectivity,after assimilating pseudo-water vapor or pseudo-in-cloud potential temperature,the analyses and forecasts of these three tornado severe weather events are qualitatively and quantitatively improved,including: obtaining more consistent analyses of moisture and better analyses of precipitation;reductions of spurious storm cells;and more realistic prediction of reflectivity patterns and 2-5 km updraft helicity tracks which better match the observed tornado damage tracks.(4)The analysis and forecast of these two Meiyu front heavy rain events improved qualitatively and quantitatively with these two cases when the pseudo-observations were assimilated,in terms of obtaining more consistent analyses of reflectivity,and more realistic QPF.(5)Assimilating radar data,pseudo-water vapor and in-cloud potential temperature in3 DVAR framework with 1.5 km horizontal resolution can accurately analyze the main three-dimensional structural characteristics of Funing tornado supercell.Although the fine structure of tornado can not be obtained from the simulation of WRF model,the partern and intensity of simulated reflectity,horizontal wind field and vorticity field for the supercell are still very useful for prediction of strong convection.