Assimilation of Pseudo-glm Data Using The Ensemble Kalman Filter

Abstract

Total lightning observations that will be available from the GOES-R Geostationary Lightning Mapper (GLM) have the potential to be useful in the initialization of convection-resolving numerical weather models, particularly in areas where other types of convective-scale observations are sparse or nonexistent. This study used the ensemble Kalman filter (EnKF) to assimilate real-data pseudo-GLM flash extent density (FED) observations at convection-resolving scale for a nonsevere multicell storm case (6 June 2000) and a tornadic supercell case (8 May 2003). For each case, pseudo-GLM FED observations were generated from ground-based lightning mapping array data with a spacing approximately equal to the nadir pixel width of the GLM, and tests were done to examine different FED observation operators and the utility of temporally averaging observations to smooth rapid variations in flash rates. The best results were obtained when assimilating 1-min temporal resolution data using any of three observation operators that utilized graupel mass or graupel volume. Each of these three observation operators performed well for both the weak, disorganized convection of the multicell case and the much more intense convection of the supercell case. An observation operator using the noninductive charging rate performed poorly compared to the graupel mass and graupel volume operators, a result that appears likely to be due to the inability of the noninductive charging rate to account for advection of space charge after charge separation occurs.