Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density in JEDI LETKF, LGETKF, and En3DVar: Development of Assimilation Capabilities and Test with a Convective Storm Case over the United States

In this study, we implement the capabilities to assimilate GOES-R Geostationary Lightning Mapper flash extent density (FED) data within the Joint Effort for Data Assimilation Integration (JEDI) system, coupled with the Finite-Volume Cubed-Sphere (FV3) dynamical core for forecasting. We evaluate different data assimilation (DA) methods, including the local ensemble transform Kalman filter (LETKF), gain-form LETKF (LGETKF), and ensemble 3DVAR (En3DVar), for a test case with active convection over the United States. The convergence behavior of En3DVar is consistent with expectations. Sensitivity to the vertical localization strategies in the algorithms is examined. LGETKF applies gain-form vertical localization, which demands more computational resources than LETKF and En3DVar when using smaller vertical localization radii [e.g., 0.2 or 0.4, compared to larger radii like 1 or 4 in ln(p/p₀) space]. While En3DVar achieves a better balance between accuracy and efficiency, it demands significantly more memory than LETKF and LGETKF, with the current JEDI implementation at least. Sensitivity experiments indicate that larger vertical localization radii [e.g., 4 in ln(p/p₀) space] improve analysis and 6-h forecast after DA when verified against the observed reflectivity field. Overall, all three DA methods produce comparable results, outperforming the experiment that does not assimilate any data. This work serves to establish the credibility of the lightning DA implementation within the new JEDI system and to understand the effects of algorithm differences related to vertical covariance localization on the assimilation of FED data, whose observation operator involves column integration of a hydrometeor state variable.