1' FY10 GOESR3 Project Proposal Title Page

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1. FY10 GOES-R3 Project Proposal Title Page

• Title An IDEA product for GOES-R data
• Project Type GOES-R data utilization project
• Status Renewal
• Duration 2 years
• Leads
• Shobha Kondragunta (NESDIS/STAR)
• Hai Zhang (UMBC)
• Other Participants
• Raymond M. Hoff (UMBC)
• James Szykman (EPA)

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2. 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 forecasts
  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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3. Motivation/Justification

• Supports NOAA Mission Goal(s)
• Weather and water
• GOES-R ABI aerosol products will support
  Memorandum of Understanding (MOU) and Memorandum
  of Agreement (MOA) between EPA and NOAA
• Current GOES aerosol products have limitations.
  Only AOD retrieval from a single channel is
  possible. Retrieval has uncertainties associated
  with surface reflectance retrieval and other
  assumptions. GOES-R ABI aerosol products are
  expected to be of better quality than current
  GOES.
• Although there are more than six hundred surface
  PM2.5 (particles smaller than 2.5 microns in
  diameter) stations over North America, there are
  large areas without measurements between stations
  and there are no measurements over the ocean.
  Satellite derived column AOD measurements
  correlate with surface PM2.5 and can be used to
  fill in the gaps and provide contiguous
  estimation of PM2.5.

Correlation between GOES-12 AOD and Surface PM2.5
for a mid-western site
GOES AOD
PM2.5 (µg/m3)
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4. Methodology

• Prepare IDEA for GOES-R ABI
• Setup IDEA website to add a panel for GOES-R ABI
  near real time AOD
• Download GOES-R AOD ABI retrievals generated at
  STAR in near real time and run 48-hr forward
  trajectories
• Continue work on testing the applicability of new
  GOES AOD retrieval algorithm (MAIAC)
• Image registration, this is to reduce the shift
  found in GOES images due to the jitter of the
  satellite so that the pixels with same
  geolocations from different images are co-located
  within one pixel error
• Project MODIS 2.12 um channel BRDF on GOES grid.
• Assume GOES channel 1 BRDF is proportional to
  MODIS 2.12 ?m channel, retrieve AOD using MAIAC
  algorithm
• Evaluate the AOD and surface reflectance
  retrievals by comparing to the results from
  AERONET, MODIS, GASP, etc
• Coordinate with Air Quality Proving Ground (AQPG)
• Adapt/modify IDEA to become an AQPG testbed

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6. Expected Outcomes

• Improved IDEA product
• Air quality application tool for state and local
  forecasters
• Improved GOES AOD product
• Implementation of MAIAC algorithm
• GOES-R ABI readiness
• Demonstration of improved air quality monitoring
  using IDEA
• Development of tailored GOES-R ABI AOD product

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7. Major Milestones

• FY08 milestones that were ongoing in FY09
• Web redirecting algorithm design and
  implementation inactive
• GOES-R ABI AOD proxy data inclusion, algorithm
  design and implementation - ongoing
• Comparison of ABI proxy data with GASP and MODIS
  and their relation to PM2.5 - ongoing
• Rewrite part of IDEA system in C - inactive
• Documentation completed
• FY09
• GOES-R3
• Complete the development of nowcasting component
  of IDEA product
• Completed. Tested the implementation of wind
  fields derived from GOES AOD imagery. 3-hr movie
  loops of derived wind speed and direction are
  displayed for forecasters.
• Complete the development of Air Quality index map
  for IDEA product - completed
• Complete the refinement of IDEA website panels to
  make it more user friendly - completed
• Complete the adaptation of MAIAC algorithm to
  GOES - completed
• Complete the survey of users for feedback on IDEA
  tool and website - completed

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7. FY09 Accomplishments

• MAIAC algorithm was modified to work for GOES AOD
  retrieval with the aid of the MODIS 2.1 um BRDF.
  
• The algorithm was tested over several AERONET
  site across continental US
• Compared to GASP, MAIAC algorithm has more
  accurate retrieval over several sites during
  spring when GASP underestimates surface
  reflectance. During summer and fall, the two
  algorithms have similar accuracy.

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7. FY09 Accomplishments (cont.)

• 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
• Added a satellite-derived PM2.5 panel to IDEA
  website

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7. FY08 Accomplishments (cont.)

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

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 Ryan Penn State Forecaster for
Philadelphia
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7. Major Milestones

• FY10
• GOES-R3
• Compare GASP and MAIC AODs to AERONET to
  determine which product performs better over arid
  regions
• Enhance IDEA by porting GOES-R near real time AOD
  retrievals that are generated by NESDIS to begin
  setting up for GOES-R launch
• Coordinate with air quality proving ground
• Continue maintenance of IDEA website and help
  with transition to OSDPD if approval for
  transition is obtained
• FY11
• GOES-R3
• Continue IDEA tool development in tandem with
  GOES-R ABI AOD algorithm development efforts
• Test GOES-R ABI retrievals generated from
  15-minute simulated proxy data in IDEA framework
• Interpolate 15-minute AOD retrievals to generate
  5-minute ABI like retrievals and test them in
  IDEA framework

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8. Funding Profile (K)

• Summary of leveraged funding
• STAR base funding for Shobha Kondragunta
• Coordination with GOES-R algorithm development
  work and air quality proving ground efforts

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9. Expected Purchase Items

• FY09 110,000 Total Project Budget
• (110K) UMBC scientist at full time from Sep 09
  to Aug 10
• 110K for UMBC Grant
• FY10 120,000 Total Project Budget
• (120K) UMBC scientist at full time from Sep 10
  to Aug 11
• 110K for UMBC Grant
• FY11 120,000 Total Project Budget
• (120K) UMBC scientist at full time from Sep 11
  to Aug 12
• 115K for UMBC Grant

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