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An Air Quality Proving Ground (AQPG) for GOES-R

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.

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An Air Quality Proving Ground (AQPG) for GOES-R

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  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

  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. 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 GOES <---> GOES - R

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

  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, + …..?)

  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

  9. Evaluation of the Case Use GOCART aerosol module - predicts concentrations of seven aerosol species (SO4, 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 NH4 not included so added 0.375*SO4 to account for ammonium in ammonium sulfate Added larger dust and sea salt categories to obtain PM10

  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 SO4 Good correlation for OC, but WRF-Chem biased factor of 3 low - not surprising as sources are not inventoried

  14. The overall WRF-Chem PM2.5 prediction is dominated by this under-prediction of OC

  15. Impact of speciation on AOD Bondville- WRF-Chem AOD close to AERONET AOD except when WRF-Chem predicts clouds- much higher SO4 AOD predicted Howard- Increase in SO4 and OC AOD with WRF-Chem clouds (growth of hydrophilic OC and well as SO4)

  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

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