Towards a Dynamic Data Driven Application System for Wildfire Simulation

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman filter are presented, for out-of-sequence data assimilation, hidden model states, and highly nonlinear problems. Parallel implementation and web-based visualization are also discussed.

[1]  M Beně,et al.  Mathematical and Computational Aspects of Solidification of Pure Substances , 2022 .

[2]  Larry Radke,et al.  The WildFire Experiment (WiFE): Observations with Airborne Remote Sensors , 2000 .

[3]  Stephen Gilmore,et al.  Evaluating the Performance of Skeleton-Based High Level Parallel Programs , 2004, International Conference on Computational Science.

[4]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[5]  Christopher K. Wikle,et al.  Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.

[6]  Thiagalingam Kirubarajan,et al.  Out-of-sequence measurement processing for tracking ground target using particle filters , 2002, Proceedings, IEEE Aerospace Conference.

[7]  G. Evensen,et al.  Sequential Data Assimilation Techniques in Oceanography , 2003 .

[8]  Janice L. Coen,et al.  Simulation of the Big Elk Fire using coupled atmosphere–fire modeling , 2005 .

[9]  Ying Li,et al.  AUTOMATIC ESTIMATION OF DIRECTION OF PROPAGATION OF FIRE FROM AERIAL IMAGERY , 2002 .

[10]  Wei Zhao,et al.  A Note on Dynamic Data Driven Wildfire Modeling , 2004, International Conference on Computational Science.

[11]  M. Finney.  FARSITE : Fire Area Simulator : model development and evaluation , 1998 .

[12]  Ambrose E. Ononye,et al.  IMPROVED FIRE TEMPERATURE ESTIMATION USING CONSTRAINED SPECTRAL UNMIXING , 2002 .

[13]  Anthony Vodacek,et al.  Autonomous field-deployable wildland fire sensors , 2003 .

[14]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[15]  Andrew V. Knyazev,et al.  Toward the Optimal Preconditioned Eigensolver: Locally Optimal Block Preconditioned Conjugate Gradient Method , 2001, SIAM J. Sci. Comput..

[16]  Judith Winterkamp,et al.  Studying wildfire behavior using FIRETEC , 2002 .

[17]  Chris Snyder,et al.  Toward a nonlinear ensemble filter for high‐dimensional systems , 2003 .

[18]  A. Marrs,et al.  A Bayesian approach to multi-target tracking and data fusion with out-of-sequence measurements , 2001 .

[19]  T. Clark,et al.  Description of a coupled atmosphere–fire model , 2004 .

[20]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[21]  G. Evensen.  Sampling strategies and square root analysis schemes for the EnKF , 2004 .

[22]  Craig J. Johns,et al.  A two-stage ensemble Kalman filter for smooth data assimilation , 2008, Environmental and Ecological Statistics.

[23]  T. Clark,et al.  Severe Downslope Windstorm Calculations in Two and Three Spatial Dimensions Using Anelastic Interactive Grid Nesting: A Possible Mechanism for Gustiness , 1984 .

[24]  J. Schott,et al.  Remote optical detection of biomass burning using a potassium emission signature , 2002 .