Scientists are Building a Cyberinfrastructure to Predict and Visualize Wildfire Behavior
WIFIRE system will use sophisticated computer techniques to monitor and predict the spread of wildfire.
As the trend for larger and more frequent wildfires continues, a team of scientists, engineers, technologists, firefighters and government and industry professionals is working on a project, called WIFIRE, to build an end-to-end cyberinfrastructure for simulation, prediction and visualization of wildfire behavior.
The WIFIRE system will analyze wildfire dynamics with specific emphasis on the climate. The system will integrate heterogeneous satellite information and remote sensor data by computational techniques like signal processing, visualization, modeling and data assimilation to develop a scalable method to monitor weather patterns and predict the spread of a wildfire.
The project started with a three-year, $2.65 million grant to the University of California at San Diego in October 2013 when participants in the project began integration and cataloging of data from sensors, satellites and scientific models to create scalable wildfire models. Participants include the San Diego Supercomputer Center (SDSC), the California Institute for Telecommunications and Information Technology’s Qualcomm Institute and the University of Maryland.
WIFIRE will include the remote sensor network that’s part of the High Performance Wireless Research and Education Network, started by SDSC in 2000. That program has been collecting environmental data for the last decade, which has been merged with computational models into visualizations and shared with partners, including the U.S. Forest Service and the California Department of Forestry and Fire Protection.
“WIFIRE will be scalable to users with different skill levels using specialized Web interfaces and user-specific alerts for environmental events broadcast to receivers before, during and after a wildfire,” said Ilkay Altintas, principal investigator for WIFIRE, in an SDCS news story. “This approach allows many sensors to be subjected to user-specific data algorithms to generate threshold alerts within seconds.”
Altintas said integration of the data into fire image data and models will lead to better situational awareness, response and decision-making at the state, local and federal levels.
She said the project’s findings will advance the understanding of wildfires and that knowledge will be transferable to other regions, both nationally and globally. “Proposed solutions will be readily available and transferable through open source workflows and Web services, as UC San Diego has done for data sharing during past disasters such as the 2010 Haiti earthquake and the 2011 tsunami and earthquake in Japan.”