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M. Green (UMBC), R. M. Hoff (UMBC GEST/JCET), S. A. Christopher (UAH), F. Moshary (CCNY), S. Kondragunta (STAR), R. B. Pierce (NESDIS/CIMSS), Amy Huff (Battelle Mem. Inst.). An Air Quality Proving Ground (AQPG) for GOES-R.

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An air quality proving ground aqpg for goes r

M. Green (UMBC), R. M. Hoff (UMBC GEST/JCET), S. A. Christopher (UAH), F. Moshary (CCNY),

S. Kondragunta (STAR), R. B. Pierce (NESDIS/CIMSS), Amy Huff (Battelle Mem. Inst.)

An Air Quality Proving Ground (AQPG) for GOES-R

Abstract: A consortium of Universities have been awarded an Air Quality Proving Ground for the GOES-R ABI instrument. Led by UMBC and University of Alabama- Huntsville, the Proving Ground will provide the first steps to building a user community who will be prepared to use the ABI data in near-real time for air quality forecasting and analysis needs. Based on the currently successful, IDEA product, the AQPG will evolve a product delivery system so that regional air quality forecasters have access to measurements from GOES-R, from ground based sites, and from models to better predict particulate air quality in the US. The Year 1 activities of the Proving Ground will be to gather user driven ("pull") guidance on the understanding of the ABI product and how it would be used in such a forecast system. To that end, an AQPG User Group will be formed that will advise the project in the future. Evolving from the current Three-Dimensional Air Quality System User Group and adding members from the NWS Forecast Guidance User community, these advisers will assess ABI proxy data which has been and will be processed in the future. Using data sets from existing satellites, ground based remote sensing, ground based air quality measurements, and models, at least ten case studies will be created which exercise the ABI algorithm and allow the User Group to comment on how those data would be used in their forecast tasks.

Past and Present:

NOAA maintains and supports the Infusing Satellite Data into Environmental Applications (IDEA) product at Designed to disseminate NOAA GOES Aerosol and Smoke Product (GASP) data, NASA MODIS data, and EPA ground-based PM2.5 measurements, IDEA has become a core product in the toolkits used by State and local air quality analysts and forecasters. As part of a NASA funded effort (the Three-Dimensional Air Quality System (3D-AQS, Zhang et al., 2009; Hoff et al., 2009), a user community has been formed who provide us with advice and guidance on how these satellite-derived products can be better utilized in an operational air quality forecasting environment.

Figure 1: The current IDEA product. (Real-time demo)

The current user group has 19 members who have been trained in the utility of the currently available aerosol and trace gas products from NASA and NOAA. Concerns they have identified as “very important” are free access to the data, availability by 3:00 pm local time in their time zone, and “one stop shopping” (I.e. combined sensor data, surface and modeling data).

ABI outputs will be integrated into an IDEA-like platform which will be the AQPG testbed for dissemination of the AOD data from GOES-R to the public. Options for this platform include NOAA web services or AWIPS-II.

Evaluation by the user group of the utility of the delivered product will help define/refine the suite of products which need to be included on the GOES-R ABI data platform to external users. A workshop will be held in August 2010 to determine whether additional development products under the AQPG need to be created. Issues which are expected to come up at this meeting include the integration of non-aerosol products (trace gases, meteorology, NOAA numerical guidance products, etc.) into this data platform.


Zhang, H., Hoff, R.M., Engel-Cox, J.A. 2009: The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by EPA regions, J.Air & Waste Manage. Assoc., Accepted.

Hoff, R. H. Zhang, N. Jordan, A. Prados, J. Engel-Cox, A. Huff, S. Weber, E. Zell, S. Kondragunta, J. Szykman, B. Johns, F. Dimmick, A. Wimmers, J. Al-Saadi, and C. Kittaka, 2009. Applications of the Three-Dimensional Air Quality System (3D-AQS) to Western U.S. Air Quality: IDEA, Smog Blog, Smog Stories, AirQuest, and the Remote Sensing Information Gateway. JAWMA,, 59, 980-989.


Part of this work was funded by NOAA contract DG133E07CN0285 (IDEA project support) and NA06OAR4810162 (NOAA CREST Cooperative Agreement). The views, opinions, and findings contained in this report are those of the author(s) and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision.

Designing an Air Quality Proving Ground:

With IDEA as a conceptual model of something which works for regional forecasters, we have started activities to create test data sets from observational data along the US East Coast which can be used to construct up to ten cases which exercise the abilities of GOES-R ABI. The process is as follows:

A presentation/workshop at the 2010 EPA National Air Quality Conference to familiarize the existing 3D-AQS Focus Group with GOES-R’s capabilities. We expect to invite the NWS Forecasting group to participate in the AQPG external guidance.

2. AQPG team members will identify cases of interest (smog, dust, smoke, etc.) for which ground-based and satellite data are available and have been previously studied in detail. These become “truth” cases for exercising the ABI aerosol algorithms. Cases with lidar data available (New York (CCNY), Baltimore/Washington (UMBC), the Southeast (UAH), and Wisconsin (CIMSS)) will be prioritized.

3. Model predictions of the motion of the aerosol within 1-2 days of the “truth” days will be examined using air chemistry transport models such as WRF-Chem (CIMMS), CMAQ (UAH), and RAQMS (CIMSS). Modeled spatial and horizontal distribution of aerosol will form the basis of the radiance inputs to the ABI processing algorithm.

4. The ABI aerosol algorithm will be run by NESDIS STAR to create proxy data which will demonstrate the ABI capability.

ABI proxy data will be “validated” against lidar and ground data and provide quality checks on the precision of the proxy inputs as well as the ABI retrievals.