Here, we relate the satellite-determined growth (FEDS, 12-hourly from 2012-2020; FIRED, daily from 2003-2020) of all individual wildfires in California to their local, near-surface environmental conditions (as observed by model reanalyses, ERA5 and gridMET; observational networks of surface stations, RAWS; and in-situ measurements, FMR). Both absolute and relative growth (relative to the previous timestep) are used to define rapid-growth events.
Environmental conditions are compared between individual fires of varying sizes and spread rates. The conditions before, during, and after periods of large absolute growth are composited across many fires, grouped by their relative growth (high or low). Regional spatial patterns and distributions of near-surface dryness and winds during large absolute-growth events are also evaluated and compared to climatological conditions. RAWS and ERA5 data are also compared directly.
Overall, large absolute-growth events generally occur when dead fuels are driest, trailing short periods of increased atmospheric dryness. For large absolute-growth events, high relative growth is usually associated with strong winds while low relative growth is generally associated with quiescent to climatological winds. Winds during high relative-growth events are greatest in autumn and surpass the 95th percentile on average. For these autumnal events, atmospheric dryness is less than during other seasons, but atmospheric and fuel dryness are still sufficient for fire. Irrespective of season, winds during particularly rapid-growth events (high absolute and relative growth) are frequently downslope and influenced by local terrain regardless of the climatological conditions.
While both surface stations and reanalysis provide qualitatively similar results on a whole, surface stations better capture local, fire-relevant conditions during some individual fires (e.g., gusty, convective outflow) especially when dynamically selected for active-fire proximity. Additionally, while both individual-fire datasets are improvements over gridded data, FEDS provides sharper composites as FIRED tends to spread out burned area in time as compared to reality.
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