Potential use of TEMPO AOD & NO2 retrievals to support wild fire plume & O3 & PM2.5 forecast in National Air Quality Forecasting Capability (NAQFC) Pius Lee, Youhua Tang, Tianfeng Chai, Li Pan, Hyuncheol Kim, Daniel Tong and Barry Baker NOAA Air Resources Laboratory airquality.weather.gov TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

Use satellite data to improve NAQFC Data assimilation: Adjust model initial conditions and/or emissions. Verification: Track model performance over certain regions and/or certain periods in contrary with traditional surface monitors that has limited spatial and temporal coverages. TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

NAQFC use near-real-time satellite data NAQFC is providing daily forecast for O3 and PM2.5 using CMAQ driven by NAM meteorology; Besides anthropogenic and biogenic emissions, wildfire, wind-blown dust emissions are important; Pollutant inflows from outside the CONUS, such as influences from long-range transported Sahara dust and Canadian forest wildfires should be quantified. TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

Smoke forecasts with HYSPLIT: Example of data assimilation to constrain emission Smoke forecasts with HYSPLIT: NOAA NESDIS HMS Smoke and fire detection Smoke column from the HYSPLIT model (blue) and NESDIS Hazardous Mapping System (orange) Incorporates imagery from NOAA and NASA satellites (GOES-West, GOES-East, Terra/Aqua MODIS, AVHRR on NOAA-15/-17/-18) Provides fire locations, starting time and duration NOAA 16 (GOES-R) FRP 89.5W USFS’s BlueSky: Estimate PM2.5 emissions and plume rise Emission is aggregated and assumptions made for the forecasting period TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

Derived from NESDIS GASP Comparison in dust concentration in the PBL at 18Z on Aug 17 2015 HYSPLIT Derived from NESDIS GASP

Top-down estimation of wildfire smoke emission based on HYSPLIT model and NESDIS GOES Aerosol/Smoke products (GASP) to improve smoke forecasts An independent HYSPLIT simulation starting at each HMS fire location with given starting time and duration is run with unit source, at several possible release height to generate a Transfer Coefficient Matrix (TCM). Source terms are solved by minimizing a cost function built to mostly measure the differences between model predictions and observations, following a general data assimilation approach. Hourly gridded GASP Source: Chai et al. AE 2015 TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

NAQFC PM2.5 forecasts with CMAQ BlueSky to derive wildfire emission similar to HYSPLIT added to CMAQ PM2.5 forecasts in the operational NAQFC modeling system since Feb 2016 NAQFC without fire source NAQFC with fire source August 21, 2015 1hr PM2.5 Max

State-level Changing Factors Con’d example of data assimilation to constrain emission NO2 Emission Data Assimilation (EDA): Use fused satellite (OMI) and ground (AQS) data to trend emission changes Fusing AQS & OMI State-level Changing Factors Comparison of OMI and AQS (x100) Samples Source: Tong et al., AE 2015; Tong et al., GRL 2016 (2005 to 2012) 8

MDA8 O3 (ppb) regional statistics for August 2015 Day-1 performance obs Bias Normalized mean bias% RMSE Coeff corr, r Index of agreement CON NAQFC 40.0 6.8 17.0 11.5 0.70 0.60 With EDA 3.1 7.8 9.8 0.64 PC 45.2 0.12 0.27 10.0 0.85 0.72 -1.1 -2.4 9.9 RM 48.0 2.1 4.9 8.7 -1.8 -3.6 8.4 NE 40.2 9.7 31.4 12.5 0.80 0.55 3.9 15.5 8.2 0.65 UM 36.0 9.0 25.0 11.4 0.86 0.58 4.5 12.33 8.8 0.82 SE 33.2 10.1 30.3 0.54 6.1 18.1 9.5 0.81 LM 34.0 11.6 33.5 14.4 0.75 0.47 26.5 13.5 0.48 TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

24 hour average PM2.5 concentrations regional statistics for August 2015 [µg/m3] Sample size Obs. Mean Bias RMSE Corr. coeff. CONUS 13100 NAQFC 10.0 6.78 -3.22 10.12 0.34 With EDA 9.08 -0.92 8.40 0.66 PC 3000 14.0 5.67 -8.33 14.98 0.57 12.22 -1.78 12.14 0.63 RM 1235 12.9 5.56 -7.46 19.46 0.61 12.91 0.01 15.77 0.70 NE 1850 7.8 7.91 0.11 3.78 0.56 7.74 -0.06 3.87 0.52 UM 2400 7.7 8.08 0.38 4.02 0.54 8.05 0.35 3.98 0.53 SE 2050 9.2 6.85 -2.35 5.35 0.29 6.72 -2.48 4.61 LM 1550 10.2 6.70 -3.30 5.79 0.25 7.70 -2.30 5.28 0.31 CONUS-wide statistics are improved. Largest improvements are for wildfire-impacted western US regions

MODIS NAQFC derived via CRTM valid on July 1 2011 Example of data assimilation for improving initial conditions MODIS AOD (collection 5.1) compared to a prior CMAQ AOD calculated with CRTM (Community Radiative Transfer Model) MODIS NAQFC derived via CRTM valid on July 1 2011 TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

Example of data assimilation for improving initial conditions Surface ASO4J (accumulation-mode sulfate) increment after GSI-AOD assimilation

Source: Tang et al., JAWMA 2014 Example of data assimilation for improving initial conditions Surface PM2.5 base case (left) and the run with GSI assimilation (right) including both AOD and surface PM2.5 assimilations Source: Tang et al., JAWMA 2014 TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

e.g. of Verification: Nested 4km CMAQ run in SENEX Field Campaign June 14 2013 TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

Similar comparison, but for July 17 2013 TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

Both GOCART and RAQMS have D.A. CALIPSO satellite detected a Sahara dust intrusion episode in Aug, 2016 Both GOCART and RAQMS have D.A. TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard

Summary: Satellite data improves NAQFC Data assimilation: Constrain emission strengths and locations: both intermittent and continuous sources; Adjust initialization fields; Adjust lateral boundary conditions by considering global models (e.g. RAQMS from Pierce http://raqms-ops.ssec.wisc.edu/) for intruding plumes constrained by satellite data. Verification (potentially input for data fusion): Identify discrepancies and pin-point potential weaknesses at fine temporal and spatial resolutions; Explore finer resolution PM forecast to be fused with satellite-data derived surface level PM concentration. TEMPO Sc. Team Meeting May 31 – June 1 2017, Harvard