AbstractAbstractExploring lightning patterns alongside meteorological variables and fuel information helps diagnose the conditions under which lightning ignites fires. This study describes distributions of lightning, land surface, and meteorological conditions associated with known lightning ignitions in the western United States during 2020–22. Although most fire ignition lightning is classified as cloud-to-ground (89%), lightning misclassified as intracloud ignites 11% of fires. Findings indicate clear differences between the ignition and nonignition (null) lightning, and the fires detected on the day of versus those that holdover. Both the National Lightning Detection Network (NLDN) and Geostationary Lightning Mapper (GLM) observations help identify lightning more likely to ignite wildfires. On average, positive and negative polarity ignition flashes are 3.9 and 4.8 kA stronger than null flashes. Both GLMs indicate that ignition flashes are larger and 3–4 times brighter than the domainwide values. Many additional variables reveal a clear distinction between the ignition and null storm environments. The average hourly (daily) quantitative precipitation estimate is 7.08 (12.69) mm for the null flashes and only 2.76 (5.16) mm for ignitions. The Multi-Radar Multi-Sensor variables indicate weaker storms surrounding the ignitions, and several different relative humidity measures indicate ∼10% drier environments. Each variable shown indicates that fires reported on the day of ignition occur in the most fire-prone environments, and null cases represent the least conducive environments, with holdover fires somewhere in between. Our analysis shows that distributions of lightning, land surface, and meteorological conditions can help identify environments most vulnerable to lightning ignitions.Significance StatementThis study describes the development and application of a database of lightning known to have ignited fires in the western United States during 2020–22. Our findings indicate clear differences between the ignition and nonignition (null) lightning, and the fires detected on the day of versus those that holdover. Each of the variables shown indicates that fires detected on the day of ignition occur in the most fire-prone environments, the null cases represent the least conducive environments, and the fires that holdover fall somewhere in between. Our analysis indicates that distributions of lightning, land surface, and meteorological conditions can help identify environments most vulnerable to lightning ignitions. This knowledge will help guide the development of automated applications for identifying the most impactful lightning collocated with conditions conducive to wildfire ignition.
