Using Radar-Derived Parameters to Develop Probabilistic Guidance for Lightning Cessation within Isolated Convection near Cape Canaveral, Florida

Abstract

Thunderstorms in central Florida frequently halt outdoor activities, requiring that one wait some prescribed time after an assumed last flash before safely resuming activities. The goal of this research is to develop a high-skill probabilistic method that can be used in high pressure real-world operations to terminate lightning warnings more quickly while maintaining safety. Probabilistic guidance tools are created for isolated warm season storms in central Florida using dual-polarized radar data at 1-min intervals. The parameters examined are maximum reflectivity and graupel presence at the 0°, −5°, −10°, −15°, and −20°C levels as well as composite reflectivity. Random samples of the radar data are used to train a generalized linear model (GLM) to make a probabilistic prediction whether a given flash is the storm’s last flash. The most statistically significant predictors for lightning cessation are found to be the storm’s maximum reflectivity in the composite and the 0°C levels, along with graupel presence or absence at the −5°, −10°, −15°, and −20°C levels. Statistical verification is used to analyze the performance of the two GLMs at different probability thresholds (95.0%, 97.5%, and 99.0%). When applying the cessation guidance as though storms are occurring in real time, results showed ~99% of the storms produced no additional lightning after the GLM suggested cessation had already occurred. Although these results are encouraging, the procedure must be tested on much larger datasets having different convective modes and different areal coverages to prove its value compared to operational forecasters.

Lightning Cessation Guidance Using Polarimetric Radar Data and Lightning Mapping Array in the Washington, D.C. Area

“…However, our results show that our best-performing cessation algorithms in Washington, D.C., end lightning advisories later than those in Florida [5]. A similar trend was found in [18] in which their lightning cessation model had a longer median lag-time for the Washington, D.C. area than found with the [19] model in Florida. The delay in ending lightning advisories for Washington, D.C., may be due to this area having stronger storms.…”

“…Thus, cessation guidance from this study will likely need to be adjusted for other geographic regions. Examining a composite index was beyond the scope of this study, but previous cessation studies [18,19] using a bootstrap model show promising results. Therefore, we encourage future cessation studies to develop guidance using multiple radar thresholds and isotherm levels.…”

“…Several studies have already focused on lightning cessation guidance using different variables and thresholds [5,[13][14][15][16][17][18][19]. Some of these studies were more effective than others, but non-isolated cells proved ineffective for cessation guidance due to the charging of nearby cells [5,13].…”

“…A study by [13] attempted to create cessation algorithms for non-isolated storms but found that their algorithms were ineffective. More recently, [18] examined convective cells in the Washington, D.C. area to develop a bootstrap model for lighting cessation guidance after its proven success for storms in Florida [19]. However, the change in geographic region made their cessation algorithm less effective than in the tropics.…”

An Evaluation of Relationships between Radar-Inferred Kinematic and Microphysical Parameters and Lightning Flash Rates in Alabama Storms

“…In contrast to studies using explicit cloud electrification and lightning flash rate [46,47], the flash rate used for LNO X production in these chemical transport models is estimated from flash rate parameterization schemes, which utilize model-predicted storm kinematic and microphysical quantities and are often based on radar-observed relationships [8][9][10]13,14,25,48,49]. Similarly, the initiation, cessation, and frequency of lightning have been forecasted using flash rate parameterizations with model-predicted [50,51] or radar-observed [52][53][54][55] kinematic and microphysical parameters as inputs.…”