1. FY09 GOES-R3 Project Proposal Title Page

1. 1. FY09 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) 1

2. 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 nowcasts. 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 a new component in IDEA product Develop and evaluate new GOES AOD retrieval algorithm (MAIAC) Expected Outcome Improved IDEA product Implementation of the new GOES AOD algorithm into IDEA Demonstration of improved air quality predictions 2

3. 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) 3

4. 4. Methodology New IDEA component Develop aerosol winds for nowcasting applications by replacing current IDEA trajectory forecasts with winds derived from aerosol imagery 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. 4

5. 6. Expected Outcomes Improved IDEA product Air quality application/nowcasting tool for state and local forecasters Satellite-derived air quality index map Refine PM2.5/AOD relation by EPA region Improved GOES AOD product Implementation of MAIAC algorithm Demonstration of improved air quality predictions using IDEA 5

6. 7. Major Milestones FY08 Complete code modification of the MAIAC algorithm to conduct different retrieval experiments from GOES- completed GOES AOD and surface reflectance retrieval test and evaluation Archiving system setup - completed Web redirecting algorithm design and implementation - ongoing 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++ - ongoing Documentation – ongoing 6

7. 7. FY08 Accomplishments Improved IDEA by Including additional satellite aerosol data in the IDEA system MODIS Aqua AOD, an individual tab with all the five products OMI AI, incorporated in the Aqua trajectory forecast so that trajectories are initiated at level where OMI AI are high GOES west AOD Comparison of OMI and GOME-2 Aerosol Index (AI) product Developed GOES image registration method to prepare for the new AOD retrieval algorithm Major findings On average, GOME-2 AI is larger than OMI AI over most regions for absorbing aerosols, and is smaller for scattering aerosols The GOES channel 1 images shift due to the satellite jitter. We use image registration to reduce this shift to less than 1 pixel. 7

8. 7. FY08 Accomplishments (cont.) IDEA with new satellite aerosol data added 8

9. 7. FY08 Accomplishments (cont.) A portion from two GOES ch1 Images before registration Substraction of the two images before and after registration Coast line GOES image registration 9

10. 7. Major Milestones FY09 GOES-R3 Complete the development of nowcasting component of IDEA product Complete the development of Air Quality index map for IDEA product Complete the refinement of IDEA website panels to make it more user friendly Complete the adaptation of MAIAC algorithm to GOES Complete the survey of users for feedback on IDEA tool and website 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 10

11. 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 11

12. 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 $110,000 Total Project Budget (110K): UMBC scientist at full time from Sep 10 to Aug 11 110K for UMBC Grant 12