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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.

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Presentation on theme: "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."— Presentation transcript:

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 NA09NES 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 NA09NES and through the CREST Cooperative Agreement


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