NESDIS Center for Satellite Applications and Research

Air Quality Products from GOES-R Air Quality Products from GOES-R Advanced Baseline Imager (ABI)Advanced Baseline Imager (ABI)

S. Kondragunta, NOAA/NESDIS/STARS. Kondragunta, NOAA/NESDIS/STARChair, GOES-R Aerosols/Atmospheric Chemistry/Air Quality Chair, GOES-R Aerosols/Atmospheric Chemistry/Air Quality

Application TeamApplication Team

Contributions from:Contributions from:P. Ciren, C. Xu, X. Zhang (IMSG) P. Ciren, C. Xu, X. Zhang (IMSG)

I. Laszlo (NESDIS/STAR) I. Laszlo (NESDIS/STAR) H. Zhang (UMBC)H. Zhang (UMBC)

First Air Quality Proving Ground Workshop

UMBC, September 14, 2010

Outline

• Air Quality Products from current operational GOES satellites

• Examples of user applications

• Limitations of current GOES

• GOES-R Products and capabilities

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3

Aerosol Retrievals from a Geostationary Platform

Single snap shot of MODISComposite image from multiple snap shots of GOES-12

Example Image of GOES AOD Full Disk

• Quantitative measure of atmospheric aerosol loading that has been shown to be a proxy for surface particulate matter pollution, PM2.5 (aerosol mass in µg/m3).

Single channel retrieval using measured visible channel reflectances from GOES Imager.

IR channels used in identifying clouds.

Physical retrieval that separates contribution of surface from aerosols.

Retrievals over clear sky and dark vegetative pixels only.

Product specifications Name: GOES Aerosol and Smoke

Product (GASP) Satellites: GOES-East and GOES-

West Accuracy: 0.04 (GOES-E) ; 0.06

(GOES-W) Spatial resolution: 4 km at nadir Temporal resolution: 30 min. Latency: within one hour of data

capture Data format: binary file and JPEG

imagery Data availability: 2003 – present

PM2.5 Monitoring using GOES AOD Data

Figures courtesy of Philip Morefield, EPA

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

2008 2008

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Aerosol Retrievals from a Geostationary Platform (cont.)

• GOES vs AERONET AOD comparisons for 10 eastern US stations show retrievals have:

– 0.13 rmse

– Absolute bias less than 30% when AOD is > 0.1

• Results are similar to MODIS vs AERONET

• On average, 39% of days in a month of have good retrievals compared to 11% for MODIS and 5% for MISR.

Prados et al., JGR, 2007Paciorek et al., Envin. Sci.

and Tech., 2008

Percent Good Retrievals

GOES AOD Validation

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Seasonal Burned Area over Alaska from GOES-11 in 2009

Fuel Load (kgC/ha)

Biomass Burning Products

• Fire hot spots Detection and

instantaneous fire size from GOES

• Burned area Based on

algorithm that combines GOES and MODIS/AVHRR fire hot spots/sizes in a fourier model to compute burned area

• Fuel load Based on MODIS

land products

• Emissions Product of burned

area (ha), fuel load KgC/ha), emissions factors (g/KgC)

Product specifications Name: GBBEP Satellites: GOES-East and

GOES-West Accuracy:30% Spatial resolution: 4 km at

nadir Temporal resolution: houtly Latency: one day Data format: binary file and

JPEG imagery Data availability: 2002 –

present

5-yr mean over CONUS

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hkmc

Animation of Smoke Plume Detection

Original AOD Image Smoke AOD Image

• Semi-quantitative retrieval of column average smoke concentration (µg/m3) using AOD and fire hot spots from GOES

Uses source apportionment and pattern recognition techniques to isolate smoke aerosols from other type of aerosols

Smoke mass concentration (mc) is obtained using AOD (τ), mass extinction efficiency (k), and aerosol height (h)

• Product specifications Name: ASDTA Satellites: GOES-East and GOES-West

(includes Alaska and Hawaii) Accuracy: 40% Spatial resolution: 0.15o

Temporal resolution: hourly Latency: one day Data format: binary file, GRIB file, JPEG

imagery Data availability: 2007 - present

GOES Smoke Concentration Product (1)

NWS testing of smoke verification for Alaska

using NESDIS GOES-W smoke product

GOES Smoke Concentration Product (2)

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GOES-12:

GOES-13:

Geostationary Satellite Platform Challenges

• Navigation errors are a big problem for current GOES satellites as shown in adjacent figure for a fire event

– Spacecraft jitter despite 3-axes stabilization

• Recently launched GOES-13 (new spacecraft) has less jitter

Visible Shortwave IR

10UTC 14:45 07/20/2008

GOES-West

GOES-East

Viewing Geometry:

GOES-West (135oW) and GOES-E (75oW) observing the same location on the Earth can provide totally different AODs. In this example, GOES-W views in a forward scattering mode (scattering angles less than 90o) whereas GOES-E views in a backward scattering mode (scattering angles greater than 90o). Since sensitivity (change in top of the atmosphere reflectance to change in AOD) is greater for forward scattering mode, retrievals are more accurate for those conditions.

GOES-W AOD sector

does not have coverage

Optimum scattering angle retrieval range

Geostationary Satellite Platform Challenges (cont.)

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UTC:1415 UTC:2315

Bad retrievals due to viewing geometry effects etc. can be removed in post-processing of data. NESDIS developed a data quality flag based on scattering angle and spatial variability tests. Only data for viewing geometry corresponding to scattering angles greater than 70o and less than 150o are retained.

Before screening

After screening

Geostationary Satellite Platform Challenges (cont.)

What about GOES-R?

• Similar and some new products but with enhanced accuracy, spatial resolution, and temporal resolution.

• 2-km resolution allows picking up localized events;

• 5-min resolution allows peeking through clouds, now-casting;

• Night-time retrievals (except for dust) not possible due to reliance on visible channels

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GOES-R ABI in five minutes Current GOES in five minutes

24 April, 2004, Central America, 5 minute resolution

loop

Note black dotsthat are fire detections

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Aerosol Retrievals from Next Generation Geostationary Satellite

• GOES-R capabilities– 16 channel Advanced

Baseline Imager.– AOD at 2 X 2 km

resolution at nadir– 5-min CONUS, 30-sec

mesoscale, 15-min full disk modes or full disk in five minutes.

– AOD retrieval is a multi-channel MODIS/VIIRS type retrieval. Current GASP retrieval is a single channel retrieval.

TOA reflectance in blue band

TO

A r

efle

cta

nce

in r

ed

ba

nd

aerosol model 1aerosol model 2

Residual 1

Residual 2

Retrieved AOD550

Observation

Illustration of aerosol retrieval concept

Laszlo et al., Advances in Space Research, 2007

http://www.orbit.nesdis.noaa.gov/smcd/spb/pubu/validation_new/index.php

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±0.05

±0.02±0.04

±0.1

±0.12

0.01 0.1 1

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

LAND WATER

Re

trie

va

l A

cc

ura

cy

AERONET AOD0.4

Aerosol Retrievals from Next Generation Geostationary Satellite (cont.)

• GOES-R ABI products– AOD– Aerosol Particle Size– Smoke/dust mask– Fire hot spots and

emissions

AOD Accuracy

Dust Mask

Smoke Mask

AODABI Aerosol Detection

CALIPSO Matchups

Accuracy (%)

Smoke mask

22 80

Dust mask 26 81

Proving Ground Experiments with the National Weather Service (1)

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• NESDIS/STAR is developing a global biomass burning emissions product from a network of geostationary satellite sensors using GOES-R emissions algorithm.

• STAR scientists are working closely with NWS/NCEP scientists in validating and testing the product.

• Adjacent figure shows an example of seasonal mean Fire Radiative Power (FRP) for Fall 2009. FRP is used to compute trace gas and aerosol emissions that are used in the model to improve forecasts.

Funded by Joint Center for Satellite Data Assimilation (JCSDA) but the concept is similar to AQPG

Proving Ground Experiments with the National Weather Service (2)

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•

Version 3 Version 5

STAR scientists worked closely with the NWS scientists in testing the use of satellite-derived dust mask (MODIS used as a proxy for GOES-R Advanced Baseline Imager) in verifying

dust forecasts. In working with the NWS, we learned that the algorithm is flagging some of the thin dust plumes (localized) as clouds. Algorithm (V3) was changed (V5) to rectify the

identification of dust as cloud.

Proving Ground Experiments with the National Weather Service (3)

V3

Funded by GOES-R program office and National Weather Service Office of Science and Technology but the

concept is similar to AQPG

V3 V5

GOES AOD

ABI AOD

ABI Suspended Matter (µg/m3)

ABI AOD and Aerosol Model

Method 2Method 1

GOES-R ABI Suspended Matter

UMCP aircraftspirals

Preliminary Validaiton of GOES-R ABI Suspended Matter

Profile No. GOES

AODAircraft

AODABIAOD

ABIMass (ug/m3)

GOES Mass

(ug/m3)

Aircraft Mass

(ug/m3)

1 0.19±0.02 0.179 --- --- 11.8±1.4 17.5

2 0.33±0.01 0.08 --- --- 20.1±0.56 5.37

3 --- 0.086 --- --- --- 6.7

4 0.11±0.11 0.07 --- --- 6.5±6.5 2.16

5 0.18±0.01 0.167 0.13±0.01

Method 1: 8.32±0.01Method 2: 12.06±0.0

11.3±0.7 12.4

6 0.21±0.06 0.194 --- --- 13.0±3.9 4.7

7 0.36±0.01 0.178 0.33±0.01

Method 1: 20.0±0.83Method 2: 19.6±15.6

21.6±0.64 19.7

AOD Suspended Matter

ABI AOD Derived using MODIS radiances as proxy

Conclusions

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• Air quality products from GOES-R ABI have been in the development at NOAA/NESDIS for the past three years. Algorithms built based on experience with GOES and MODIS.

MODIS and model simulated data are being used as proxy data.

• Algorithm developers working closely with NWS.

• Algorithm developers would like to work closely, through the proving ground, with EPA, state/local air quality forecasters. User/focus group feedback very critical for improving product

quality, usage, distribution, and the development of new applications including specific data formats.