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IDEA for GOES-R ABI

Presented by S. Kondragunta, NESDIS/STAR

Team Members: R. Hoff and H. Zhang, UMBC

GOES-R Risk Reduction Review June 10 2010

Project Summary• Use operational MODIS, GOES Aerosol Optical Depth (AOD) products,

and OMI/GOME-2 Aerosol Index (AI) to provide near-real-time air quality monitoring and forecasting guidance. Research and development work done under this project will investigate the usefulness of satellite measurements in improving air quality forecast guidance and pave the way for using enhanced aerosol products from GOES-R ABI

– Operational GOES AOD, MODIS AOD, OMI/GOME-2 AI data– GOES-R ABI like retrievals obtained from MODIS radiances

• Tasks– Develop and evaluate new GOES AOD retrieval algorithm (MAIAC)– Adapt IDEA to GOES-R ABI retrievals, CONUS views and full disk views

• Expected Outcome– Improved IDEA product– Implementation of the new GOES AOD algorithm into IDEA– Demonstration of improved air quality predictions

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Infusing satellite Data into Environmental Applications (IDEA)

Infusing satellite Data into Environmental Applications (IDEA)

Developed by NASA Langley and U. Wisconsin in cooperation with EPA and NOAA (Al-Saadi et al., BAMS, 2006)To improve air quality assessment, management, and prediction by infusing satellite measurements into analyses for public benefitIDEA tool

Near real-time, integrates satellite and ground data, and generates forward trajectories for dissemination to users.MODIS Terra AOD was the main data sourceRan continuously at UW from 2004 to 2008

Developed by NASA Langley and U. Wisconsin in cooperation with EPA and NOAA (Al-Saadi et al., BAMS, 2006)To improve air quality assessment, management, and prediction by infusing satellite measurements into analyses for public benefitIDEA tool

Near real-time, integrates satellite and ground data, and generates forward trajectories for dissemination to users.MODIS Terra AOD was the main data sourceRan continuously at UW from 2004 to 2008

GOES-R Risk Reduction Review June 10 2010

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IDEA transition and enhancementIDEA transition and enhancement

Transition IDEA from Wisconsin to NESDIS from research to pre-operational systemUMBC was funded by GOES-R risk reduction program and NASA 3D-AQS project to enable this transition and add GOES components.Additional data sources incorporated into IDEA : GOES-E AOD, GOES-W AOD, MODIS Aqua AOD, OMI aerosol indexIntegrated IDEA with the original MODIS Terra product and made it switchable between different satellite productsMore components are addedIDEA or a system similar to it will use GOES-R ABI aerosol data once GOES-R is launched

Transition IDEA from Wisconsin to NESDIS from research to pre-operational systemUMBC was funded by GOES-R risk reduction program and NASA 3D-AQS project to enable this transition and add GOES components.Additional data sources incorporated into IDEA : GOES-E AOD, GOES-W AOD, MODIS Aqua AOD, OMI aerosol indexIntegrated IDEA with the original MODIS Terra product and made it switchable between different satellite productsMore components are addedIDEA or a system similar to it will use GOES-R ABI aerosol data once GOES-R is launched

GOES-R Risk Reduction Review June 10 2010

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IDEA webpage at NESDIShttp://www.star.nesdis.noaa.gov/smcd/spb/aq/

National and regional views of MODIS/GOES AODs and true color image (MODIS) or reflectance of visible band (GOES)

Aerosol trajectory initiated from MODIS/GOES AOD and OMI AI

3-day composite data animation

Time series, scatter plots, histogramsof MODIS/GOES AOD and PM2.5 for each hourly PM2.5 site

National correlation summary between MODIS/GASP AOD and surface measurements of PM2.5

PM2.5 estimates from AODGOES-R Risk Reduction Review

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New data source included: GOES AOD national & regional map animation

Can switch to view MODIS AOD, GASP AOD map, and GASP AOD animationCan select from the 10 EPA geographical regions to view regional map

EPA Region 1-3

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Incorporation of OMI AI into 48 hr forward trajectories from MODIS Aqua AOD

High OMI aerosol index usually indicates high level of aerosol layerIt can also detect aerosol above cloudOMI onboard Aura is eight minutes behind AquaIn trajectory initialization height depends on OMI AI value: OMI AI >2.0,

trajectory initialized at 3 km. Otherwise, trajectory initialized close to surface.

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Statistics of MODIS/GOES AOD and surface PM2.5

• Statistics plots for each hourly PM2.5 site

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Daily PM2.5 estimates from AOD

Calculate daily average AOD from MODIS Terra, Aqua, and GASP East. Ignore GASP AOD if MODIS AOD is available.

Compute PM2.5 from daily average AOD using linear regression relationship between AOD and PM2.5

The linear regression relationship varies seasonally and regionally ( four season and ten EPA geographic regions, Zhang et al., JAWMA 2009)

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Visualization of MODIS AOD Data in Google Earth

We set up MODIS AOD near-real-time image in KML format, which is available as a link on IDEA. The AOD images can be viewed along with the RGB images (from MODIS Today, http://ge.ssec.wisc.edu/modis-today/) in Google Earth. *Thanks Liam Gumley of CIMSS for the help on the set up

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Aerosol wind -- an experimental product• Wind fields derived from GOES AOD

imagery– A portion of the GOES smoke

concentration algorithm code that was developed by STAR provides observed wind fields (speed and direction) from GOES AOD imagery.

– This code was adapted for the IDEA website to display 3-hr movie loops of observed wind fields

– A survey of the users revealed that while they like these observed wind fields, they are of not great value for forecasting applications

GOES-R Risk Reduction Review June 10 2010

Product could become useful to forecasters in GOES-R era because of 5-minute refresh rate.

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IDEA user survey

These trajectories are one of the most important forecast tools that are available for PM2.5. Bear in mind, forecasters have very few tools for PM forecasting. Statistical models are not good and numerical models are experimental. We have to depend on persistence and transport for forecast guidance to a much larger degree than ozone forecasting. Having observations tied to forecast transport and presented in an elegant manner as on IDEA are a critical tool for us.

Bill RyanPenn State Forecaster for Philadelphia

Obtained feedback from a focus group comprising of 20 state and local air quality forecasters on IDEAUser feedback was very favorable to IDEA product. An excerpt from one user is shown in the adjacent panel

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Air quality monitoring during Beijing Olympics

We provided near real-time MODIS AOD images over northeastern China during Beijing Olympics for smog blog http://alg.umbc.edu/usaq

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Port MAIAC for GOES data

• Apply Lyapustin’s MAIAC (Multi-Angle Implementation of Atmospheric Correction) algorithm for MODIS to GOES AOD retrieval

• MAIAC algorithm derive surface BRDF and AOD from time sequences of MODIS images

• Assumptions:– AOD is homogeneous over 25x25 km2 area– Surface BRDF in blue channel and red channel is

proportional to 2.1 μm channel

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GASPMAIAC

Terra Aqua

Comparison with GASP, MODIS Terra, Aqua

MAIAC retrieval has more details than MODIS and larger area than GASP and MODIS

GOES-R Risk Reduction Review June 10 2010

Interactions With AWG/PG

• This project work on adapting MAIAC algorithm is done with close interaction with the AWG team.

• Products/tools developed under this project provide high visibility to AWG algorithms/products when this or a similar tool is adapted for Air Quality Proving Ground

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SummaryTransition of IDEA from UW to NOAA/NESDISIDEA enhancements

Incorporate AOD data from GOES East, GOES West, MODIS Aqua into components of IDEAIncorporate aerosol index (AI) data from OMI in trajectory forecastScatter plots, histograms of AOD and PM2.5 at each hourly PM2.5 stations over North AmericaPM2.5 estimates from AODMODIS true color imageryMODIS AOD in KML format, ready to be viewed in google Earth

Collaborations3D-AQS data product: MODIS, GASP AOD matchup datasetAir quality monitoring for Beijing OlympicSERVIR, air quality monitoring for Central America

Development of new AOD retrieval algorithm for GOESGOES image co-registrationValidation

GOES-R Risk Reduction Review June 10 2010

Future Work• Complete testing of MAIAC algorithm

implementation for GOES• Develop GOES-R ABI tutorials for

AMS/AGU conference presentations• Work hand-in-hand with air quality

proving ground to bring IDEA experience for the development of a visualization tool for ABI products

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