Georgia Institute of Technology

1. Continued improvements of  air quality forecasting through emission adjustments using surface and satellite data &Estimating fire emissions: satellite vs. bottom-up Talat Odman, Yongtao Hu and Ted Russell School of Civil & Environmental Engineering, Georgia Institute of Technology With thanks to Pius Lee and the NOAA ARL Forecasting Team AQAST Meeting, January 15th, 2014 Georgia Institute of Technology

2. Objective Improve air quality forecasting accuracy using earth science products through dynamic adjustments of emissions inventories and simulation of wildland fire impacts • Air quality forecasting is an integral part of air quality management. • Current forecasting accuracy calls for improvement. • Forecasting with 3-D models relies on accuracy of emissions. • Emission inventories are typically at least 4 years behind and “growth factors” are outdated. • Wildland fires are becoming an increasingly important contributor to PM and ozone. • Fire is one of the most uncertain emission categories as multi-year averages of past fires do not represent future fires. Georgia Institute of Technology

3. Hi-Res Forecasting System • Based on SMOKE, WRF and CMAQ models • Forecasting ozone and PM2.5 since 2006 • 48-hour forecast at 4-km resolution for Georgia and 12-km for most of Eastern US • Used by GA EPD assisting their AQI forecasts for Atlanta, Columbus and Macon • Potentially useful for other states Hi-Res Modeling Domains Georgia Institute of Technology

4. Hi-Res performance during 2006-2013 ozone seasons for Metro Atlanta Ozone PM2.5 Georgia Institute of Technology

5. Inverse Modeling Approach for Adjusting Emissions An emissions and air quality auto-correction system utilizing near real-time satellite and surface observations Minimizes the differences between forecasted and observed concentrations (or AOD) With minimum adjustment to source emissions Using contributions of emission sources calculated by CMAQ-DDM-3D Source contributions can be used for dynamic air quality management.(e.g., fires) Georgia Institute of Technology

6. Inverse Model Formulation • Solve for Rj that minimizes 2 DDM-3D calculated sensitivity of concentration i to source j emissions weigh for the amount of change in source strengths total number of sources total number of obs emission adjustment ratio uncertainties Georgia Institute of Technology

7. Off-line tests using “real-time” PM2.5 observations • Surface PM2.5 data from six sites in Atlanta • Direct use of satellite data (AOD) was problematic because of much larger uncertainties compared to surface data. • AOD will be “fused” to PM2.5 concentration fields to provide “real-time” spatial patterns. Georgia Institute of Technology

8. DDM-3D sensitivities calculated for week1: Dec. 1-7, 2013 Shown for select day Dec. 2, 2013 Obtained emissions adjustments ratios (Rj) Georgia Institute of Technology

9. PM2.5 Forecasting Performance for week 2: Dec. 08-14, 2013 with emissions adjustments Dec.11, 2013 PM2.5 Concentration without emissions adjustments Dec. 11, 2013 PM2.5 Concentration Georgia Institute of Technology

10. Comparison of Fire Emission Estimates: Satellite vs. Bottom-up • Both have roles in improving accuracy of fire impact forecasts: Satellite for wildfires and bottom-up for prescribed burns. • Global Biomass Burning Emissions Product (GBBEP) is currently using Fire Radiative Power from GOES • Buttom-up estimates use fuel-loads, consumption and emission factors. • GBBEP and buttom-up emissions compared for Williams fire, a 200 acre chaparrel fire in California on November 11, 2009 Akagi et al., ACP, 2012 Georgia Institute of Technology

11. Comparison of Emission Estimates: Williams Fire • Buttom-up PM2.5 emission estimates are ~50% larger than GBBEP emissions • Aircraft measured aerosol light scattering, converted to PM2.5 and compared to modeled PM2.5 concentrations Georgia Institute of Technology

12. Comparison of Modeled PM2.5 to Aircraft Measurements • Uncertainties in dispersion modeling (WS, WD, plume height, etc.) must be reduced to better evaluate emission estimates. Georgia Institute of Technology

13. Conclusions • Dynamic emissions inventory adjustment dramatically improving PM forecast accuracy in off-line testing. On-line testing and implementation underway • Large bias in dust emissions in winter corrected • Improved approach to assimilating AOD and PM measurements underway • Bottom-up and satellite-based fire emission estimates being improved with airborne smoke measurements • Fire emission contribution forecasts underway for dynamic prescribed-burn management Georgia Institute of Technology

14. Poster • Davis et al., Nitrogen Deposition (Tiger Team Project)

15. Acknowledgements • 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

16. Georgia Emission Totals (tons/yr) Georgia Institute of Technology

17. DDM-3D sensitivities calculated for week1: Jul. 6-12, 2011 Shown for Jul. 11, 2011 Emission adjustments ratios (Rj) Georgia Institute of Technology

18. PM2.5 Forecasting Performance of week2: Jul. 13-19, 2011 with emissions adjustments Jul.15, 2011 PM2.5 Concentration without emissions adjustments Jul. 15, 2011 PM2.5 Concentration Georgia Institute of Technology