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Ingesting GOES-16 fire radiative power retrievals into Warn-on-Forecast System for Smoke (WoFS-Smoke)

Background: The record number of wildfires in the United States in recent years has led to an increased focus on developing tools to accurately forecast their impacts at high spatial and temporal resolutions. Aims: The Warn-on-Forecast System for Smoke (WoFS-Smoke) was developed…
Person:
Year: 2024
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Effects, Weather
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, National
Keywords: data assimilation, ensemble, GOES-R, NWP - numerical weather prediction, probabilistic forecasting, smoke forecasting, weather radar, wildfires, Colorado, Texas, Oklahoma, Arkansas

Current and future patterns of global wildfire based on deep neural networks

Global climate change and extreme weather has a profound impact on wildfire, and it is of great importance to explore wildfire patterns in the context of global climate change for wildfire prevention and management. In this paper, a wildfire spatial prediction model based on…
Person:
Year: 2024
Type: Document
Source: FRAMES
Topic(s): Climate, Emissions and Smoke, Fire Behavior, Fire Occurrence, Fire Prevention, Models, Weather
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: fire management, GFED - Global Fire Emissions Database, convolutional neural network, area burned