1. 1 1. FY09 GOES-R3 Project Proposal Title Page Title: Trace Gas and Aerosol Emissions from GOES-R ABI Project Type: GOES-R algorithm development project Status: New (moved from AWG to risk reduction) Duration: 2 years Leads: –Shobha Kondragunta (NESDIS/STAR) Other Participants: –Xiaoyang Zhang (IMSG) –Ivan Csiszar (NESDIS/STAR)

2. 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, MODIS, 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, MODIS, SEVIRI, and simulated proxy data –Intercompare results from the two algorithms to evaluate performance Expected Outcome –Delivery of algorithm package and ATBD

3. 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

4. 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)

5. 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

6. 6 7. Major Milestones FY08 –Modified and tested GBBEP algorithm to run on GOES-R simulated ABI proxy data provided by CIRA –Cleaned up the GBBEP software to adhere to AWG coding standard –Provided the code to Algorithm Implementation Team (AIT) for implementation on framework –Conducted internal consistency checks of the product –Developed a regression analysis for Biomass Combusted (Landsat TM data) vs FRE (GOES) over US to verify if this regression is similar to those reported in published literature

7. 7 7. FY08 Accomplishments Matched Fire Events between GOES-W and BLM Field Data GOES-W observed 45 fire events and only 9 of them are close to the location of field observations even though the buffer zone for match up is enlarged for several times. This shows the geo-location in GOES-W is poor in this region.

8. 8 7. FY08 Accomplishments (cont.) X-axis: PM2.5 emissions derived from CIRA RAMS model simulated fire sizes Y-axis: PM2.5 emissions derived from GOES-R ABI fire sizes. ABI fire sizes were obtained from forward model calculations using RAMS model inputs Published two papers on GBBEP algorithm: Zhang et al., Near real time biomass burning PM2.5 emissions across CONUS using multiple satellites, Atmospheric Environment, 2008 Zhang and Kondragunta, Temporal and spatial variability in biomass burning area across the USA using the GOES fire product, Remote Sensing of Environment, 2008 GBBEP algorithm became operational on July 16, 2008 Processed 7 years of GOES data to create emissions climatology Adapted and tested GBBEP algorithm using GOES-R proxy data. Adjacent plot shows comparisons of GOES-R ABI PM2.5 emissions compared to truth

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

10. 10 7. FY08 Accomplishments (cont.) FRP algorithm: –Emissions are a product of biomass combusted (KgC) times emissions factors (g/KgC) obtained from literature. Biomass combusted can be obtained by scaling FRE. FRE is computed by integrating FRP over time. FRP is computed using GOES derived fire size and temperature. Adjacent plot derived from Landsat TM biomass combusted and GOES FRE shows a slope similar to the one published in literature. Once this relationship is established, biomass combusted can be calculated for any given time and location as long as FRP is available from GOES

11. 11 7. Major Milestones FY09 GOES-R3 –Complete the development of FRP emissions algorithm –Complete testing the FRP emissions algorithm on GOES-R ABI proxy data (SEVIRI, simulated, MODIS) –Complete modification of GBBEP algorithm to use GOES-R ABI fuel moisture. Coordinate with vegetation team for this work –Complete comparisons of FRP and GBBEP emissions products using data processed for 2002 GOES data. –Complete evaluation of GBBEP and FRP emissions data to EPA truth data –Develop product validation plan FY10 GOES-R3 –Complete algorithm refinements –Complete ATBD and other documents –Cleanup the software for implementation on AIT framework

12. 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 Funding Sources Procurement Office Purchase Items FY08FY09FY10 GOES-R3 095135* GOES-R3 STAR IMSG Contract 095135* 000 Other Sources 000 000 000 000 * 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

13. 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 –(125K): STAR contractor at full time from Apr 10 to Mar 11 125K for IMSG contract