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

1
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.)
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.

• 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.
• References
• 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.
• Acknowledgment
• 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.

Past and Present NOAA maintains and supports the
Infusing Satellite Data into Environmental
Applications (IDEA) product at http//www.star.nes
dis.noaa.gov/smcd/spb/aq/. 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 300 pm local
time in their time zone, and one stop shopping
(I.e. combined sensor data, surface and modeling
data).

• 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-Rs
  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.