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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
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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IDEA (http://star.nesdis.noaa.gov/smcd/spb/aq/)
IDEA (http://star.nesdis.noaa.gov/smcd/spb/aq/)
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GOES Aerosol and Smoke Product (GASP)
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!
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GOES <---> GOES - RGOES <---> 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
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Spectral (wavelength dependent) thresholds can separate thick smoke, light smoke, and clear sky conditions
Aerosol Detection Physical Description
Heavy smoke
clearsmoke
Clear Regime
SmokeRegime
Thick SmokeRegime
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Air Quality Proving GroundAir 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, + …..?)
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AQPG WorkflowAQPG Workflow
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AQPG Case 1 - Aug 20-24, 2006AQPG 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
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Evaluation of the CaseEvaluation 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
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
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Contour map of IMPROVE network particulate sulfur (8/24/06)
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Contour map of IMPROVE organic carbon for 8/24/06
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GOES and WRF-Chem AOD show similar patterns
WRF-chem.gif
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ResultsResults
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
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The overall WRF-Chem PM2.5 prediction is dominated by this under-prediction of OC
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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)
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6:00 9:00 12:00 15:00 18:00 21:00 0:00 3:00 6:00
Cloud OD
AOD
Bondville
AOD tot AOD C AOD SO4 AERONET Cloud OT
01002003004005006007008009001000
0
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6:00 9:00 12:00 15:00 18:00 21:00 0:00 3:00 6:00
Cloud OD
AOD
Howard
AOD tot AOD C AOD SO4 AERONET Cloud OT
Impact of speciation on AOD
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Next StepsNext 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