Demonstrating the Validity of a Wildfire DDDAS

  • Craig C. Douglas
  • Jonathan D. Beezley
  • Janice Coen
  • Deng Li
  • Wei Li
  • Alan K. Mandel
  • Jan Mandel
  • Guan Qin
  • Anthony Vodacek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of weather and wildfire behavior from real-time weather data, images, and sensor streams. The system changes the forecast as new data is received. We encapsulate the model code and apply an ensemble Kalman filter in time-space with a highly parallel implementation. In this paper, we discuss how we will demonstrate that our system works using a DDDAS testbed approach and data collected from an earlier fire.


Data Assimilation Ensemble Member Wildland Fire Observation Function Approximate Inverse 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Craig C. Douglas
    • 1
    • 2
  • Jonathan D. Beezley
    • 4
  • Janice Coen
    • 3
  • Deng Li
    • 1
  • Wei Li
    • 1
  • Alan K. Mandel
    • 1
  • Jan Mandel
    • 4
  • Guan Qin
    • 5
  • Anthony Vodacek
    • 6
  1. 1.Department of Computer ScienceUniversity of KentuckyLexingtonUSA
  2. 2.Department of Computer ScienceYale UniversityNew HavenUSA
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.Department of Mathematical SciencesUniversity of Colorado at Denver and Health Sciences CenterDenverUSA
  5. 5.Institute for Scientific ComputationTexas A&M UniversityCollege StationUSA
  6. 6.Center for Imaging ScienceRochester Institute of TechnologyRochesterUSA

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