Algorithm for Improved QPE over Complex Terrain Using Cloud-to-Ground Lightning Occurrences

Abstract

Lightning and deep convective precipitation have long been studied as closely linked variables, the former being viewed as a proxy, or estimator, of the latter. However, to date, no single methodology or algorithm exists for estimating lightning-derived precipitation in a gridded form. This paper, the third in a series, details the specific algorithm where convective rainfall was estimated with cloud-to-ground lightning occurrences from the U.S. National Lightning Detection Network (NLDN), for the North American Monsoon region. Specifically, the authors present the methodology employed in their previous studies to get this estimation, noise test, spatial and temporal neighbors and the algorithm of the Kalman filter for dynamically derived precipitation from lightning.

Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

“…The Geostationary Lightning Mapper (GLM) and Lightning Mapping Imager (LMI) mounted on the Geostationary Operational Environmental Satellite R-series (GOES-R) and Fengyun-4A (FY-4A) are useful for early predictions of storms and severe convection events, respectively [9,10]. Lightning has been employed to estimate convective precipitation as well [11][12][13]. Relying on the extensive lightning observation network and on the robust association between lightning and deep convection, the use of lightning data assimilation (LDA) is very useful to improve severe convective weather forecasting.…”