Evaluating the Relationship Between Lightning and the Large‐Scale Environment and its Use for Lightning Prediction in Global Climate Models
Montana Etten‐Bohm, Junho Yang, Courtney Schumacher, Mikyoung Jun
Abstract
Abstract The objective of this study is to determine the relationship between lightning observed by the Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) and seven large‐scale environmental variables obtained from the 3‐hourly Modern‐Era Retrospective analysis for Research and Application version 2 (MERRA‐2) reanalysis in the tropics and subtropics. The large‐scale environmental variables used are: convective available potential energy (CAPE), normalized CAPE (nCAPE), lifting condensation level (LCL), column saturation fraction (r), 700‐hPa omega, low‐level wind shear (LS) from 900 to 700 hPa, and deep wind shear (DS) from 900 to 300 hPa. All environmental variables show a significant shift toward larger values when lightning is present except for shear. DS decreases when lightning is present, while LS shows little mean change. However, strong geographical differences exist in the relationship between the environmental variables and lightning occurrence, particularly between land and ocean and the tropics and subtropics. Using a logistic regression, a lightning parameterization for global climate models (GCMs) is created using the above environmental variables as predictors while also adding geographic indicators (coast, slope, and latitude) and terms representing interactions between the predictors. The logistic regression predicts lightning occurrence accurately up to 86% of the time and is further applied to MERRA‐2. While there are regions of overprediction and underprediction, the lightning parameterization performance shows promising potential for use in GCMs.