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1. FY10 GOES-R3 Project Proposal Title Page

1. FY10 GOES-R3 Project Proposal Title Page. Title : Trace Gas and Aerosol Emissions from GOES-R ABI Project Type : GOES-R algorithm development project Status : Renewal Duration : 2 years Leads: Shobha Kondragunta (NESDIS/STAR) Other Participants : Xiaoyang Zhang (IMSG)

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1. FY10 GOES-R3 Project Proposal Title Page

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  1. 1. FY10 GOES-R3 Project Proposal Title Page Title: Trace Gas and Aerosol Emissions from GOES-R ABI Project Type: GOES-R algorithm development project Status: Renewal Duration: 2 years Leads: Shobha Kondragunta (NESDIS/STAR) Other Participants: Xiaoyang Zhang (IMSG) Ivan Csiszar (NESDIS/STAR) 1

  2. 2. Project Summary Project goal is to adapt current operational GOES Biomass Burning Emissions Product (GBBEP) to GOES-R ABI and to also develop and test alternate algorithms Operational GOES fire products and MODIS land products GOES-R ABI like fire products derived from MODIS radiances and simulated proxy data SEVIRI fire products and radiance data Tasks Run GBBEP algorithm on GOES, SEVIRI, simulated proxy data Evaluate GOES-R ABI aerosol and trace gas emissions data Develop alternate algorithm that uses fire radiative power Apply FRP emissions algorithm on GOES, SEVIRI, and simulated proxy data Intercompare results from the two algorithms to evaluate performance Expected Outcome Delivery of algorithm package and ATBD 2

  3. 3. Motivation/Justification • To provide air quality community with temporally resolved biomass burning emissions of trace gases and aerosols in near real time • GOES-R ABI will provide better fire characterization (fire size and temperature) that can be used in GBBEP and FRP emission algorithms to derive trace gas and aerosol emissions • Current GOES fire products have gaps in diurnal coverage and fire size is not accurate, especially for small fires • FRP algorithm is expected to improve the algorithm because GBBEP algorithm depends on multiple input datasets that have different levels of uncertainties and lead to uncertainties in emissions 3

  4. 4. Methodology • Algorithm development • GBBEP algorithm • First, we will model emissions using parameters including fire sizes, fuel loadings, fuel moisture, and fractions of combustions and emissions factors. Among these parameters, we will focus on the detections of instantaneous fire sizes in subpixels using GOES-R infrared bands with a temporal resolution as high as 5 minutes • Algorithm testing using GOES-R ABI proxy data (simulated data) • Software will adhere to GOES-R AWG coding standards • Adapt GBBEP algorithm to use GOES-R ABI like fuel moisture retrievals instead of AVHRR fuel moisture product • FRP algorithm • Secondly, we will develop and test FRP algorithm for direct estimates of emissions using FRP and scaling factor to derive biomass combusted and then multiplying biomass combusted with emissions factors. • Analysis of LANDSAT and SEVIRI data to determine correlation between biomass combusted and Fire Radiative Energy (FRE) to derive scaling factor needed by the algorithm • Algorithm testing using GOES-R ABI proxy data (simulated data) • Algorithm testing using SEVIRI data • Software will adhere to GOES-R AWG coding standards • Evaluation of GBBEP and FRP algorithms • Emissions product from GBBEP and FRP will be compared to other data sources to determine which algorithm will meet the specifications • Recommend one algorithm to AWG • Prepare Algorithm Theoretical Basis Document (ATBD) 4

  5. 6. Expected Outcomes GOES-R ABI trace gas and aerosol emissions algorithm, algorithm theoretical basis document, algorithm test plan, algorithm validation plan, and all other relevant documents on proxy data Demonstration of improvements to emissions product due to better fire characterization 5

  6. 7. Major Milestones FY09 Complete the development of FRP emissions algorithm Complete testing the FRP emissions algorithm on GOES-R ABI proxy data (SEVIRI) Complete comparisons of FRP and GBBEP emissions products using data processed for 2005 GOES data. 6

  7. 7. FY09 Accomplishments Aerosol (PM2.5) Emissions for August 2006 • GOES-R Fire Radiative Power (FRP) Emissions algorithm was applied to one month (August 2006) of EUMETSAT SEVIRI data to generate aerosol and trace gas emissions over Africa. These emissions were used in WRF-CHEM aerosol simulations to generate GOES-R Advanced Baseline Imager (ABI) proxy data 7

  8. 7. FY09 Accomplishments (cont.) • CMAQ point sources are not discernable in AIRS CO map (e.g., mid-West). Spatial patterns are somewhat similar • CMAQ with fire emissions captures enhanced CO in Florida. However enhanced CO in AIRS more widespread 8

  9. 7. FY09 Accomplishments (cont.) • Fire Radiative Energy FRP = A σ T4 σ -- the Stephan-Boltzman Constant (5.67x10-8 Js-1K-4) A – area burned T – fire temperature The  value is 0.368±0.015 kg/MJ (Wooster et al., 2005) BC = β * FRE FRE–fire radiative energy BC – biomass combusted (kgC) β–biomass combustion rate 9

  10. 7. Accomplishments (cont.): Example Emissions Products

  11. 7. Major Milestones FY10 GOES-R3 Complete testing the FRP emissions algorithm on GOES-R ABI proxy data (SEVIRI, simulated) – FY09 activity still ongoing Complete modification of GBBEP algorithm to use GOES-R ABI fuel moisture. Coordinate with vegetation team for this work – FY09 activity. Will not begin until land team is ready with their product Complete evaluation of GBBEP and FRP emissions data to EPA truth data – FY09 activity ongoing Develop product validation plan – FY09 activity ongoing Complete algorithm refinements Complete ATBD and other documents Cleanup the software for implementation on AIT framework FY11 GOES-R3 Continue algorithm development and refinement Continue product validation work Develop product tailoring techniques 11

  12. 8. Funding Profile (K) Summary of leveraged funding STAR base funds for Shobha Kondragunta AWG and GOES-R3 support to GOES-R fire product development. Emissions algorithm depends on fire products * Increase is to support contractor fulltime. In FY09 contractor will spend 15% of his time on a vegetation project with NOAA CREST colleagues and will be supported by other funding source 12

  13. 9. Expected Purchase Items FY09 $95,000 Total Project Budget (120K): STAR contractor at 85% time from Apr 09 to Mar 10 120K for IMSG contract FY10 $135,000 Total Project Budget (135K): STAR contractor at full time from Apr 10 to Mar 11 135K for IMSG contract FY11 $135,000 Total Project Budget (135K): STAR contractor at full time from Apr 10 to Mar 11 135K for IMSG contract 13

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