1. An Air Quality Proving Ground (AQPG) for GOES-R R. M. Hoff (UMBC GEST/JCET), S. A. Christopher (UAH), F. Moshary (CCNY), S. Kondragunta (STAR), R. B. Pierce (NESDIS/CIMSS), M. Green (DRI), A. Huff (Battelle) GOES-R Proving Ground January 2010 Call R. M. Hoff (UMBC GEST/JCET), S. A. Christopher (UAH), F. Moshary (CCNY), S. Kondragunta (STAR), R. B. Pierce (NESDIS/CIMSS), M. Green (DRI), A. Huff (Battelle) GOES-R Proving Ground January 2010 Call

2. IDEA (http://star.nesdis.noaa.gov/smcd/spb/aq/)

3. GOES Aerosol and Smoke Product (GASP) GASP is derived from a single visible channel and from a 28 day tracking of the darkest pixel in a scene Cannot do what MODIS and other multiwavelength sensors can do!

4. GOES GOES - R  Single wavelength  1/2 hourly scenes  Requires 28 day spin- up  Has a known diurnal bias  Less precise than MODIS AOD  Single wavelength  1/2 hourly scenes  Requires 28 day spin- up  Has a known diurnal bias  Less precise than MODIS AOD  Advanced Baseline Imager (ABI) “MODIS at GEO”  16 spectral channels  Full disk, CONUS, and special scans  5 minute images  AOD should be as good as MODIS  Advanced Baseline Imager (ABI) “MODIS at GEO”  16 spectral channels  Full disk, CONUS, and special scans  5 minute images  AOD should be as good as MODIS

5.  Spectral (wavelength dependent) thresholds can separate thick smoke, light smoke, and clear sky conditions Aerosol Detection Physical Description Heavy smoke clear smoke Clear Regime Smoke Regime Thick Smoke Regime

6. Air Quality Proving Ground  Using MODIS + Models + Ground data in hand, can we create cases that look interesting enough to train users?  NOAA is creating proxy data sets from model data  UMBC/UAH identifying cases which impact multiple areas and stations (UMBC, UAH, UW, CCNY, + …..?)  Using MODIS + Models + Ground data in hand, can we create cases that look interesting enough to train users?  NOAA is creating proxy data sets from model data  UMBC/UAH identifying cases which impact multiple areas and stations (UMBC, UAH, UW, CCNY, + …..?)

7. AQPG Workflow

8. AQPG Case 1 - Aug 20-24, 2006  Mark Green of DRI is working on a case study which exercises the AQPG  This is a case with smoke in the US Northwest and sulfate haze in the east  Period chosen in part because it occurred during the Second Texas Air Quality Study (TexAQS II)  We have a proxy GOES-R product for this case produced by Brad Pierce  “A model is guilty until proven innocent”- Bill Ryan  Mark Green of DRI is working on a case study which exercises the AQPG  This is a case with smoke in the US Northwest and sulfate haze in the east  Period chosen in part because it occurred during the Second Texas Air Quality Study (TexAQS II)  We have a proxy GOES-R product for this case produced by Brad Pierce  “A model is guilty until proven innocent”- Bill Ryan

9. Evaluation of the Case  Use GOCART aerosol module - predicts concentrations of seven aerosol species (SO 4, hydrophobic OC, hydrophilic OC, hydrophobic BC, hydrophilic BC, dust, sea-salt) + “other pm2.5”(p25)  Output at 15 minute intervals  Model PM2.5 calculated as: pm2_5_dry=p25+bc1+bc2+oc1+oc2+dust1+dust2*0.286+ssalt1+ssalt2*0.942+sulfate  NH 4 not included so added 0.375*SO 4 to account for ammonium in ammonium sulfate  Added larger dust and sea salt categories to obtain PM 10

10. Contour map of IMPROVE network particulate sulfur (8/24/06)

11. Contour map of IMPROVE organic carbon for 8/24/06

12. GOES and WRF-Chem AOD show similar patterns WRF-chem.gif

13. Results WRF-Chem does a good job predicting SO 4 Good correlation for OC, but WRF-Chem biased factor of 3 low - not surprising as sources are not inventoried

14. The overall WRF-Chem PM 2.5 prediction is dominated by this under-prediction of OC

15. Bondville- WRF-Chem AOD close to AERONET AOD except when WRF-Chem predicts clouds- much higher SO 4 AOD predicted Howard- Increase in SO 4 and OC AOD with WRF-Chem clouds (growth of hydrophilic OC and well as SO 4 ) Impact of speciation on AOD

16. Next Steps  Several more case studies have been identified  Amy Huff of Battelle Memorial Institute will be forming a user group at the EPA National Air Quality Conference in March  We will have a workshop in August to start training users on the case studies  Funding has been provided by GOES-R program office (Steve Goodman) under cooperative agreement number NA09NES4400022 and through the CREST Cooperative Agreement  Several more case studies have been identified  Amy Huff of Battelle Memorial Institute will be forming a user group at the EPA National Air Quality Conference in March  We will have a workshop in August to start training users on the case studies  Funding has been provided by GOES-R program office (Steve Goodman) under cooperative agreement number NA09NES4400022 and through the CREST Cooperative Agreement