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  <event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/241/A-General-Algorithm-for-Gridding-Earth-Sensing-Scanning-Instruments">
    <rdfs:label><![CDATA[A General Algorithm for Gridding Earth Sensing Scanning Instruments]]></rdfs:label>
    <event:title><![CDATA[A General Algorithm for Gridding Earth Sensing Scanning Instruments]]></event:title>
    <event:speaker><person:MSStudent rdf:about="http://ebiquity.umbc.edu/person/html/David/Chapman/"><person:name><![CDATA[David  Chapman]]></person:name><rdfs:label><![CDATA[David  Chapman]]></rdfs:label></person:MSStudent></event:speaker>
    <event:startDate rdf:datatype="&xsd;dateTime">2008-05-05T10:00:00-05:00</event:startDate>
    <event:endDate rdf:datatype="&xsd;dateTime">2008-05-05T12:00:00-05:00</event:endDate>
    <event:location><![CDATA[325 ITE]]></event:location>
    <event:abstract><![CDATA[Gridding in remote sensing must re-project observations from their
original coordinate system based on satellite orbit and attitude to a grid
defined by Earth coordinates.  Primitive methods assume that observations
are located at points on Earth and typically average observations in grid
cells, or interpolate geolocated observations.  These approaches are
inaccurate, because they do not make use of the instrument’s footprint
geometry, and spatial response.  Observation Coverage (Obscov) gridding
techniques make use of the satellite optics and geometry to more
accurately describe coverage of a footprint on within each grid cell. 
Obscov gridding provides significant accuracy improvements exceeding 1
Kelvin Brightness Temperature over most regions on Earth for a 12 micron
window channel on-board the Atmospheric Infrared Sounder (AIRS).  Existing
Obscov algorithms are only applicable to specific instruments and depend
heavily on implicitly defined spatial response functions.  We make use of
raycasting and adaptive grid numerical integration to compute Obscov for
the spatial response function of any instrument while processing streaming
satellite observation data faster than 400 Megabits/second on a 6 machine
cluster.  We discuss the quality benefits of our algorithm by analyzing
the results of gridded AIRS infrared sensor data with 324 operational
spectral channels.  We also address parallel processing issues to
integrate AIRS Obscov gridding with SOAR, an on demand climate processing
system built on a 122 processor blade server.]]></event:abstract>
    <event:host><person:PrincipalFaculty rdf:about="http://ebiquity.umbc.edu/person/html/Milton/Halem/"><person:name><![CDATA[Milton  Halem]]></person:name><rdfs:label><![CDATA[Milton  Halem]]></rdfs:label></person:PrincipalFaculty></event:host>
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