Workshop Report on Data-Driven Operational Wildfire Spread Modeling Released

Prof. Gollner and Trouve recently released a Workshop Report providing a record of the discussions that took place during the workshop entitled “Towards Data-Driven Operational Wildfire Spread Modeling” held on January 12-13, 2015, at the University of California, San Diego. The workshop was organized as part of WIFIRE, a collaborative project sponsored by the National Science Foundation (NSF) between the San Diego Supercomputer Center, Calit2’s Qualcomm Institute and Jacobs School of Engineering at the University of California at San Diego (UCSD) and the Department of Fire Protection Engineering at the University of Maryland (UMD). The objective of WIFIRE is to build a cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior (see http://wifire.ucsd.edu). WIFIRE is funded by NSF Award #1331615 as part of the Interdisciplinary Research in Hazards and Disasters (Hazards SEES) program.

The objectives of the WIFIRE workshop were: (1) to identify technical barriers and milestones that need to be overcome in order to develop validated data-driven wildfire spread models and make them operational; and (2) to bring together leading representatives of the wildfire research community, the geosciences community and the fire science community. The wildfire research community has relevant expertise on wildfire operations; the geosciences community has relevant expertise on large-scale effects in wildfires (e.g., the coupling with atmospheric phenomena); the fire science community has relevant expertise on flame-scale effects in wildfires (e.g., the response of the fire to changing local conditions). The workshop was organized around four main topical areas and corresponding breakout groups, including operational rate-of-spread models for wildfire spread, CFD models, wildfire data, and data assimilation. Our goal in this report is to document and share the substance and scope of the workshop discussions and to thereby invite the wider research community to support, engage in, and contribute to the general effort to develop operational data-driven tools for wildfire spread predictions.