Dry Lightning in the Western United States: Meteorological Conditions, Wildfire Ignition, Air Quality Impacts, and Future Projections. Kalashnikov, D. A. Ph.D. Thesis, Washington State University, 2024. Medium: application/pdf Publisher: Washington State University![Dry Lightning in the Western United States: Meteorological Conditions, Wildfire Ignition, Air Quality Impacts, and Future Projections [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Cloud-to-ground lightning occurring with little to no rainfall – typically referred to as “dry lightning” – is a major source of wildfire ignition in the western United States (WUS) during summer months. Although lightning-caused wildfires occur naturally and are generally ecologically beneficial, changing climatic conditions are increasing the risk of large and severe wildfires. Motivated by these impacts, my dissertation aims to advance our understanding of dry lightning in the WUS and its associated meteorological conditions, wildfire ignitions, air quality impacts, and future projections. In the first chapter, I provide an overview of the motivations for undertaking this dissertation. In the second chapter, I use gridded air pollutant and population data to examine compound air pollution episodes in the WUS. This study demonstrates an increase in the co-occurrence of two harmful air pollutants (fine particulate matter and ground-level ozone) during the WUS wildfire season in the past two decades, and increasing population exposure to these co-occurrences of 25 million person-days per year largely driven by increasing wildfire activity. I show that the largest population exposure to co-occurring air pollution was associated with the major outbreak of dry lightning that ignited hundreds of wildfires in California during August, 2020. To better understand dry lightning in this fire-prone region, in the third chapter I examine the meteorological and geographical factors associated with dry lightning in central and northern California. I apply k-means clustering to atmospheric reanalysis data to identify four types of meteorological patterns associated with the largest dry lightning outbreaks over this region, and quantify the spatial patterns of enhanced dry lightning risk associated with each pattern. In the fourth chapter, I use radar-derived rainfall data and gridded climatological variables to investigate the precipitation amounts and biophysical factors associated with lightning-caused wildfire ignitions across the WUS. Critically, my results refine the widely-used <2.5mm precipitation amount to define dry lightning by demonstrating that substantial regional variation exists in ignition-relevant precipitation amounts depending on local topography, vegetation, and climate. In the fifth chapter, I use Convolutional Neural Networks (CNNs) to predict cloud-to-ground lightning in the WUS at the grid cell level using a suite of reanalysis-derived meteorological variables as predictors. The CNNs are skillful at predicting lightning (domain-median AUC = 0.8) and realistically capture the year-to-year variation of lightning activity across the WUS (domain-median interannual correlation = 0.87). The CNN-based predictive models developed in this study can be applied to output from global climate models, thus enabling the ability to project future lightning and lightning-caused wildfires. In the final chapter, I summarize my findings from the four studies that comprise my dissertation. The outcomes of my research can be useful to forecasters and fire managers to anticipate possible wildfire ignitions in the present climate, and can be used to inform planning, management, and policy decisions around future lightning-caused wildfires in the WUS.
@phdthesis{kalashnikov_dry_2024,
type = {Doctor of {Philosophy} ({PhD})},
title = {Dry {Lightning} in the {Western} {United} {States}: {Meteorological} {Conditions}, {Wildfire} {Ignition}, {Air} {Quality} {Impacts}, and {Future} {Projections}},
copyright = {Creative Commons Attribution 4.0 International, Open},
shorttitle = {{DRY} {LIGHTNING} {IN} {THE} {WESTERN} {UNITED} {STATES}},
url = {https://rex.libraries.wsu.edu/esploro/outputs/doctoral/99901120940501842},
abstract = {Cloud-to-ground lightning occurring with little to no rainfall – typically referred to as “dry lightning” – is a major source of wildfire ignition in the western United States (WUS) during summer months. Although lightning-caused wildfires occur naturally and are generally ecologically beneficial, changing climatic conditions are increasing the risk of large and severe wildfires. Motivated by these impacts, my dissertation aims to advance our understanding of dry lightning in the WUS and its associated meteorological conditions, wildfire ignitions, air quality impacts, and future projections. In the first chapter, I provide an overview of the motivations for undertaking this dissertation. In the second chapter, I use gridded air pollutant and population data to examine compound air pollution episodes in the WUS. This study demonstrates an increase in the co-occurrence of two harmful air pollutants (fine particulate matter and ground-level ozone) during the WUS wildfire season in the past two decades, and increasing population exposure to these co-occurrences of 25 million person-days per year largely driven by increasing wildfire activity. I show that the largest population exposure to co-occurring air pollution was associated with the major outbreak of dry lightning that ignited hundreds of wildfires in California during August, 2020. To better understand dry lightning in this fire-prone region, in the third chapter I examine the meteorological and geographical factors associated with dry lightning in central and northern California. I apply k-means clustering to atmospheric reanalysis data to identify four types of meteorological patterns associated with the largest dry lightning outbreaks over this region, and quantify the spatial patterns of enhanced dry lightning risk associated with each pattern. In the fourth chapter, I use radar-derived rainfall data and gridded climatological variables to investigate the precipitation amounts and biophysical factors associated with lightning-caused wildfire ignitions across the WUS. Critically, my results refine the widely-used \<2.5mm precipitation amount to define dry lightning by demonstrating that substantial regional variation exists in ignition-relevant precipitation amounts depending on local topography, vegetation, and climate. In the fifth chapter, I use Convolutional Neural Networks (CNNs) to predict cloud-to-ground lightning in the WUS at the grid cell level using a suite of reanalysis-derived meteorological variables as predictors. The CNNs are skillful at predicting lightning (domain-median AUC = 0.8) and realistically capture the year-to-year variation of lightning activity across the WUS (domain-median interannual correlation = 0.87). The CNN-based predictive models developed in this study can be applied to output from global climate models, thus enabling the ability to project future lightning and lightning-caused wildfires. In the final chapter, I summarize my findings from the four studies that comprise my dissertation. The outcomes of my research can be useful to forecasters and fire managers to anticipate possible wildfire ignitions in the present climate, and can be used to inform planning, management, and policy decisions around future lightning-caused wildfires in the WUS.},
language = {en},
urldate = {2024-08-23},
school = {Washington State University},
author = {Kalashnikov, Dmitri A.},
collaborator = {Singh, Deepti and Moffett, Kevan B and Walden, Von P and Loikith, Paul C},
year = {2024},
note = {Medium: application/pdf
Publisher: Washington State University},
}
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I show that the largest population exposure to co-occurring air pollution was associated with the major outbreak of dry lightning that ignited hundreds of wildfires in California during August, 2020. To better understand dry lightning in this fire-prone region, in the third chapter I examine the meteorological and geographical factors associated with dry lightning in central and northern California. I apply k-means clustering to atmospheric reanalysis data to identify four types of meteorological patterns associated with the largest dry lightning outbreaks over this region, and quantify the spatial patterns of enhanced dry lightning risk associated with each pattern. In the fourth chapter, I use radar-derived rainfall data and gridded climatological variables to investigate the precipitation amounts and biophysical factors associated with lightning-caused wildfire ignitions across the WUS. Critically, my results refine the widely-used <2.5mm precipitation amount to define dry lightning by demonstrating that substantial regional variation exists in ignition-relevant precipitation amounts depending on local topography, vegetation, and climate. In the fifth chapter, I use Convolutional Neural Networks (CNNs) to predict cloud-to-ground lightning in the WUS at the grid cell level using a suite of reanalysis-derived meteorological variables as predictors. The CNNs are skillful at predicting lightning (domain-median AUC = 0.8) and realistically capture the year-to-year variation of lightning activity across the WUS (domain-median interannual correlation = 0.87). The CNN-based predictive models developed in this study can be applied to output from global climate models, thus enabling the ability to project future lightning and lightning-caused wildfires. In the final chapter, I summarize my findings from the four studies that comprise my dissertation. 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Critically, my results refine the widely-used \\<2.5mm precipitation amount to define dry lightning by demonstrating that substantial regional variation exists in ignition-relevant precipitation amounts depending on local topography, vegetation, and climate. In the fifth chapter, I use Convolutional Neural Networks (CNNs) to predict cloud-to-ground lightning in the WUS at the grid cell level using a suite of reanalysis-derived meteorological variables as predictors. The CNNs are skillful at predicting lightning (domain-median AUC = 0.8) and realistically capture the year-to-year variation of lightning activity across the WUS (domain-median interannual correlation = 0.87). The CNN-based predictive models developed in this study can be applied to output from global climate models, thus enabling the ability to project future lightning and lightning-caused wildfires. In the final chapter, I summarize my findings from the four studies that comprise my dissertation. 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