1. Research & Development Building a science foundation for sound environmental decisions EPA Workshop on Satellite and Above-Boundary Layer Observations for Air Quality Management: A Focus on Exceptional Events Analysis UMBC, January 11, 2012 Jim Szykman US EPA Office of Research and Development, NERL Todd Plessel and Matt Freeman Lockheed Martin, USEPA EMVL Heidi Paulsen USEPA NCC-OEI, Office of Technology Operations and Planning Overview of the US EPA Remote Sensing Information Gateway (RSIG)

2. 2 Outline Why RSIG was created Why RSIG was created Goals and ObjectivesGoals and Objectives RSIG Architecture, Data Sets, User Interface, and Features RSIG Architecture, Data Sets, User Interface, and Features Potential uses of RSIG relevant to EE Potential uses of RSIG relevant to EE Summary and Discussion Summary and Discussion

3. 3 Why RSIG was created Needed a common framework for the analysis of related datasets, and Needed to find a way to reduce the barriers preventing routine use of satellite and modeled data sets Extend access and increase use of relevant Earth science model and observations data into relevant EPA applied research Create a system to demonstrate useful research results that can be transitioned into an operational setting, supporting decision-making activities. (Scheffe et al., 2009) Research& Development Building a science foundation for sound environmental decisions

4. RSIG Goal and Objectives Provide a Common Operational picture for relevant air quality related data.Provide a Common Operational picture for relevant air quality related data. Provide access to the 3-dimensional data to create a 3-D Air Quality System – part of EPA’s commitment under 3D-AQS.Provide access to the 3-dimensional data to create a 3-D Air Quality System – part of EPA’s commitment under 3D-AQS. Provide users the ability to integrate various data sets across different time and space scales.Provide users the ability to integrate various data sets across different time and space scales. Support EPA Researchers and Analysts needs with Infrastructure for Data Gathering, Sharing, Visualization, and Analysis for Air Quality Research and Management that can be extended into the larger Air Quality Community

5. 5 Remote Sensing Information Gateway (RSIG) now provides the functions envisioned by 3D-AQS. NASA Applied Science 3-D AQS Results transition into development RSIG 3D Air Quality System (3D-AQS): NASA Applied Science Project focused on increasing access and use of satellite and lidar data within the Air Quality Community. Satellite LIDAR PI – Ray Hoff, UMBC

8. Key Features Designed into RSIG Accessible from computers outside the EPA network: (http://badger.epa.gov/rsig). Accessible from computers outside the EPA network: (http://badger.epa.gov/rsig). Subsets files at the source, allowing users to access most current data version. Subsets files at the source, allowing users to access most current data version. Aggregates data files in time and space within visualization and save functions. Aggregates data files in time and space within visualization and save functions. Allows for on-the-fly re-gridding of satellite data onto standard CMAQ model grid or user specified grid parameters. Allows for on-the-fly re-gridding of satellite data onto standard CMAQ model grid or user specified grid parameters. Provides many useful “Save As” formats for the data and images, such as XDR binary, ASCII, HDF, MPEG, NetCDF, and KMZ. Provides many useful “Save As” formats for the data and images, such as XDR binary, ASCII, HDF, MPEG, NetCDF, and KMZ. Interoperable with other OGC-compliant systems. Interoperable with other OGC-compliant systems.

9. Remote Sensing Information Gateway (RSIG) Main Access: http://www.epa.gov/rsig Main Interface is via a JAVA applet which provides interactive features via the users web browser. The RSIG applet requires: IE 7, Firefox 3, Safari 4 (or any earlier versions), Java runtime environment (JRE) 1.5, or greater, configured with the following memory settings: -Xms64m - Xmx256m, and java.policy file saved to users Home directory After the initial configuration the user can launch the RSIG applet at: http://badger.epa.gov/rsig/rsig2D.html The RSIG applet can be launched from the link on the home webpage. (or)

10. Research& Development Building a science foundation for sound environmental decisions Remote Sensing Information Gateway JAVA Web Interface

11. Research& Development Building a science foundation for sound environmental decisions Remote Sensing Information Gateway JAVA Web Interface Data Source Selection RSIG allows up to 5 concurrent data sources and variables to be selected Variable Source Selection Additional RSIG data services on data display and screening criteria

12. Research& Development Building a science foundation for sound environmental decisions Remote Sensing Information Gateway JAVA Web Interface Data Source and Variable Selection Pull-Down Menu reveals Data Sources and Associated Variables for Selection RSIG webpage on Data Inventory Data Source Menu also contains some RSIG functions, such as difference between two data sets, ratios, and CMAQ intersections.

13. Research& Development Building a science foundation for sound environmental decisions Remote Sensing Information Gateway JAVA Web Interface

15. Remote Sensing Information Gateway JAVA Web Interface Data and Image Save Options Save function in RSIG allows the user to save the data and/or visualization in various formats. RSIG will save CMAQ or regridded data to NetCDF -IOAPI. User has the option to: Save all layers of CMAQ output Save interpolated corner points for MODIS pixels Save data as one file/day

16. Research& Development Building a science foundation for sound environmental decisions Subsetting Retrieve only data of interest Time: extract & transfer only data within hourly range (over many days) Time: extract & transfer only data within hourly range (over many days) Variable: extract & transfer only variables of interest Variable: extract & transfer only variables of interest Domain: extract & transfer only data within a chosen lon-lat box Domain: extract & transfer only data within a chosen lon-lat box Direct download of daily L2 MODIS AOD granule file and L1 CALIOP TAB over CONUS = ~0.50 GB The subsetted file over the region of interest is only 400 KB—1/8000 th the source data size. Convenient, fast, and efficient to transmit across the network.

17. Research& Development Building a science foundation for sound environmental decisions Regridding 2D Regridding of any available surface data points onto 2D Regridding of any available surface data points onto standard regional and CONUS grid (12km and 36km), new hemispheric grid (108km), or user specified CMAQ grid (layer 1). (Methods : weighted (1/r2), mean, nearest) Regridding of Satellite Data Controlled by User!

18. Difference Feature Available via pull down menu for Data Sources and Variables. Allows User to select simple, absolute or percent difference and also ratio. Limited to CMAQ vs. AOD, surface O 3 or PM 2.5. Research& Development Building a science foundation for sound environmental decisions

19. Internal Applications Model- Measurement Comparison Big Sur Fire Complex Aerosol Optical Depth Comparisons Maximum Values: June 23-30, 2008 CMAQ Model MODIS Source R. Mathur

20. Adjacent images shows MODIS aerosol optical depth, CALIPSO 1064nm total attenuated backscatter and continuous PM 2.5 concentration.Adjacent images shows MODIS aerosol optical depth, CALIPSO 1064nm total attenuated backscatter and continuous PM 2.5 concentration. The time series captures the transport of a large dust storm from the Saharan as is comes off the coast.The time series captures the transport of a large dust storm from the Saharan as is comes off the coast. The combination of MODIS AOD and CALIPSO present a 3-D picture as the dust storm is transported into the Gulf of Mexico.The combination of MODIS AOD and CALIPSO present a 3-D picture as the dust storm is transported into the Gulf of Mexico. Use of MODIS AOD and CALIPSO TAB can help evaluate Ground Measurements Influenced by Exceptional Events- 2006 Saharan Dust Storm

21. Use of MODIS AOD and CALIPSO TAB can help evaluate Ground Measurements Influenced by Exceptional Events - 2007 Georgia Fires Savannah, Georgia USA Hourly PM 2.5 concentration 24-Hour average PM2.5 concentration 1-hour average PM2.5 concentration MODIS AOD * Research& Development Building a science foundation for sound environmental decisions

22. RSIG and IDEA can be used as a rapid screening tool identify Exceptional Events - 2011 FL/GA Fires Tallahassee, FL Research& Development Building a science foundation for sound environmental decisions

23. Potential Updates in 2012 Matched (temporal and spatial) coincident satellite pixels (MODIS, GASP, CALIPSO) and ground based measures (AQS and AIRNow) as a standard data output. Matched (temporal and spatial) coincident satellite pixels (MODIS, GASP, CALIPSO) and ground based measures (AQS and AIRNow) as a standard data output. Provide on-line time series plots of satellite and ground based data with the ability to select and display percentiles (50, 75, 95, etc.) of a monitored value at a ground monitoring site over a selected time frame. Provide on-line time series plots of satellite and ground based data with the ability to select and display percentiles (50, 75, 95, etc.) of a monitored value at a ground monitoring site over a selected time frame. Incorporation of PM2.5 speciation data (CSN and IMPROVE) available in AQSData Mart into the RSIG. Incorporation of PM2.5 speciation data (CSN and IMPROVE) available in AQSData Mart into the RSIG. Visualize the existing GOES-biomass burning emissions to show the location of fire anomaly detected by GOES in lieu of actual emission estimates. Visualize the existing GOES-biomass burning emissions to show the location of fire anomaly detected by GOES in lieu of actual emission estimates. Additional of CALIPSO Vertical Feature Mask and Aerosol Subtype Additional of CALIPSO Vertical Feature Mask and Aerosol Subtype Addition of MOPITT CO and TES trace gas data products. Addition of MOPITT CO and TES trace gas data products.

24. Research & Development Building a science foundation for sound environmental decisions USEPA, Office of Research and Development, National Exposure Research Lab – David Mobley, Rohit Mathur, and Tom Pierce NASA Goddard Space Flight Center - Cid Praderas (Sigma Space), Ed Masuoka, and Lorraine Remer NASA Langley Research Center- Danny Mangosing (SSAI), Pamela Rinsland, and Chip Trepte Acknowledgment

25. Research & Development Building a science foundation for sound environmental decisions Disclaimer Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.