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Predicting Wildfire Behavior
National Center for Atmospheric Research (NCAR) 26 February, 2020
William P. Mahoney III Director, Research Applications Laboratory
UNDERSTANDING WILDLAND FIRES:How new research can help protect lives and property
This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977.
WILDFIRE BEHAVIOR
National Center for Atmospheric Research (NCAR)
Boulder, Colorado
Designed by modernist architect I. M. Pei in 1961
WILDFIRE BEHAVIOR
• NCAR is a Federally Funded Research & Development Center
• Administered by consortium of 120 North American universities through the University Corporation for Atmospheric Research (UCAR), a not-for-profit 501(c)(3) organization
National Center for Atmospheric Research (NCAR)
WILDFIRE BEHAVIOR
Mesa Lab Aviation Facility Center GreenFoothills Lab Campus
NCAR-Wyoming Supercomputer Center
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Mission • To understand the behavior of the atmosphere and related Earth and geospace
systems
• To support, enhance, and extend the capabilities of the university community and the broader scientific community
• To foster the transfer of knowledge and technology to society
National Center for Atmospheric Research
Chemistry Weather Climate Observations Geoscience Super Computing
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Foundational Advancements in Science & Technology
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Problem to Address
Wildland fires are exceedingly complex phenomena
• Humans cannot integrate all the interacting factors in real-time
• More sophisticated tools are needed that capture interactions between the fire and local atmosphere
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Wildland Fire ComplexityWildland fires generate extreme fire behaviors such as:
• Fire whirls and ‘firenadoes’• Fire wind blowing 10X stronger than ambient winds• Flames bursting ahead of the fire line• Fire blow-ups and firestorms• Pyrocumulus clouds• Fire splitting and merging
These all result from dynamic interactions between a fire and its environment.
Source: David McNew/Getty Images
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Fire Behavior Prediction Data Needs• Local weather observations
– Wind direction and speed– Wind gustiness– Temperature– Humidity– Precipitation amount and type
• Local weather prediction data• Up to date fuel data and local fuel condition• Vegetation state (moisture content)• High resolution topography information
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• Standard and Remote Automated Weather Station (RAWS) – (interagency)
• NWS weather prediction models (GFS, NAM, HRRR)• DOI & USGS fuel datasets (LANDFIRE) State data• USGS digital elevation models• Multi-spectral: Landsat (USGS), VIIRS, MODIS (NASA)• GOES-R series lightning, IR (NOAA)• Aircraft and UAS visible and multi-spectral data
Elko County, NV
USGS and US Dept. of Interior
DEM – Sierra Nevada
NASA - Landsat
Fire Prediction Data Sources
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Wildland Fire Research Contributions
• Forest and rangeland ecology• Combustion physics & dynamics• Fuel data development• Numerical weather prediction• Land surface prediction• Remote and in situ sensing• Coupled modeling systems• Data assimilation• Computer science
To understand fire behavior fundamentals…Source: Dr. Janice Coen, NCAR/MMM
The “universal” fire shape and fire whirls evolve from fire-atmosphere interactions.
WILDFIRE BEHAVIOR
Source: Dr. Janice Coen, NCAR/MMM
Critical Data Need – Accurate Fuel Moisture Content
RAWS observations10-h dead fuel moisture based on MODIS retrievals
(NCAR project funded by NASA AIST)
Daily updates to fuel moisture at grid interval of 1 km!
Source: Dr. Branko Kosovic, NCAR/RAL
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Bringing It All Together
Taking advantage of these important data sources and integrating these research areas provides tremendous opportunities to advance wildland fire management
Yarnell Hill Fire Yarnell, AZ, 6/30/13
Source: Dr. Janice Coen, NCAR/MMM
Wildfire simulation illustrating the dramatic effects of changing surface winds on fire behavior
WILDFIRE BEHAVIOR
High Park, CO Fire Simulation 6/9/12
N
Shown:• Wind speed and direction near ground• Fire location • Heat released• Smoke concentration
1 frame = 1 minute First 23 hours of the fire
Map of fire extent near the end of this period
Source, Janice Coen (NCAR/MMM)
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Wind speed in plane (in m s-1)
Simulated wind in vertical slice through the ignition point
1 frame = 1 minute, beginning 5:45 a.m. 9 June
Height(m)
Distance W-E (m)
High Park, CO Fire Simulation 6/9/12
Source, Janice Coen (NCAR/MMM)
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16
Camp Fire, CA 2018
Source, Janice Coen (NCAR/MMM)
Decision Support for Fire Management
Simulated fire line from Camp Fire at Paradise, CA
Fire Behavior Simulation
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Colorado Fire Prediction System
• Real-time data ingest of weather, fuel, and active fire detection data from the MMA and Visible Infrared Imaging Radiometer Suite (VIIRS)
• Multiple fire model cycles (runs) per day (utilizing updated weather and fire mapping data)
• User ability to select fire prediction location and size (via CO-WIMS)• User ability to input ignition information (via CO-WIMS)• Output customized and formatted to be displayed on CO-WIMS
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Colorado Fire Prediction System
3 KM Colorado1 KM outer subdomain
CAWFE®
Surface Air Temperature
CONUS Scale
Source - NWS
Wildland Fire Scales
~100 meters
CAWFE®
High-Resolution RapidRefresh
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Colorado Fire Prediction System19
Colorado Fire Prediction SystemFully coupled fire-atmosphere model
• Product suite: Fire extent Rate of spread Heat release Flame Length Smoke concentration Significant fire phenomena Turbulence intensity Down/updraft regions Wind shear regions Wind speed & direction Surface air temperature & humidity
Cold Springs Wildfire (near Nederland, Colorado)
July 2016
Source NCAR/RAL
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We need to provide 21st century technologies for the wildland fire community to support risk assessment and management decisions such as:
• Risk assessment & strategic planning• Mitigation effectiveness studies• Management of individual fires• Resource planning for regional operations• Support for prescribed fire planning and execution• Forest and rangeland management and planning• Smoke impacts (AQ) on human health• Forensic evaluations of fire ignition sources• Training
Risk Management for Wildfire Behavior
