HR: 0800h
AN: IN11A-0100    [Abstracts]
TI: Sensor Management for Applied Research Technologies (SMART) On Demand Modeling (ODM) Project
AU: * Conover, H
EM: hconover@itsc.uah.edu
AF: University of Alabama in Huntsville, Information Technology and Systems Center, Huntsville, AL 35899, United States
AU: Berthiau, G
EM: berthiau@nsstc.uah.edu
AF: University of Alabama in Huntsville, Earth System Science Center, Huntsville, AL 35899, United States
AU: Blakeslee, R
EM: rich.blakeslee@nasa.gov
AF: NASA MSFC, VP61, Huntsville, AL 35812, United States
AU: Botts, M
EM: mike.botts@uah.edu
AF: University of Alabama in Huntsville, Earth System Science Center, Huntsville, AL 35899, United States
AU: Goodman, M
EM: michael.goodman@nasa.gov
AF: NASA MSFC, VP61, Huntsville, AL 35812, United States
AU: Hood, R
EM: robbie.hood@nasa.gov
AF: NASA MSFC, VP61, Huntsville, AL 35812, United States
AU: Jedlovec, G
EM: gary.jedlovec@nasa.gov
AF: NASA MSFC, VP61, Huntsville, AL 35812, United States
AU: Li, X
EM: xli@itsc.uah.edu
AF: University of Alabama in Huntsville, Information Technology and Systems Center, Huntsville, AL 35899, United States
AU: Lu, J
EM: jlu@itsc.uah.edu
AF: University of Alabama in Huntsville, Information Technology and Systems Center, Huntsville, AL 35899, United States
AU: Maskey, M
EM: mmaskey@itsc.uah.edu
AF: University of Alabama in Huntsville, Information Technology and Systems Center, Huntsville, AL 35899, United States
AB: On-demand data processing and analysis of Earth science observations will facilitate timely decision making that can lead to the realization of the practical benefits of satellite instruments, airborne and surface remote sensing systems. However, a significant challenge exists in accessing and integrating data from multiple sensors or platforms to address Earth science problems because of the large data volumes, varying sensor scan characteristics, unique orbital coverage, and the steep learning curve associated with each sensor, data type and associated products. The development of sensor web capabilities to autonomously process these data streams (whether real-time or archived) provides an opportunity to overcome these obstacles and facilitate the integration and synthesis of Earth science data and weather model output. The authors will present initial results from Sensor Management for Applied Research Technologies (SMART) On Demand Modeling (ODM). This NASA- funded project is developing and demonstrating the readiness of Open Geospatial Consortium Sensor Web Enablement (SWE) capabilities that integrate both Earth observations and forecast model output into new data acquisition and assimilation strategies. First year accomplishments include development of numerous Sensor Observation Services (SOS) and an SOS registry for sensor data discovery and access, as well as a prototype user application, built on these services, for validating cloud types as observed by multiple instruments. The three-year goal of this project is to demonstration how SWE-enabled systems can have practical and efficient uses in the Earth science community for enhanced data set generation, real-time data assimilation with operational applications, and for autonomous sensor tasking for unique data collection.
UR: http://smart.itsc.uah.edu
DE: 3315 Data assimilation
DE: 3394 Instruments and techniques
DE: 4294 Instruments and techniques
DE: 9805 Instruments useful in three or more fields
DE: 9820 Techniques applicable in three or more fields
SC: Earth and Space Science Informatics [IN]
MN: 2007 Fall Meeting