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

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

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

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!

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

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

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, + …..?)

AQPG WorkflowAQPG Workflow

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

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

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

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

GOES and WRF-Chem AOD show similar patterns

WRF-chem.gif

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

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

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)

0

1

2

3

4

5

6

7

8

0

0.2

0.4

0.6

0.8

1

1.2

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

1

2

3

4

5

6

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

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