CFBM simulations and verification dataset for sheltered and unsheltered wildland fires
d581585
In this study we simulated the fire evolution of four wildland fires with the Community Fire Behavior Model (CFBM). There are two unsheltered ground fires (no canopy: Boone Draw, BD, and Murphy, MY, fires) and two sheltered fires (spreading under the canopy; Hayden Pass, HP, and Indian Valley, IV, fires). For the unsheltered fires we use two options to calculate the location of the mid-flame height. Option 1 uses the fuel bed depth (opt1) whereas option 2 uses Byram's equation (opt2). For the sheltered fires we also use two values for the wind adjustment factor: 0.1 and 0.2 (opt3_0p1 and opt3_0p2, respectively). Simulations are compared against observed fire perimeters and active-fire detections from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. For all the simulations this dataset includes: 1) the WRF simulations (1 km grid spacing, output every 15 minutes) used to drive the CFBM; 2) the GEOGRID files used to define the CFBM domains (100 m), 3) the CFBM simulations (e.g., fire perimeter, 100 m fuel types, elevation) collocated with observed perimeters, and 4) the observed perimeters and the VIIRS retrievals used for evaluation.
dataset
https://gdex.ucar.edu/datasets/d581585/
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function: information
https://gdex.ucar.edu/datasets/d581585/dataaccess/
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climatologyMeteorologyAtmosphere
dataset
revision
2021-03-30
Suomi-NPP > Suomi National Polar-orbiting Partnership
WRF > Weather Research and Forecasting (WRF) Model
revision
2026-05-13
EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > FIRE ECOLOGY
EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > FIRE ECOLOGY > WILDLAND FUELS
revision
2026-05-13
2016-07-12
2018-09-14
publication
2026-05-14
notPlanned
Creative Commons Attribution 4.0 International License
None
pointOfContact
NSF NCAR Geoscience Data Exchange
name: NSF NCAR Geoscience Data Exchange
description: The Geoscience Data Exchange (GDEX), managed by the Computational and Information Systems Laboratory (CISL) at NSF NCAR, contains a large collection of meteorological, atmospheric composition, and oceanographic observations, and operational and reanalysis model outputs, integrated with NSF NCAR High Performance Compute services to support atmospheric and geosciences research.
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pointOfContact
2026-05-14T19:09:26Z