1. Continued improvements of air quality forecasting through emission adjustments using surface and satellite data & Estimating fire emissions: satellite vs. bottom-up Georgia Institute of Technology 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 15 th, 2014

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 –Emission inventories are typically at least 4 years behind and “growth factors” are outdated.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 PM 2.5 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 Georgia Institute of Technology Hi-Res Modeling Domains

4. Georgia Institute of Technology Hi-Res performance during 2006-2013 ozone seasons for Metro Atlanta Ozone PM 2.5 MNB20% MNE25% MNB-10% MNE32%

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

7. Off-line tests using “real-time” PM 2.5 observations Surface PM 2.5 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 PM 2.5 concentration fields to provide “real-time” spatial patterns. Georgia Institute of Technology

8. DDM-3D sensitivities calculated for week1: Dec. 1-7, 2013 Georgia Institute of Technology SourceAreaOn-roadNon-roadPoint Dec. 1-7,20130.170.830.850.97 Obtained emissions adjustments ratios (Rj) Shown for select day Dec. 2, 2013

9. Georgia Institute of Technology PM 2.5 Forecasting Performance for week 2: Dec. 08-14, 2013 Obs (ug/m3)Sim (ug/m3)NFENFB Dec. 11, 20138.5716.5765% Emis adjusted8.4524%2% Dec. 8-14, 20134.6410.0486%85% Emis adjusted5.6254%39% without emissions adjustments Dec. 11, 2013 PM 2.5 Concentration with emissions adjustments Dec.11, 2013 PM 2.5 Concentration

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 Georgia Institute of Technology Akagi et al., ACP, 2012

11. Comparison of Emission Estimates: Williams Fire Buttom-up PM 2.5 emission estimates are ~50% larger than GBBEP emissions Aircraft measured aerosol light scattering, converted to PM 2.5 and compared to modeled PM 2.5 concentrations Georgia Institute of Technology

12. Comparison of Modeled PM 2.5 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. Georgia Institute of Technology 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)

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

17. DDM-3D sensitivities calculated for week1: Jul. 6-12, 2011 Georgia Institute of Technology Emission adjustments ratios (Rj) Shown for Jul. 11, 2011 SourceAreaOn-roadNon-roadPoint Jul. 6-12,20113.341.091.461.10

18. Georgia Institute of Technology PM 2.5 Forecasting Performance of week2: Jul. 13-19, 2011 Obs (ug/m3)Sim (ug/m3)NFENFB Jul. 15, 201111.353.8594%-94% Emis adjusted7.2350%-40% Jul. 13-19, 201114.398.6754%-44% Emis adjusted14.9244%7% without emissions adjustments Jul. 15, 2011 PM 2.5 Concentration with emissions adjustments Jul.15, 2011 PM 2.5 Concentration