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WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)
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WE1.L10 - IMPLEMENTATION OF THE LAND, ATMOSPHERE NEAR-REAL-TIME CAPABILITY FOR EOS (LANCE)

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  • Examples of side-by-side comparisons of the standard and near-real time. The first shows a Land Surface Reflectance granule over the Midwest. There appears to be no difference between the products. However, under close examination the near-real time view shows slightly more haze West of the Great Lakes. In contrast, the second side-by-side comparison is for Cloud Top Temperature for the same granule and there are very obvious differences in the region West of the Great Lakes. This is as a result of the sensitivity of this product to the GDAS ancillary data.
  • AIRS false-color 3-km visible image (left), and the SO2 brightness temperature difference at 10-km resolution (right) BT_diff_SO2 values under-6K have likely volcanic SO2. Information from multiple sensors indicates that this eruption is mostly ash with little SO2 being expelled into the atmosphere.
  • This AMSR-E Brightness Temperature image with a GOES background shows Hurricane Alex entering Mexico, just as it is degraded to a tropical storm. Courtesy of US Naval Research Laboratory
  • OMI image showing the ash cloud from the Icelandic volcano as flights are cancelled all across Europe, impacting tens of thousands of customers. Shown with MODIS “Blue Marble” Background
  • MODIS Terra image taken on a relatively cloud free day just off the coast of New Orleans capturing the oil slick as it is being transported to the south via a loop current. The thicker oil slick appears brighter than surrounding water.
  • Do we have a more recent example from LANCE The maps they generate are available to governments and relief organizations. For flood detection, the Flood Observatory has defined at least 710 sections of rivers worldwide. Each section is 20 to 30 kilometers in length and 20 to 30 kilometers in width. MODIS monitors the sections, called reaches, to determine the status of each. When water levels rise to flood status, the river can be targeted by higher-resolution sensors.
  • The Canadian Ice Service uses MODIS images to chart out the daily ice conditions in the lakes, bays, and ocean around Canada.
  • Dust events over Iraq can be quite nerve-wracking. Using MODIS data, Dr. Miller from our group developed and published a technique that applied a multi-spectral approach to distinguish dust from both clouds and the very complex desert terrain. In this example, the true color image reveals a complicated terrain pattern over the region within NW Persian Gulf. The dust product revealed a mesoscale frontal pattern of dust. Adding model winds depicts the circulation pattern within the dust plume along with a hint of the projected movement within the plume. In this example, dust over the Red Sea is clearly visible, but the extent of the plume isn’t clearly evident until the dust product is observed. In this example over the desert and mountainous terrain of Afghanistan, the dust pattern is visible, however, the dust product exposes the thickness of the plume, the sources of dust production, and hidden pockets of dust as shown here. This final example depicts the envelope of dust. Throughout the Middle East campaign, the NRL dust product has been an invaluable resource for military planners. Although there are a number of dust products available, the true test of dust detection comes in this type of terrain. Miller, S. D., 2003: A consolidated technique for enhancing desert dust storms with MODIS. Geophys. Res. Lett., 30, No. 20, 2071. Courtesy: NRL Satellite FOCUS web team, Naval Research Laboratory, Marine Meteorology Division, Monterey, CA 93943
  • Transcript

    • 1. Land Atmosphere Near real-time Capability for EOSDIS (LANCE) Karen Michael, Kevin Murphy, Dawn Lowe, Jeanne Behnke - ESDIS Project, GSFC Martha Maiden, NASA HQ Chris Justice (UMd), Michael Goodman (NASA HQ) UWG Co-Chairs
    • 2. The Land, Atmosphere Near-real-time Capability for EOS (LANCE)
      • Building on existing EOSDIS elements LANCE provides data from MODIS, OMI, AIRS, MLS, and AMSR-E instruments in near real-time (< 3 hours from observation)
      • Utilizes algorithms used for Standard Science Products, but relaxes requirements for slower ancillary data inputs
      • High operational availability
      • Applications of LANCE data include:
        • Numerical weather & climate prediction/forecasting
        • Monitoring of Natural Hazards
        • Drought Early Warning
        • Disaster Relief
        • Agricultural Monitoring
        • Air Quality
        • Homeland Security
      • A stand alone system Ocean Color Web has been developed for NRT applications
    • 3. LANCE System Architecture lance.nasa.gov
      • Leverages existing EOS processing and distribution capabilities at multiple locations, collocated with science expertise.
      • Provides users with a ‘one-stop-shop’ for EOS near real-time products through the LANCE web portal.
      • Primary driver of latency is in the spacecraft to ground transmission. New approaches and capabilities are being evaluated to effect latency improvements.
      TERRA AQUA / AURA
    • 4. High Operational Availability with Measurable Latency Metrics reports are updated weekly and available from the LANCE website
    • 5. LANCE vs. Standard Product Latency – MODIS Example Standard Processing LANCE Processing (typical) Product Category Terra(hrs) Aqua(hrs) Terra/Aqua (hrs) L1/Cloud Mask 8 25 1.7 L2 Snow 8 25 1.8 L2 Sea Ice 8 25 2.0 L2 Fire 8 25 1.9 L2 Clouds 32 32 2.2 L2 Aerosol 32 32 2.2 L2 LSR 40 41 2.1
    • 6. Near Real-Time vs. Science Quality Products – MODIS Example Science Product Near Real-Time Product Land Surface Reflectance Cloud Top Temperature
    • 7. Near Real-Time vs. Science Quality Products – MODIS Example
      • For LSR the Match is the percentage of NRT data with <1% error margin when compared to the operational Collection 5 codes
      • For Snow, Sea Ice, and Fire, the Match is the exact pixel to pixel match between NRT and operational Collection 5 codes
      • Omission and Commission errors are computed as a percentage of the snow, sea ice, fire in the operational Collection 5 products
      Short Name Science Data Match (% Global) #pixel (%Global) Omission Error #pixel (%) Commission Error #pixel (%) MOD09 LSR-B1 98.94 N/A N/A N/A MOD09 LSR-B2 99.12 N/A N/A N/A MOD09 LSR-B3 99.32 N/A N/A N/A MOD09 LSR-B4 99.16 N/A N/A N/A MOD10 L2 Snow 99.97 31831094 (2.1%) 39751 (0.13%) 44255 (0.14%) MOD29 L2 Sea Ice 99.95 14471848 (5.5%) 8383 (0.06%) 14812 (0.1%) MOD14 L2 Fire 99.97 3207 (0%) 2 (0.06%) 3 (0.09%)
    • 8. Brokerage and Direct Access
      • LANCE focuses on providing data to brokers who perform value added processing and services for downstream users.
      • LANCE provides limited services that enable brokers to quickly create products for hazard, forecasting and disaster response
      • All requests for LANCE enhancements are reviewed by the user working group to ensure enhancements are coordinated with LANCE capabilities and resources
      Application Brokers Application Brokers Direct Access
    • 9. AIRS/MLS Products LANCE-AIRS-Aqua Icelandic Volcano Ash Plume Eyjafjallajokull April 15, 2010
      • LANCE AIRS/MLS
        • AIRS L1B channel subsets are available in BUFR
        • AIRS L2 quality improved using NOAA/NCEP Global Forecast System
        • AIRS is availble in the following formats PNG, GeoTIFF, and KMZ formats using the Open Geospatial Consortium (OGC) Web Map Service (WMS)
      Visible 3km SO2 10km Processing Lead: Bruce Vollmer Instrument Product Categories Average Latency AIRS Radiances, Temperature and Moisture Profiles, Clouds and Trace Gases 75 – 140 minutes MLS Ozone, Temperature 75 – 140 minutes
    • 10. AMSR-E Products LANCE-AMSR-E-Aqua Hurricane Alex, July 1, 2010,08:26 UTC
      • AMSR-E
        • Currently implementing Baseline Near Real-time processing at the AMSR-E SIPS
        • Enhancements will include development of an L2A algorithm specialized for near-real time processing and rapid deployment of updated L2B and L3 algorithms operating at the SIPS.
      Processing Lead: Helen Conover Instrument Product Categories Average Latency AMSR-E Brightness Temperatures, Soil Moisture, Rain Rate, Ocean Products 80 - 135 minutes
    • 11. OMI Products LANCE-OMI-Aura Icelandic Volcano Eyjafjallajokull April 15, 2010, 11:58-12:04 UTC
      • LANCE OMI
        • OMI NRT products are available through LANCE to registered users including OMTO3 (Total Column Ozone), OMSO2 (Sulphur Dioxide) and OMAERUV (Aerosol).
      Processing Lead: Curt Tilmes Instrument Product Categories Average Latency OMI Ozone, Sulfur Dioxide, Aerosols 100 – 165 minutes Latency excludes L3
    • 12. MODIS Products LANCE-MODIS-Terra Oil Spill in Gulf of Mexico May 24, 2010 16:50 UTC
      • LANCE MODIS
        • Currently implementing geographic, parameter and band subsetting in addition to pan sharpening, reprojection, mosaicing, sub-sampling, flood mapping and Geo Tiff format conversion.
        • Future developments include integration of the MODIS Rapid Response, Web Coverage and Mapping along with the Fire Information for Resource Management System
      Processing Lead: Edward Masuoka Instrument Product Categories Average Latency MODIS Radiances, Cloud/Aerosols, Water Vapor, Fire, Snow Cover, Sea Ice, Land Surface Reflectance, Land Surface Temperature 90 – 145 minutes Latency excludes L2G and L3
    • 13. MODIS Global Time Fires Last 10 days
    • 14. Fire Information for Resource Management
      • FIRMS provides the MODIS fire information in small, easy to use formats through the internet and fire email alerts.
      • Capabilities
        • Interactive Web GIS
        • Email alerts (sent to over 100 countries)
        • Subsets of MODIS images
        • Active fire data downloads (KML, Shape, Text files and plug-ins for Google Earth and NASA World-Wind)
      University of Maryland, NASA GSFC, Sigma Space, UN FAO FIRMS Fire Information for Resource Management
    • 15. Active Fire Mapping Source: http://activefiremaps.fs.fed.us/index.php USDA/USFS
    • 16. Flood Mapping Dartmouth Flood Observatory
    • 17. Sea - Ice Monitoring Source: http://www.ec.gc.ca/glaces-ice/default.asp?lang=En&n=D32C361E-1 Canadian Ice Service
    • 18. Dust Detection Over Land The Application: Depicting dust storms over the bright deserts through enhanced imagery. DoD Context: Mission planning, aircraft routing/launch/recovery, weapons selection. Source: Jeff Hawkins Naval Research Laboratory Monterey The Approach: Use multi-spectral MODIS data to identify dust via color, thermal, spectral (11/12) contrast, and 1.38 cirrus filtering. (  ) Vis/NIR Index Temperature Cirrus Flag Split Window dust
    • 19. Drought Monitoring Source: NASA MODIS NDVI Source: NASA MODIS NDVI Mean Vegetation Index Drought Impacted Vegetation Index (2007-08) USDA FAS / UMD / GSFC Dec 3-18, 2007 Dec 19-Jan1, 2008 Jan 1-Jan 16, 2008 Jan 17 – Feb 1, 2008 Feb 2 – Feb 17, 2008 Feb 18 – March 4, 2008 March 5- March 20, 2008 March 21- Apr 5, 2008 April 6- Apr 21, 2008 Vegetation Index Time Series for Cropped Areas in Iraq Vegetation Index Date Current Season (2007-2008) Mean (2000-2007) average average average
    • 20. GEO Agricultural Monitoring Community of Practice
      • Several global/regional scale systems in place – many use MODIS data
      • Data latency is critical for all of these programs
    • 21. Way Forward
      • Initial System Review and User Forum Held (Dec 2009)
        • Decisions on LANCE Registration
        • Need for a User Working Group recognized to guide system development and prioritize activities
      • Weekly Project Telecons to coordinate common issues across processing systems – web portal, metrics, reducing latency, algorithm succession, outreach
      • LANCE User Working Group being formed (Fall Meeting Planned, DC Area) – topics include:
      • - Prioritization of development activities
      • - Further Latency improvements being worked
      • - Access improvements
      • - NASA Near Real Time Symposium Planned for Spring 2011
      • Join us at the Fall 2010 AGU
        • IN07: Current Capabilities and Future Needs of Near Real-Time Data: Perspectives from Users and Producers

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