Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

作     者：Rong KONG Ming XUE Edward R.MANSELL Chengsi LIU Alexandre O.FIERRO Rong KONG;Ming XUE;Edward R. MANSELL;Chengsi LIU;Alexandre O. FIERRO

作者机构：Center for Analysis and Prediction of StormsNorman Oklahoma 73072U.S.A University of OklahomaNorman Oklahoma 73072U.S.A NOAA/National Severe Storms LaboratoryNormanOklahoma 73072U.S A.4Zentralanstalt für Meteorologie und GeodynamikDepartment of Forecasting Models-ZAMGVienna 1190Austria 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2024年第41卷第2期

页      面：263-277页

核心收录：

学科分类：07[理学] 0706[理学-大气科学] 

基　　金：supported by NOAA JTTI award via Grant #NA21OAR4590165, NOAA GOESR Program funding via Grant #NA16OAR4320115 provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Cooperative Agreement #NA11OAR4320072, U.S. Department of Commerce supported by the National Oceanic and Atmospheric Administration (NOAA) of the U.S. Department of Commerce via Grant #NA18NWS4680063。 

主　　题：GOES-R lightning data assimilation EnKF EnVar 

摘      要：Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series (GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.