1. 1 Assimilate Column and Surface Data for Air Quality Forecasting and Chemical Reanalysis 13 th JCSDA May13-15, 2015, College Park Pius Lee 1, Greg Carmichael 2, Yongtao Hu 3, Edward Hyer 4, Yang Liu 5, Dick McNider 6, Brad Pierce 7, Robert Atlas 8, Ted Russell 3, Sid Boukabara 9, Youhua Tang 1,10, Hyuncheol Kim 1,10, Li Pan 1,10, Sean Casey 9 and Daniel Tong 1,10 1 Air Resources Laboratory / NOAA, NCWCP, College Park, MD 2 College of Engineering, University of Iowa, Iowa City, IA 3 School of Civil and Environmental Engr., Georgia Institute of Technology, Atlanta, GA 4 Naval Research Laboratory, Monterey, CA 5 Department of Environmental Health, Emory University, Atlanta, GA 6 Department of Atmospheric Science, University Alabama, Huntsville AL 7 National Environmental Satellite and Information Service (NESDIS), Madison, WI 8 Atlantic Oceanographic & Meteorological Lab., Miami, FL 9 NOAA/NESDIS/Joint Center for Satellite Data Assimilation (JCSDA), College Park, MD 10 Cooperative Institute for Climate and Satellite, University of Maryland College Park, MD

2. Courtesy: Dan Costa “New Directions in Air Quality Research at the US EPA” 2 Dr. L. Uccellini, May14 2015 “Impact-based DSS” 13 th JCSDA May13-15, 2015, College Park

3. Gridded forecast guidance products On NWS servers: airquality.weather.gov and ftp- serversOn NWS servers: airquality.weather.gov and ftp- servers On EPA serversOn EPA servers Updated 2x dailyUpdated 2x daily Verification basis, near-real time: Ground-level AIRNow observationsGround-level AIRNow observations Satellite smoke observationsSatellite smoke observations Twice daily surface O3 concentration forecast NAQFC 3 13 th JCSDA May13-15, 2015, College Park

4. 4 Constrained Satellite Products Global Assimilation + AQ Assessments + State Implementation Plan Modeling + Rapid deployment of on- demand rapid- response forecasting; e.g., new fuel type,…, etc. + Health Impacts assessments + Demonstration of the impact of observations on AQ distributions + Ingestion of new AQAST products into operations http://acmg.seas.harvard.edu/aqast/projects.html AQAST Project: Air Quality Reanalysis ( Translating Research to Services ) 13 th JCSDA May13-15, 2015, College Park

5. WRF_ARW-MCIP-CMAQ forward model CMAQ 4.7.1 WRF-ARW (LCC) (42  -P model Layers) LBC from GFS Projection of endo-domain intermitten sources: Obs’d wild fire and prescribed burns EPA Emissions Inventory + simple obs-based adjustment Hourly 3-D Gridded Chemical Concentration MCIP MODIS- AOD-based adjusted IC, BC: RAQMS C-grid Column integrated AOD C-grid 42  -P met. Layers C-grid 13 th JCSDA May13-15, 2015, College Park

6. Widely used data assimilation schemes in Air Quality (AQ): 6 3DVAR e.g. in NCEP Ensemble Adjustment Kalmar Filter (KF) e.g. in trained Bias Correction schemes Optimal Interpolation: is a truncated extended KF Continuity equation for species φ i in generalized co-ordinate (Byun and Schere 2006) 13 th JCSDA May13-15, 2015, College Park

7. Optimal Interpolation (OI) OI simplifies the extended Kalman filter formulation (Dee et al. Q. J. R. Meteor. Soc. 1998) by limiting the analysis problem to a subset of obs. Obs far away (beyond background error correlation length scale) have no effect in the analysis. Injection of Obs through OI takes place at 1800 UTC daily. 7 13 th JCSDA May13-15, 2015, College Park

8. Estimate Model Error Statistics w/ Hollingsworth-Lonnberg Method At each data point, calculate differences between forecasts (B) and observations (O) Pair up data points, and calculate the correlation coefficients between the two time series Plot the correlation as a function of the distance between the two stations, Alternatively 3DVar: M. Pagowski Q.J.R.Meteor. 2010 13 th JCSDA May13-15, 2015, College Park

9. MODIS AOD & AIRNow PM 2.5 assimilated For initial condition adjustment 9 00Z 06Z 12Z 19Z AIRNOW PM2.5, PM10, Ozone MODIS AOD (Terra and Aqua) 14Z17Z 18Z 13 th JCSDA May13-15, 2015, College Park

10. Total AOD assimilation tests Base case: CMAQ4.7.1 without any data assimilation OI forecast: CMAQ results after assimilating previous day AOD observations OI Analysis: CMAQ results after assimilating same day AOD, for next day forecast Fine mode AOD assimilationTotal AOD assimilation 10 18Z 17Z 20Z OI: MODIS total AOD OI: AIRNow pm2.5 13 th JCSDA May13-15, 2015, College Park

11. 11 Transmittance AIRNow Cloud-obs Photolysis rates Isoprene & PAR done Yet to do 13 th JCSDA May13-15, 2015, College Park

12. 12 Analysis field Verification: AIRNOW O 3 & PM 2.5 over NE 13 th JCSDA May13-15, 2015, College Park

13. 13  WRF 3.2.1 for meteorological fields NCEP North American Regional Reanalysis (NARR) 32- km resolution inputs NCEP ADP surface and soundings observational data MODIS landuse data for most recent land cover status 3-D and surface nudging, Noah land-surface model  SMOKE 2.6 for CMAQ ready gridded emissions NEI inventory projected to 2011 using EGAS growth and existing control strategies BEIS3 biogenic emissions based on BELD3 database GOES biomass burning emissions: ftp://satepsanone.nesdis.noaa.gov/EPA/GBBEP/  CMAQ 4.6 revised to simulate gaseous & PM species SAPRC99 mechanism, AERO4, ISORROPIA thermodynamic, Mass conservation, Updated SOA module (Baek et. al. JGR 2011) for multi- generational oxidation of semi-volatile organic carbons Analysis fields as IC and forecast with Total AOD Assimilation basis for LBCs 12 km 36 km 4 km IC/BC of Base and AOD_DA cases DISCOVER-AQ GA_Tech 4 km 13 th JCSDA May13-15, 2015, College Park

14. 14 Analysis field Verification: AIRNOW O 3 & PM 2.5 over BW SIP 13 th JCSDA May13-15, 2015, College Park

15. PM2.5 24h avgObs meanMean biasRMSECorr. Coef. L4212.3-7.710.40.4 L42RAQMS12.3-5.48.50.5 L42-Fire112.3-7.610.60.3 L42-Fire1-OI412.3-1.116.650.61 CONUS PM2.5 24h avgObs meanMean biasRMSECorr. Coef. Base19.8-0.6724.180.49 With L42-Fire1-OI4 field to derive LBC 19.81.155.790.53 15 DISCOVER-AQ GA_Tech 4 km Application of analysis field for State Implementation Planning 13 th JCSDA May13-15, 2015, College Park

16. Forecast Model Simulated Observations Validation of End-to-End OSSE Set-up Nature Runs DAS OSSE Metrics OSSE TESTBED COMPONENTS - Current ECMWFGLA.25 T213GLA.5° T511(MM5) T799(WRF) Validation and Augmentation Valid. Cyclones Mean fields Clouds Precip Aug. Clouds Aerosols Turbulence Current Scatterometer Rawinsonde Surface Aircraft TOVS AIRS AMV Future DWL CMIS Molnya UAS CL Balloon GPS Sounding Buoy Rocket - Realism of simulated observations and input - Impact model not too close to NR Global GFS FIM Regional WRF HWRF NAM CMAQ Cyclones, Fronts, Jets Anomaly Correlation Air Traffic Routing Precip.Utility Load Mgmt. Current GSI 4DVAR EnKF H*Wind Targeting Observation schemes 16 13 th JCSDA May13-15, 2015, College Park

17. 17 13 th JCSDA May13-15, 2015, College Park

18. AQ study using 2 GFS simulations 18 CONTROL T382(~38km); 3D-Hybrid (Var & EnKF); No AIRS_G13 Simulated observations for July-August 2005 (T511 ECMWF Nature Run (NR)) assuming 2012 observation system All instruments (conventional, GPS radiation occultation, radiance) operational in July-August 2012, with addition of Special Sensor Microwave Imager Sounder (SSMIS)-F16,F17,F18 Random-errors added to all radiance observations using modified version of R. Errico’s (GMAO) error-addition code Two week spin-up, 47-day experiment period (20050716-20050831) AIRS As CONTROL but w/AIRS_G13 (AIRS instrument in the location of GOES-13, 75°W) Simulated from T511 NR by Z. Li, U. Wisconsin, using Stand Alone Radiation Transfer Algorithm (SARTA) (compared to CRTM for JCSDA- simulated radiances) Random-errors added using expected error distribution for AIRS_AQUA Atlas et al. BAMS 2011 doi:2010BAMS2946.1 13 th JCSDA May13-15, 2015, College Park

19. Preliminary VSDB Results 19 Right: RMSE for 500 hPa geopotential height (forecast hour on horizontal axis) Lower figure: difference between mean RMSE, AIRS-CONTROL Red boxes: 95% confidence interval; counts outside these bounds are considered statistically significant Comparisons done with respect to T511 NR Here, the experiment with AIRS_G13 shows significant reduction in RMSE for days 1, 2 Full results can be viewed on JCSDA website: http://www.jcsda.noaa.gov/vsdb/users/s casey/prs382hwa/vsdb.php http://www.jcsda.noaa.gov/vsdb/users/s casey/prs382hwa/vsdb.php I created a “rough scorecard” based on the Version 16 results (next slide) CONTROL AIRS CONTROL 13 th JCSDA May13-15, 2015, College Park Courtesy: Sean Casey

20. O3 on 20050805 CONTROL- standard GFS AIRS-assisted GFS Localized improvement in r & other metrics O3 on 20050805 O3 on 20050803 Spatial distribution of correlation coefficient for surface O3 on Aug 3,5 2005 20 13 th JCSDA May13-15, 2015, College Park

21. 21  Configured AQ forward-model vertical structure matching that of GFS  The reanalysis forward model is tested and used to generate July 2011 analysis fields for MDE for SIP modeling – GATech partnered SIP modelers  Data Set assimilated: RAQMS (MLS, OMI O3, MODIS AOD); HMS Fire; GOES cloud fraction for photolytic rate correction; MODIS AOD; AIRNow O 3, PM 2.5  Next sets: lightning NOx; OMI SO2; PAR adjustment by retrievals  Verification to include data from O 3 lidar network  Begin transition of assimilation algorithm to GSI  Production mode generation of analysis field for 2010 (HTAP Collaboration) OSSE accomplishment:  Performed GFS-WRF-CMAQ sensitivity runs to investigate AIRS-assisted GFS  AIR-assisted GFS-WRF-CMAQ does improve surface O3 and PM2.5 forecast  The degree of improvement is small compared to Chemical Data assimilation on CMAQ  The Chemical Data Assimilation (CDA) on PM2.5 benefits stronger than that for O3 Summary 13 th JCSDA May13-15, 2015, College Park

22. 22 EXTRA SLIDES Contact: Pius.Lee@noaa.gov Pius.Lee@noaa.gov http://www.arl.noaa.gov/ 13 th JCSDA May13-15, 2015, College Park

23.  This study aims to build a prototype  User friendliness and reliability are keys to success: e.g.. Goal: A user friendly downloadable archive 23 13 th JCSDA May13-15, 2015, College Park

24. MODIS (Moderate Resolution Imaging Spectroradiometer) AOD http://terra.nasa.gov/About/ Orbit:705 km, 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua) Swath Dimensions: 2330 km (cross track) by 10 km (along track at nadir) Spatial Resolution: 250 m (bands 1-2) 500 m (bands 3-7) 1000 m (bands 8-36) Courtesy :NESDIS 24 National correlation map between AIRNow measurement and MODIS AOD Typically good correlation between surface PM 2.5 and AOD retrieved by MODIS Typically good correlation between surface PM 2.5 and AOD retrieved by MODIS 13 th JCSDA May13-15, 2015, College Park

25. 25 Analysis field Verification: AIRNOW O 3 & PM 2.5 over CONUS 13 th JCSDA May13-15, 2015, College Park