Significant contributions from:

Todd Schaack and Allen Lenzen (UW-Madison, Space Science and Engineering Center)Mark C. Green (Desert Research Institute)

Shobha Kondragunta, Quanhua (Mark) Liu, Pubu Ciren, and Qiang Zhao (NESDIS/STAR)Sundar Christopher, Eun-Su Yang (University of Alabama, Huntsville)

Development of Proxy ABI Data for the

GOES-R Air Quality Proving Ground (AQPG)

R. Bradley Pierce (NOAA/NESDIS/STAR)

NOAA Air Quality Proving Ground Advisory Group Workshop, September 14, 2010, Baltimore MD

• Extensive efforts are underway to develop and demonstrate the broad range of capabilities that GOES-R data will provide when it becomes available.

• These efforts involve the use of existing satellite and synthetic (model based) data sets for algorithm development and demonstration activities.

Use of existing satellite data has focused on Moderate Resolution Imaging Spectroradiometer (MODIS) and involves the following steps:

1. A fast, Look-up-table (LUT) based radiative transfer scheme was developed to simulate cloud-free radiance fields in 6 ABI bands, i.e., 0.47, 0.64, 0.865, 1.378, 1.61 and 2.25 µm.

2. MODIS derived atmospheric (cloud mask, aerosol optical depth, total column ozone and water vapor) and surface (8-day composite surface reflectance) properties are used to constrain clear sky radiative transfer calculations [Laszlo et al., 2008].

3. Top Of Atmosphere (TOA) radiance fields are generated for developing and validating the ABI aerosol retrieval algorithm.

Development of Proxy ABI Data

April 12,2002

MODIS Based Proxy data set for ABI AOD retrieval studies

MODIS

Retrieved

Development of Proxy ABI Data (cont)

Generation of synthetic proxy datasets allows us to demonstrate the advantages of ABI (MODIS like multi-band retrieval with significantly higher temporal resolution) and involves the following steps:

1. Synthetic high resolution meteorological, aerosol and ozone data are created over the continental US using WRF-Chem and CMAQ air quality simulations.

2. Proxy ABI radiances are generated using radiative transfer modeling capabilities from the Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM)

3. The Synthetic ABI radiances are used as input into the GOES-R ABI Aerosol Optical Depth (AOD) retrievals to simulate what ABI will provide when in orbit

This presentation will focus primarily on the generation of synthetic proxy data sets for two Case Studies:

• August 24, 2006 • May 22-24, 2007

Community Radiative

Transfer Model (CRTM)

ABI SM/AOD Algorithm

GOES-R ABI Radiances at 16 Channels

ABI AOD ABI SM

3D WRF-CHEM met/chemistry/

aerosol fields3D RAQMS

chemistry/aerosol fields

Simulated GOES-R ABI Aerosol Optical Depth (AOD) for Case 1

3D GFS met fields

High resolution (4km) WRF-CHEM Continental US simulationGOES fire detections used for biomass burning emissions

GOES-R ABI AOD Case Study 1: August 24, 2006

From IDEA at http://www.star.nesdis.noaa.gov/smcd/spb/aq/

MODIS/AIRNow/WF-ABBA/850mb Wind Composite

GOES-R ABI AOD Case Study 1: August 24, 2006

Simulated Aerosol Optical Depth (AOD) and Cloud Optical Thickness (COT)

y = 0.98x + 0.11R² = 0.27 n=588

0.00.51.01.52.02.53.03.5

0.0 0.5 1.0 1.5 2.0

WRF

-Che

m A

OD

AERONET AOD

All sites August 24, 2006 episode

Cloud OT category

Average WRF-Chem AOD

Average AERONET AOD

WRF-Chem Carbon AOD

WRF-Chem SO4 AOD

COT <0.1 0.202 0.172 0.110 0.080

COT>0.1 0.614 0.221 0.331 0.270

Comparison with Aeronet Aerosol Optical Depth (AOD)

1:1

y = 0.98x + 0.11R² = 0.27 n=588

0.00.51.01.52.02.53.03.5

0.0 0.5 1.0 1.5 2.0

WRF

-Che

m A

OD

AERONET AOD

All sites August 24, 2006 episode

Cloud OT category

Average WRF-Chem AOD

Average AERONET AOD

WRF-Chem Carbon AOD

WRF-Chem SO4 AOD

COT <0.1 0.202 0.172 0.110 0.080

COT>0.1 0.614 0.221 0.331 0.270

Comparison with Aeronet Aerosol Optical Depth (AOD)

Overestimate in WRF-CHem AOD occurs when clouds (and enhanced sulfate production) are simulated but not observed

1:1

y = 0.42x + 2.49R² = 0.43

0

5

10

15

20

25

30

35

0 20 40 60 80

WRF

-Che

m PM

2.5 (µ

g/m

3 )

IMPROVE PM2.5 (µg/m3)

Comparison with IMPROVE PM2.5

WRF-Chem underestimates PM2.5relative to IMPROVE network

1:1

y = 0.939x + 0.220R² = 0.863

0

5

10

15

20

25

30

0 5 10 15 20 25 30

WRF

-Che

m A

MM

SO

4

IMPROVE AMM SO4

Amm Sulfate

Comparison with IMPROVE SO4

WRF-Chem SO4 in very good agreement with IMPROVE network

1:1

y = 0.245x + 0.172R² = 0.650

02468

10121416

0 5 10 15 20 25 30 35 40

WRF

-Che

m OR

g C

IMPROVE Org C

OC

Comparison with IMPROVE Organic Carbon

WRF-Chem underestimates OCrelative to the IMPROVE network

1:1

Atmospheric Spectroscopy Model

Aerosol and Cloud Optical Model

Surface Emissivity, Reflectivity Models

Forward Radiative Transfer Schemes

Receiver and Antenna Transfer Functions

Jacobian Schemes

Atmospheric State Vectors Surface State Vectors

Community Radiative Transfer Model

Atmospheric Spectroscopy Model

Aerosol and Cloud Optical Model

Surface Emissivity, Reflectivity Models

Forward Radiative Transfer Schemes

Receiver and Antenna Transfer Functions

Jacobian Schemes

Atmospheric State Vectors Surface State Vectors

Community Radiative Transfer Model

INPUT from WRF-CHEM

OUTPUT GOES-RABI Radiances

0.645 micron band MODIS Terra and Aqua L1 radiances (left panel) and 0.64 micron band ABI proxy radiances (right panel)

11.03 micron band MODIS Terra and Aqua L1 radiances (left panel) and 11.2 micron band ABI proxy radiances (right panel)

Comparison between MODIS and simulated ABI radiances (18:30Z on August 24th, 2006)

Comparison between MODIS and simulated ABI radiances (18:30Z on August 24th, 2006)

ABI Channel (micron)

0.470 0.640 0.860 1.380 2.26 3.90 7.30 8.50 9.60 11.20 12.30 13.30

MODIS Channel (micron)

0.469 0.645 0.858 1.375 2.13 3.96 7.33 8.55 9.73 11.03 12.02 13.34

% Diff -35.77 -37.93 -31.96 -23.49 -14.38 -7.53 0.10 4.89 12.45 8.99 12.26 0.07

Spectral dependence of the observed radiances is well represented although the radiances in the simulated visible channels tends to be overestimated, particularly at the shortest wavelengths.

Simulated GOES-R ABI Suspended Matter/Aerosol (SM/AOD) Optical Depth for Case 2

Community Radiative

Transfer Model (CRTM)

ABI SM/AOD Algorithm

GOES-R ABI Radiances at 16 Channels

ABI AOD ABI SM

3D MM5/CMAQ PM25 fields

3D GFS met fields

Moderate resolution (12km) CMAQ South Eastern US simulationGOES Biomass Burning Emissions Product (GBBEP) used for biomass burning emissions

Yang et al., JGR, in review

April – May 2007: 125,000 acres of land burned as estimated by GOES-12 Imager

Comparison of Scaled CMAQ and MODIS AOT within biomass burning plumes

A nearly 1:1 correspondencebetween the CMAQ and MODIS AOT is obtained within biomass burning plumes when the CMAQ AOT is scaled by a factor of 3 (sf=3) which is used for top-down tuning of the GBBEP emissions (FIRE3)

Biomass burning plumes are defined as AOT values (with GBBEP missions) that are 12 times larger then without GBBEP emissions.

Yang et al., JGR, in review

Comparison with AirNow PM2.5 and SEARCH Organic Carbon (OC)

High OC at Southeastern Aerosol Research and Characterization Study (SEARCH) sites indicate the sporadic fire smoke intrusions at Birmingham andAtlanta.

CMAQ FIRE3 improves thethe simulated PM2.5 concentrations, but they are still less than observed at Tallahassee.

Since Tallahassee is atthe boundary of the fire, a small error in wind speed and direction mightcause significant changes in PM2.5 concentrations.

Yang et al., JGR, in review

GOES-R ABI Retrievals (May 24, 2007)

• Gaps in satellite data due to clouds but more coverage overall due to rapid refresh rate.• Information on aerosol type and suspended matter that will be available from GOES-R ABI not available from currently operational GOES satellites.• User/focus group feedback on quality and usefulness of this information very critical to product developers.

ABI Aerosol Type

Summary

MODIS based proxy data sets provide the most realistic estimates of the quality of future ABI aerosol optical depth retrievals. However, they do not demonstrate the advantages of higher temporal resolution that will be provided by ABI.

The synthetic proxy data sets have flaws, as do the forward radiative transfer models used to generate the synthetic radiances, particularly with regard to surface reflectivity.

We rely on the MODIS based proxy data sets to evaluate the accuracy of the ABI retrieval prior to GOES-R launch due to the realistic TOA radiances that can be generated.

We rely on synthetic (model based) proxy data sets to explore the utility of future ABI AOD retrievals for Air Quality Forecasting due to their ability to represent the higher temporal resolution afforded by ABI.