1. FY10 GOES-R3 Project Proposal Title Page

1. 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) 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 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 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 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 4

5. 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 5

6. 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 6

7. 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. 7

8. 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 8

9. 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 9

10. 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 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 $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 12