Estimation Of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning

Kosović, Branko; Jiménez, Pedro A.; McCandless, Tyler; Petzke, B.; Massie, Steven; Siems-Anderson, Amanda; DeCastro, Amy; Muñoz‐Esparza, Domingo; Haupt, Sue Ellen

doi:10.1109/igarss39084.2020.9323134

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Dry fuels can be a significant driver of wildfire behavior and smoke emission, and to better understand model sensitivity to fuel moisture, we incorporate recently derived fuel moisture satellite products intro WRF‐Fire as static input conditions (Kosović et al., 2020). Here we use the WRF‐Fire code developed for Colorado Fire Prediction System (CO‐FPS) which allows fuel moisture content (FMC) maps from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments Terra and Aqua to be ingested into our simulations at 1 km resolution (Kosović et al., 2020). Fuel moisture maps (Figures 1c and 1d) are static for each simulation and are usually retrieved around 12 hr before the start of our fire simulations.…”

Section: Methodsmentioning

confidence: 99%

“…High‐resolution topography and fuel categories can be found on the LANDFIRE data distribution site (Department of Interior, Geological Survey, and U.S. Department of Agriculture, 2016; Ryan & Opperman, 2013). The fuel moisture content maps are archived by the National Center for Atmospheric Research (NCAR) and Geoscience Data Exchange (GDEX) (Kosovic et al., 2019). Containment data and fire perimeters can be found at the National Interagency Fire Center (NIFC) Open Data Site (Wildland Fire Interagency Data Service (WFIGS), National Interagency Fire Center (NIFC), National Wildfire Coordinating Group (NWCG) Geospatial Subcommittee, 2021).…”

Section: Data Availability Statementmentioning

confidence: 99%

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Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

Turney,

Saide,

Jimenez Munoz

et al. 2023

JGR Atmospheres

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Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts.

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Section: Methodsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

Turney,

Saide,

Jimenez Munoz

et al. 2023

JGR Atmospheres

View full text Add to dashboard Cite

show abstract

“…NEXRAD and GOES‐17 data can be obtained from the Amazon cloud at https://registry.opendata.aws/noaa-nex-rad/ (NEXRAD on AWS, 2023) and https://registry.opendata.aws/noaa-goes/ (last accessed for both links 1 November 2021) (NOAA Geostationary Operational Environmental Satellites 16 & 17, 2021). Gridded fuel moisture data sets are available at https://www.climatologylab.org/gridmet.html (Abatzoglou, 2013) and https://gdex.ucar.edu/dataset/fuel_moisture_content.html (Kosovic et al., 2019; Schreck et al., 2023). RAWS observations are available at https://mesowest.utah.edu/ which is described in Horel et al.…”

Section: Data Availability Statementmentioning

confidence: 99%

Sensitivity of Simulated Fire‐Generated Circulations to Fuel Characteristics During Large Wildfires

Roberts,

Lareau,

Juliano

et al. 2024

JGR Atmospheres

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Coupled fire‐atmosphere models often struggle to simulate important fire processes like fire generated flows, deep flaming fronts, extreme updrafts, and stratospheric smoke injection during large wildfires. This study uses the coupled fire‐atmosphere model, WRF‐Fire, to examine the sensitivities of some of these phenomena to the modeled total fuel load and its consumption. Specifically, the 2020 Bear Fire and 2021 Caldor Fire in California's Sierra Nevada are simulated using three fuel loading scenarios (1X, 4X, and 8X LANDFIRE derived surface fuel), while controlling the fire rate of spread using observations. This approach helps isolate the fuel loading and consumption needed to produce fire‐generated winds and plume rise comparable to radar observations of these events. Increasing fuel loads and corresponding fire residence time in WRF‐Fire leads to deep plumes in excess of 10 km, strong vertical velocities of 40–45 m s−1, and combustion fronts several kilometers in width (in the along wind direction). These results indicate that LANDFIRE‐based surface fuel loads in WRF‐Fire likely under‐represent fuel loading, having significant implications for simulating landscape‐scale wildfire processes, associated impacts on spread, and fire‐atmosphere feedbacks.

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Estimation Of Fuel Moisture Content by Integrating Surface and Satellite Observations Using Machine Learning

Cited by 2 publications

References 6 publications

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

Sensitivity of Simulated Fire‐Generated Circulations to Fuel Characteristics During Large Wildfires

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