The Usefulness and Economic Value of Total Lightning Forecasts Made with a Dynamic Lightning Scheme Coupled with Lightning Data Assimilation

Abstract

Abstract Total lightning probability forecasts for 26 mostly springtime days and 27 summertime days were analyzed for their usefulness and economic value. The mostly springtime forecast days had a relatively high number of severe weather reports compared with the summertime forecast days. The lightning forecasts were made with a dynamic lightning forecast scheme (DLS), and each forecast dataset used lightning assimilation to hasten convective initiation and, in most cases, to improve short-term forecasts. A spatial smoothing parameter σ of 48 km yielded more skillful, reliable, and economically valuable hourly forecasts than other values of σ. Mostly springtime forecasts were more skillful and had more hours of useful skill than summertime forecasts, but the latter still demonstrated useful skill during the first two forecast hours. The DLS forecasts were compared to those obtained with the “McCaul” diagnostic scheme, which diagnoses lightning flash data. The DLS had significantly higher fractions skill scores than the McCaul scheme for or at least one event/flash (10 min)−1. Bias values of the forecast lightning fields with both schemes were overall small. Yet, DLS forecasts started in the early summer evening with RAP data did have positive bias, which was attributed to initial conditions within the RAP. Correlating fractions skill scores for lightning and precipitation indicated that more accurate forecasts of lightning were associated with more accurate precipitation forecasts for convection with a high, but not lower, number of severe weather reports.