Rong Kong,a Ming Xue,a,b, Jun Park a, Tao Sun a, Chengsi Liu a
a Center for Analysis and Prediction of Storms, Norman, Oklahoma
b University of Oklahoma, Norman, Oklahoma
The Geostationary Lightning Mapper (GLM) carried by the Geostationary Operational Environmental Satellite ‘‘R-series’’ (GOES-R) satellites measures from space the total lightning rate in convective storms at high spatial and temporal frequencies. The products contain valuable information on convective storm activity and complement weather radars networks over mountainous regions and oceans.
Recently, a research team led by CAPS/OU developed direct Flash Extent Density (FED) assimilation capabilities within the new Joint Effort for DA Integration (JEDI) data assimilation (DA) framework. In this study, the FED data is assimilated using JEDI LETKF, LGETKF, and En3DVar DA algorithms, coupling with the limited area model with Finite Volume Cubed-Sphere dynamical core (FV3-LAM). Performance of FED DA using different vertical localization radius for different DA algorithms are compared based on both single-point and cycled DA experiments. Results show that JEDI PEnVar outperforms JEDI LETKF, LGETKF in terms of FSS in FED and composite reflectivity analyses/forecasts and more accurate precipitation forecasts for a mesoscale convective system MCS. The algorithm differences in LGETKF/LETKF (minimum variance solution) and PEn3DVar (maximum likelihood solution) are believed to be the reason. The conventional data are also assimilated together with FED data and are compared with that of with radar data for five convective storm cases in US. Assimilating conventional data together with FED data performs similarly to that with radar data, and both produce better precipitation forecasts than that assimilates conventional data only as well as NoDA that does not assimilate any data.
Keyword: GOES-R, data assimilation, JEDI, FV3-LAM, EnVar, LETKF, LGETKF
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