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Overview of the US EPA Remote Sensing Information Gateway (RSIG)

Overview of the US EPA Remote Sensing Information Gateway (RSIG). 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

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Overview of the US EPA Remote Sensing Information Gateway (RSIG)

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  1. Overview of the US EPA Remote Sensing Information Gateway (RSIG) 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

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

  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&DevelopmentBuilding a science foundation for sound environmental decisions

  4. RSIG Goal and Objectives 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 • 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 users the ability to integrate various data sets across different time and space scales.

  5. NASA Applied Science 3-D AQS Results transition into development RSIG Remote Sensing Information Gateway (RSIG)now provides the functions envisioned by 3D-AQS. 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 PI – Ray Hoff, UMBC LIDAR

  6. Key FeaturesDesigned into 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. • 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. • 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.

  7. 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 The RSIG applet can be launched from the link on the home webpage. (or) After the initial configuration the user can launch the RSIG applet at: http://badger.epa.gov/rsig/rsig2D.html

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

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

  10. 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. Research&DevelopmentBuilding a science foundation for sound environmental decisions

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

  12. 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

  13. SubsettingRetrieve only data of interest • Time: extract & transfer only data within hourly range (over many days) • Variable: extract & transfer only variables of interest • 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—1/8000th the source data size. Convenient, fast, and efficient to transmit across the network. Research&Development Building a science foundation for sound environmental decisions

  14. Regridding • 2D Regriddingof 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! Research&Development Building a science foundation for sound environmental decisions

  15. 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 O3 or PM2.5. Research&DevelopmentBuilding a science foundation for sound environmental decisions

  16. Internal ApplicationsModel- Measurement ComparisonBig Sur Fire Complex Aerosol Optical Depth ComparisonsMaximum Values: June 23-30, 2008 MODIS CMAQ Model Source R. Mathur

  17. Use of MODIS AOD and CALIPSO TAB can help evaluate Ground Measurements Influenced by Exceptional Events- 2006 Saharan Dust Storm • Adjacent images shows MODIS aerosol optical depth, CALIPSO 1064nm total attenuated backscatter and continuous PM2.5 concentration. • 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.

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

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

  20. 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. • 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. • 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 • Addition of MOPITT CO and TES trace gas data products.

  21. Acknowledgment • 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

  22. Disclaimer Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

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