Dr. Jie Ji, Professor, State Key Laboratory of Fire Science, University of Science and Technology of China (USTC), China

Biography: Dr. Jie Ji is one Full Professor at the State Key Laboratory of Fire Science, University of Science and Technology of China (USTC). He obtained his Ph.D. degree in USTC in 2008 and was promoted to full professor in 2015. Currently he is the vice director of National & Local Joint Engineering Research Center of Thermal Safety Technology, the vice director and secretary general of Popular Science & Public Education Committee of CFPA. He serves as editorial board member of Fire Safety Journal (the official journal of IAFSS) and Fire Technology (the official journal of NFPA and SFPE). He has published over 70 SCI papers being as the first or corresponding author and two books. He has presided the National Science Fund for Excellent Young Scholars funded by National Natural Science Foundation of China and several projects supported by National Key Research and Development Plan, etc.

Research Interest: Fire dynamics, prevention and control of building fires, real-time forecasting of building and wildland fires.

Speech Title: Real-Time Forecasting of Large-Scale Wildland Fire Spread Using FARSITE Tool and Ensemble Transform Kalman Filter (ETKF)

Abstract: Ensemble transform Kalman filter (ETKF) is an extension of ensemble Kalman filter (EnKF), which avoids using "perturbed observations" to eliminate additional sampling errors. This paper demonstrates the capability of ETKF algorithm for sequentially correcting dynamically evolving fire perimeter positions at regular time intervals to enhance the prediction accuracy of wildfire spread. Forecast error covariance inflation scheme is adopted in the ETKF to address the underestimation problem of forecast error covariance of EnKF. Coupled to a widely-used fire spread simulator, FARSITE, the proposed approach is employed to a landscape with complex topography, where a fire barrier is also considered. The merits of ETKF algorithm for wildfire spread prediction are highlighted by simulation experiments using synthetically-generated observations. In order to quantitatively evaluate the prediction performance of ETKF, this paper has adopted a conservative index, Hausdorff distance, which is widely used in image processing area. This work is the first attempt of applying ETKF to wildfire spread simulation. The ETKF algorithm has been demonstrated to be more accurate than EnKF for a given ensemble size for wildfire spread simulation. The findings show that the ETKF-based data assimilation strategy is a promising tool for large-scale wildfire spread simulation.

Keywords: Wildland Fire Spread Simulation, Ensemble Transform Kalman Filter, State Estimation, Fire Barrier, Hausdorff Distance