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Abstract Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud‐to‐ground (CG) lightning across the WUS during June–September from the spatial patterns of seven large‐scale meteorological variables from reanalysis (1995–2022). Individually trained CNN models at each 1° × 1° grid cell (n = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.8) and perform best in parts of the interior Southwest where summertime CG lightning is most common. Further, interannual correlation between observed and predicted CG lightning days is high (medianr = 0.87), demonstrating that locally trained CNNs realistically capture year‐to‐year variation in CG lightning activity across the WUS. We then use layer‐wise relevance propagation (LRP) to investigate the relevance of predictor variables to successful CG lightning prediction in each grid cell. Using maximum LRP values, our results show that two thermodynamic variables—ratio of surface moist static energy to free‐tropospheric saturation moist static energy, and the 700–500 hPa lapse rate—are the most relevant CG lightning predictors for 93%–96% of CNNs depending on the LRP variant used. As lightning is not directly simulated by global climate models, these CNNs could be used to parameterize CG lightning in climate models to assess changes in future CG lightning occurrence with projected climate change. Understanding changes in CG lightning risk and consequently lightning‐caused wildfire risk across the WUS could inform fire management, planning, and disaster preparedness.
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Meteorological and geographical factors associated with dry lightning in central and northern California
Abstract Lightning occurring with less than 2.5 mm of rainfall—typically referred to as ‘dry lightning’—is a major source of wildfire ignition in central and northern California. Despite being rare, dry lightning outbreaks have resulted in destructive fires in this region due to the intersection of dense, dry vegetation and a large population living adjacent to fire-prone lands. Since thunderstorms are much less common in this region relative to the interior West, the climatology and drivers of dry lightning have not been widely investigated in central and northern California. Using daily gridded lightning and precipitation observations (1987–2020) in combination with atmospheric reanalyses, we characterize the climatology of dry lightning and the associated meteorological conditions during the warm season (May–October) when wildfire risk is highest. Across the domain, nearly half (∼46%) of all cloud-to-ground lightning flashes occurred as dry lightning during the study period. We find that higher elevations (>2000 m) receive more dry lightning compared to lower elevations (<1000 m) with activity concentrated in July-August. Although local meteorological conditions show substantial spatial variation, we find regionwide enhancements in mid-tropospheric moisture and instability on dry lightning days relative to background climatology. Additionally, surface temperatures, lower-tropospheric dryness, and mid-tropospheric instability are increased across the region on dry versus wet lightning days. We also identify widespread dry lightning outbreaks in the historical record, quantify their seasonality and spatial extent, and analyze associated large-scale atmospheric patterns. Three of these four atmospheric patterns are characterized by different configurations of ridging over the continental interior and offshore troughing. Understanding the meteorology of dry lightning across this region can inform forecasting of possible wildfire ignitions and is relevant for assessing changes in dry lightning and wildfire risk in climate projections.
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- PAR ID:
- 10369243
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research: Climate
- Volume:
- 1
- Issue:
- 2
- ISSN:
- 2752-5295
- Page Range / eLocation ID:
- Article No. 025001
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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