Environmental Controls on Lightning Flash Rates in Supercells
This poster was presented at the AMS 31st Conference on Severe Local Storms held at Virginia Beach, VA, USA in October 2024.
Conference abstract:
The World Meteorological Organization recognizes lightning as one of the essential climate variables, with the goal to provide the evidence needed to understand and predict the evolution of climate. Recent studies have highlighted the role of climate change in increasing the frequency and intensity of extreme weather events, making it important to understand the environmental factors that control lightning flash rates in severe convective storms. Supercells are the leading cause of extreme weather events, such as tornadoes and hail, in the United States. Although our conceptual understanding of tornado and hail risk in such storms has vastly improved over the last few decades, significant gaps remain in our understanding of the thermodynamic and kinematic environmental controls on their lightning flash rates. While forecasters in the United States do not issue a convective outlook specifically for lightning, they often monitor real-time lightning flash rate trends to enhance situational awareness of updraft intensity, supplementing the slower updates from NEXRAD’s polarimetric radar signatures.
We hypothesize that the evolution of deep convective updrafts and storm-scale microphysical processes largely depends on the near-storm inflow environment and low-level initiation mechanisms. This study aims to systematically investigate the environmental influence on lightning flash rates by combining observations from past field-project soundings with data from the Oklahoma and West Texas lightning mapping array networks. Polarimetric radar variables, including differential reflectivity, specific differential phase, and correlation coefficient at or above the melting level will be used to quantify the covariability between lightning flash rates and radar-inferred microphysical signatures under specific environmental conditions. To avoid complications arising from storm mergers and interactions, which could interfere with our interpretation and analysis, we will initially restrict our sample size to isolated supercells.
We aim to subset the storm database based on: (1) right-moving and left-moving supercells, (2) tornadic and nontornadic supercells, (3) maximum hail size reported, (4) peak precipitation intensity, and (5) severe and significantly severe storm categories. These subclassifications will provide additional insights into the compound lightning risk posed by supercells, along with other severe weather threats.
Beyond the traditional Skew-T log-P derived convective indices, we aim to analyze the vertical distribution of parcel buoyancy, boundary-layer and free-tropospheric moisture, wind shear at low, mid, and high-levels, storm-relative winds, streamwise vorticity, storm-relative helicity, and entrainment CAPE to distinguish between supercells with small and large lightning flash rates. By identifying the environmental conditions favorable for intense lightning flash rates and/or lightning jump signatures, we aim to inform machine learning and physics-based lightning parameterization schemes in numerical models, enhancing their ability to capture the environmental influence on predicted flash rates.
