Office of Research and Development Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory Use of Remote Sensing Information in Regional Air Pollution Modeling: Examples and Potential Use of VIIRS Products Rohit Mathur Atmospheric Modeling and Analysis Division, NERL, U.S. EPA VIIRS Aerosol Science and Operational Users Workshop, November 21-22, 2013, College Park, MD Acknowledgements: George Pouliot, Xing Jia, Robert Gilliam, Jon Pleim

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Motivation Applications of regional AQ models are continuously being extended to address pollution phenomenon from local to hemispheric spatial scales over episodic to annual time scales The need to represent interactions between physical and chemical processes at these disparate spatial and temporal scales requires use of observational data beyond traditional surface networks 2

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Use of Remote Sensing Information in Regional AQMs Evaluation/Verification of model results –High spatial resolution over large geographic regions of remote sensing data is attractive Improve estimates of model parameters –Emissions (e.g, wildland fires, trends/accountability) –Key meteorological parameters (e.g., SST) –Lateral Boundary conditions (LRT effects) –Location and effects of clouds (e.g., photolysis) Chemical data assimilation –Improving short-term air quality forecasts –Identification of model deficiencies Data Fusion/Reanalysis: combining model and observed fields –For use in health, exposure and ecological studies (2012 NRC Report on Exposure Science in the 21 st Century)

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory 4 Improving Model Parameter Estimates: Fire Emissions Courtesy: A. Soja Surface PM 2.5 : June 10-17, 2008 Fire detects have greatly helped with more accurate spatial allocation of emissions, but challenges remain: Injection height/vertical distribution Emission factors (the new approach used soil carbon content) Ground fire detection Courtesy: George Pouliot Observed No Fire NEI-Smartfire New Estimate

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory 5 Significant under-estimation: July Large under-estimation (>2x) in OC in mid-July Diagnosing Model Performance

Evidence of Long-range transport from outside the modeled domain Model picks up spatial signatures ahead of the front, but under-predictions behind the front (LBCs)

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory 7 Distribution of measured carbonaceous aerosol at STN sites within domain Regional enhancement in TCM on July suggests influence of wildfires on air masses advected into the domain Further Evidence 7/14/04 7/15/04 7/16/04 7/13/04 Long Range Transport of Alaskan Plume 7/17/04

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Estimating the Impacts of Alaskan fires through Assimilation of Satellite AOD Retrievals Methodology 1. Model based correlation between AOD and column PM burden (July-August, 2004 data): –[PM] Col.Burden = f(AOD) [PM] col. Burden = AOD (r 2 =0.9) 2. Estimate inferred PM 2.5 burden: –[PM] infer = f(AOD MODIS ) 3. Estimate Difference in PM mass loading: –[PM] infer – [PM] BaseModel 4. Distribute PM 2.5 mass difference vertically between predefined altitudes Above BL: 2.2 – 4 km (based on Regional East Atmospheric Lidar Mesonet (REALM) data); layers Speciation: EPA AP-42 emission factors for wildfires: OC (77%), EC(16%), SO 4 2- (2%), NO 3 - (0.2%), Other(4.8%) CO/PM 2.5 = 10 8 Adjust Model Initial Conditions 16Z on July 19, 2004 Mathur, 2008 (JGR)

CO Comparisons with NASA DC-8 Measurements during ICARTT Assim-Base; 1700Z Enhanced CO associated with concurrently enhanced acetonitrile (CH 3 CN) – chemical marker for BB Assimilation helps improve the model predicted CO distributions Representing the 3D Transport Signature of the Alaskan Plume

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory MODIS AOD Assimilation: Impact on Surface PM 2.5 Model Performance 10 Reduced Bias/Error Improved Correlation AIRNOW July 19-23, 04 STN July 20, 04 Domain median surface levels enhanced by % due to Alaskan fires on different days

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Air Quality – Climate Interactions Establishing Confidence in Simulated Magnitude and Direction of Aerosol Feedbacks Large changes in emissions and tropospheric aerosol burden have occurred over the past two decades –Title IV of the CAA achieved significant reductions in SO 2 and NO x emissions –Large increase in emissions in Asia over the past decade 11 Can models capture past trends in aerosol loading and associated radiative effects? Can the associated increase in surface solar radiation be detected in the measurements (“brightening effect”) and be used to constrain model results? Is the signal (magnitude and direction) detectable in the observations?

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Trend in Aerosol Optical Depth (AOD) MODIS+ SeaWiFS WRF-CMAQ (sf) JJA-average MODIS - level 3 Terra SeaWiFS - level 3 Deep Blue Missing value in MODIS (mostly in Sahara Desert) was filled by SeaWiFS (550nm) 533nm Air Quality – Climate Interaction

Trend in Aerosol Optical Depth (AOD) MODIS+ SeaWiFS WRF-CMAQ(sf) East ChinaEast USEurope JJA-average (2009 minus 2000) from 1990 to 2009

Trend in clear-sky shortwave radiation CERES East ChinaEast USEurope JJA-average (2009 minus 2000) WRF-CMAQ(sf)WRF-CMAQ(nf) brightening Dimming from 1990 to 2009

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory RMSE Change 63% July 1-31: GHRSST - PathFinder Reduction in Error Increase in Error T-2m RMSE Change RMSE and bias reduced with GHRSST. Reduction is even greater compared to NAM 12-km SST data.  Implications for representing Bay Breeze and pollutant transport GHRSST 1-km horizontal resolution global dataset Daily Improving Model Parameter Estimates: Sea Surface Temperature

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Added diagnostic tracers to track impact of lateral boundary conditions: surface-3km (BL) and 3km-model top (FT) –Quantify modeled “background” O 3 “FT” contribution to model background Average: July-August, 2006 Modeled “background” O 3 Accurate representation of aloft pollution critical for simulation of surface “background” Long-Range Transport and “Background” Pollution Levels Significant spatial variability Background could constitute a sizeable fraction of more stringent NAAQS

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Representing Impacts of Long-Range Transport Transport of Saharan Dust: Summer Surface PM concentration in the Gulf states impacted by LRT during July 30-Aug 3 Texas Sites Dust Transport: 850 mb Regional Model Driven by Hemispheric LBCs

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Representing Impacts of Long-Range Transport Impact on Model Performance: July 30-August 3, Bias Difference: Base LBCs - Hemis. LBCs Lower bias in Hemis. Lower bias in Base Vertically varying (time-dependent) LBCs are needed to accurately quantify impacts of LRT on episodic regional pollution as well as “background” pollution

Office of Research and Development Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory Summary Air quality remote sensing data is useful for model evaluation and improvements –What level of quantitative agreement is acceptable? –Need for harmonization between assumptions used in retrieval and CTM process algorithms (e.g., AOD, NO 2 columns) for more rigorous quantitative use Columnar distributions are a good starting point, but there is a need for better vertical resolution –Discern between BL and FT Measurements to characterize transport aloft (and subsequent downward mixing next morning) are needed Improving the characterization of FT predictions in regional AQMs will result in improvements in surface-level predictions Potential for use in chemical data assimilation –Simultaneous information on multiple chemical species –Combining model and observed information on the chemical state of the atmosphere has potential for both human-health and climate relevant endpoints 19