![]() ![]() ![]() ![]() ![]() Grasso, 2014: Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. Deierling, 2016: Assimilation of pseudo-GLM data using the ensemble kalman filter. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.Īllen, B. 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. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. 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. 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. 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. 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |