Lightning is a major source of wildfire ignition in the western United States. My research examines the meteorological drivers and environmental conditions that produce lightning and control whether it ignites a wildfire, with a particular focus on "dry" lightning. My research has characterized the atmospheric patterns associated with dry lightning outbreaks, quantified how precipitation amounts and environmental conditions influence whether lightning starts a wildfire, and shown that commonly used definitions of dry lightning do not fully capture ignition risk across different landscapes. I have also developed neural network-based machine learning models that predict cloud-to-ground lightning from large-scale atmospheric variables, allowing lightning to be quantified in climate model simulations where it is not directly resolved. Applying these models to future climate projections shows increasing lightning activity across much of the western United States and more frequent overlap between lightning and conditions favorable for wildfire ignition. Overall, my research aims to improve our understanding and prediction of lightning-ignited wildfire risk from individual storms to broader climatological shifts due to future climate change.

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