Nikolov, N.; Bothwell, P.; Snook, J. Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification. Fire2024, 7, 111.
Nikolov, N.; Bothwell, P.; Snook, J. Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification. Fire 2024, 7, 111.
Nikolov, N.; Bothwell, P.; Snook, J. Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification. Fire2024, 7, 111.
Nikolov, N.; Bothwell, P.; Snook, J. Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification. Fire 2024, 7, 111.
Abstract
Lightning is responsible for most annually burned area by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm designed to run on a uniform 20-km grid over the Continental USA and Alaska. The algorithm consists of a large set of logistic equations parameterized via logistic regression using long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 Principal Components (strong predictors) from a list of 611 potential predictors. Our statistical analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: horizontal temperature distribution by pressure levels, amount of low-level atmospheric moisture, and wind vectors. These physical variables impact the vertical separation of electric charges in the lower troposphere during storms causing the voltage potential between ground and the cloud deck to increase to a level that triggers electrical discharges. Results from a forecast verification using independent data showed excellent model skill, thus making this algorithm suitable for inclusion into software systems forecasting the chance of wildfire ignitions.
Keywords
lightning; model; logistic regression; forecast; prediction; wildfire; probability
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
