Simulating prescribed fire impacts for air quality management
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Simulating prescribed fire impacts for air quality management

Improving Operational Regional Air Quality Forecasting Performance through Emissions Correction Using NASA Satellite Retrievals and Surface MeasurementsPI: Armistead G. Russell1, Co-Is: Yongtao Hu1, M. Talat Odman1, Lorraine Remer21Georgia Institute of Technology, 2 NASA Goddard Space Flight Center

Primary Stakeholder Clients: Georgia EPD; Georgia Forestry Commission

Simulating prescribed fire impacts for air quality management

M. Talat Odman, Yongtao Hu, Fernando Garcia-Menendez, Aika Yano, and Armistead G. Russell

School of Civil & Environmental Engineering, Georgia Institute of Technology

AQAST Meeting, June 12th, 2012

Georgia Institute of Technology



  • Overview of first year AQAST research

    • Expanded Hi-Res operational forecasting system

    • Forecasting efforts supporting field studies

      • Discover AQ & Fort Jackson Prescribed burn

    • Simulating biomass burning air quality impacts

  • Simulating biomass burning using satellite-derived fire emissions

    • Discover AQ and ARCTAS Campaign

    • Evaluation with ground-based and satellite data

  • Simulating biomass burning

    • Williams, CA prescribed fire

    • Uncertainty and evaluation

  • Related: Bayesian CMAQ-satellite data assimilation

    • Exposure estimation for epidemiologic studies

Georgia Institute of Technology

Simulating prescribed fire impacts for air quality management

Hi-Res: forecasting ozone and PM2.5

48 hr forecast @ 4-km resolution for Georgia and 12-kmfor most states of eastern US

Hi-Res forecasting products are in use by Georgia EPD assisting their local AQI forecasts for multiple metro areas

Hi-Res forecasting products are potentially useful for other states

Georgia Institute of Technology

Simulating prescribed fire impacts for air quality management

AQAST Modeling Domains

  • GOES biomass burning emissions GBBEP used for the ARCTAS and DISCOVER-AQ modeling.

  • Bottom-up estimates of fire emissions used for the Williams Burn and GA-FL wildfire simulations.

Georgia Institute of Technology

Forecasting in support of field studies

Fort Jackson, SC

Forecasting in Support of Field Studies

  • Provided 48 hour pollutant forecasts during Discover –AQ (with Emory)

    • Providing spatially more detailed AQ fields for comparison with observations ( Yang Liu’s poster)

  • Forecasting for Prescribed Burn Study on October 30, 2011 at Fort Jackson, SC

    • Concerned with impacting Columbia

Forecasting with Assimilated PM Fields

  • Using satellite-data-assimilated PM fields as IC/BC in forecasting system (with NOAA ARL, Pius Lee’s presentation)

    • Testing using Discover-AQ campaign period.

Simulating prescribed fire impacts for air quality management

CMAQ simulation: DISCOVER-AQ Campaign

Peak hour surface ozone

Surface 24-hr PM2.5

Performance (Surface networks)

Georgia Institute of Technology

Simulating prescribed fire impacts for air quality management

DISCOVER-AQ Campaign: Comparison with Satellite-derived AOD Fields

CMAQ AOD at 18Z 07022011

CMAQ AOD at 16Z 07022011

Simulated AOD is 25% lower in general

MODIS AOD Terra (L2) 16Z 07022011

MODIS AOD Aqua (L2) 18Z 07022011

Georgia Institute of Technology

Simulating prescribed fire impacts for air quality management

ARCTAS: Northern California Wildfires

June 27, 2008

July 8, 2008

Performance (Surface networks)

Georgia Institute of Technology

Underestimation of surface PM2.5

Simulating prescribed fire impacts for air quality management

ARCTAS: CMAQ–Satellite Comparison

CMAQ AOD at 21Z 06272008

CMAQ surface 24-hr PM2.5 06272008

Simulated AOD is factor of 10 lower in general, though the maximum is 1.2 versus 4.4 (sim vs. obs)

MODIS AOD Aqua (L2) at 21Z 06272008

Simplified treatment of biomass fire plumes may cause issues. There may be missing fires from the GBBEP products.

Estimation of emissions

Estimation of Emissions

  • Fuel load is estimated using photo-series , if available, or satellites

3 years

Fuel Load (tons per acre)

  • Fuel consumption is calculated by CONSUME 3.0.

    • Fuel moisture is a key fire parameter.

  • Emission Factors (EF) are available from field and/or laboratory studies.

    • Fire Sciences Lab in Missoula, MT

Fire progression model rabbit rules a cellular automata free agent model

Fire Progression Model: Rabbit Rules(A cellular automata/free agent model)

Fire Induced Winds

Fuel Density Map (Satellite –derived)

Parameters provided by rabbit rules

Parameters provided by Rabbit Rules

  • No. of updraft cores

  • Vertical velocities

  • Core diameters

  • Emissions as f(t)

Dispersion and transport models

Dispersion and Transport Models

  • Daysmoke is a dynamic-stochastic Lagrangian particle model specifically designed for prescribed burn plumes.

  • AG-CMAQ is the adaptive grid regional air quality model.

  • Daysmoke has been coupled with AG-CMAQ as an inert, subgrid-scale plume model through a process called “handover”.

Williams fire a chaparral burn in ca

Williams fire: A chaparral burn in CA

  • A suite of gases and aerosols and meteorological parameters were measured aboard an aircraft in the plume of Williams fire on 17 November 2009 (Akagi et al. , ACP, 2012).

  • Burn observed by satellites

  • Fuels/burn information is limited.

Modeled plume in pbl and aircraft track

Modeled plume in PBL and Aircraft Track

Unpaired Peaks

Observed = 676 mg/m3

Modeled = 508 mg/m3

Georgia Institute of Technology

Potential sources of uncertainty

Potential Sources of Uncertainty

Sensitivity to PBL Height

Field Study at Eglin AFB, FL

PM2.5 Emissions

Sensitivity to Wind Speed

Under-predicted by 15%

Georgia Institute of Technology

Uncertainty in satellite data

Uncertainty in Satellite Data?

Modeled PM2.5 and Aircraft Track


(regridded from L2 products 10-km resolution at nadir)

Georgia Institute of Technology

Next steps

Next Steps

  • Evaluate using airborne measurements and high resolution, level-3 AOD

    • Injection heights: MISR multi-angle products

    • Column information from satellites can provide information on plume aloft

  • Integrate satellite observations in forecast system

    • Data assimilation, potentially using direct sensitivity analysis

    • Extend 12-km domain

  • Knowledge learned will be applied to inverse modeling

    • Improve burn emissions (mass and injection height)

      • Better predict impacts from prescribed burns

Georgia Institute of Technology



  • NASA

  • Georgia EPD

  • Georgia Forestry Commission

  • US Forest Service

    • Scott Goodrick, Yongqiang Liu, Gary Achtemeier

  • Strategic Environmental Research and Development Program

  • Joint Fire Science Program (JFSP)

  • Environmental Protection Agency (EPA)

Georgia Institute of Technology

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