An Evaluation of the Impact of Assimilating GLM-observed Total Lightning Data on Short-term Forecasts of High-impact Convective Events

The recent successful launch of the Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite R-series (GOES-16/17) provides nearly uniform spatiotemporal coverage of total lightning (intracloud plus cloud-to-ground) over the Americas and adjacent vast oceanic regions. This study evaluates the potential value of assimilating GLM observations on short-term, cloud-scale (dx = 1.5 km) forecasts of several severe weather events over the Great Plains of the United States using a three-dimensional variational (3DVAR) data assimilation (DA) system.

Building on past works, the lightning DA scheme adjusts background water vapor mass mixing ratio within a fixed, confined layer above the lifted condensation level at observed lightning locations. Toward a more systematic assimilation of real GLM data, this study first conducted sensitivity tests aimed at evaluating the impact of the horizontal decorrelation length scale (L), DA cycling frequency, and the length of the accumulation window for the lightning prior to DA. Forecast statistics aggregated over all five cases suggested that an optimal forecast performance is obtained when 10-min GLM rates are assimilated every 15 minutes using L = 3 km. With this suggested configuration, companion experiments assimilating (i) radar data, (ii) GLM data, and (iii) both GLM and radar data were evaluated for the same five cases against a control run not assimilating any data. Overall, the added value of assimilating two-dimensional GLM flash density data over volumetric radar data has a neutral to positive impact on the short-term forecast skill of composite reflectivity fields, accumulated rainfall, and individual storm tracks.