Evaluation of GLM Lightning Flash Rate Observation Operators for HWRF
As the first operational lightning mapper flown in geostationary orbit, Geostationary Lightning Mapper (GLM) provides critical information to identify developing tropical cyclones. With that, assimilation of GLM lightning data has the promise of improving initial conditions and the subsequent forecast of tropical cyclones.
The assimilation of GLM lightning data requires the development of an observation operator that transforms model forecast to lightning flash rate (LFR). This study extends previous efforts to enable GLM lightning assimilation capability in the operational Hurricane Weather Research and Forecasting model (HWRF). Like many other operational models, the Gridpoint Statistical Interpolation (GSI) is also employed by HWRF as its data assimilation component.
Two LFR observation operators based on regression were introduced to GSI. One relates LFR to vertical updraft, while the other uses forecast threat algorithm. Specifically, forecast threat algorithm approximates LFR to a linear combination of graupel flux and vertically integrated ice water content. In this study, both observation operators are used to generate model equivalent LFR from HWRF output for selected hurricanes in the 2018 Atlantic season. The HWRF equivalent LFR is then compared against the GLM observed LFR. Results from the comparison will be incorporated to optimize LFR observation operators for HWRF.
Dr. Ting-Chi Wu Colorado State University