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Evaluating the Relationship Between Lightning and Large-Scale Environmental Variables
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The objective of this study is to determine the relationship between lightning and six large-scale environmental variables: convective available potential energy (CAPE), normalized CAPE (nCAPE), column saturation fraction (r), 700-hPa omega, 900-700 hPa low-level wind shear (LS) and 900-300 hPa deep wind shear (DS). Lightning data is obtained from the Tropical Rainfall Measuring Mission’s (TRMM) Lightning Imaging Sensor (LIS) from 1998 to 2013 and large-scale environmental variables are derived from 3-hourly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data. Each dataset is binned at 0.5° x 0.5°. MERRA-2 data is considered to represent a lightning environment when lightning occurs within 30 minutes of the MERRA-2 time stamp. CAPE, nCAPE and r show clear distinctions in lightning environments compared to non-lightning environments and the largest flash occurrences are associated with lowto- moderate CAPE, moderate nCAPE, slightly negative values of 700-hPa omega (i.e., rising motion), high r, low-to-moderate LS and low DS. Clear geographical distinctions for flash occurrences exist between land and ocean for CAPE, nCAPE and r and between tropical and sub-tropical areas for CAPE, nCAPE, r and DS. The relationship of r with other variables for lightning occurrences is evaluated and it is shown that CAPE and omega with r have the clearest relationships. The association between large-scale environmental variables and lightning is analyzed globally for latitudes between 35°N and 35°S using two statistical models. Using a generalized linear model (GLM), nCAPE and r are the primary predictors for lightning prediction. Using a point-process model, nCAPE is the best predictor, with strong regional contrasts present. The GLM is used in a lightning parameterization to predict lightning from MERRA-2 and the Community Atmosphere Model version 5 (CAM5). Predicted lightning from both datasets generally agrees with observations from the TRMM LIS, which supports the use of a lightning parameterization in GCMs.
Etten-Bohm, Montana (2018). Evaluating the Relationship Between Lightning and Large-Scale Environmental Variables. Master's thesis, Texas A & M University. Available electronically from