| description abstract | his work evaluates the performance of a recently developed cloud-scale lightning data assimilation technique implemented within the Weather Research and Forecasting Model running at convection-allowing scales (4-km grid spacing). Data provided by the Earth Networks Total Lightning Network for the contiguous United States (CONUS) were assimilated in real time over 67 days spanning the 2013 warm season (May?July). The lightning data were assimilated during the first 2 h of simulations each day. Bias-corrected, neighborhood-based, equitable threat scores (BC-ETSs) were the chief metric used to quantify the skill of the forecasts utilizing this assimilation scheme. Owing to inferior observational data quality over mountainous terrain, this evaluation focused on the eastern two-thirds of the United States.During the first 3 h following the assimilation (i.e., 3-h forecasts), all the simulations suffered from a high wet bias in forecasted accumulated precipitation (APCP), particularly for the lightning assimilation run (LIGHT). Forecasts produced by LIGHT, however, had a noticeable, statistically significant (α = 0.05) improvement over those by the control run (CTRL) up to 6 h into the forecast with BC-ETS differences often exceeding 0.4. This improvement was seen independently of the APCP threshold (ranging from 2.5 to 50 mm) and the neighborhood radius (ranging from 0 to 40 km) selected. Past 6 h of the forecast, the APCP fields from LIGHT progressively converged to that of CTRL probably due to the longer-term evolution being bounded by the large-scale model environment. Thus, this computationally inexpensive lightning assimilation scheme shows considerable promise for routinely improving short-term (≤6 h) forecasts of high-impact weather by convection-allowing forecast models. | |
