Title: Technology Development to Integrate Innovative Observation Capabilities into Coupled Wildfire Models for Improved Active Fire Forecasting
Presenting Author: Kyle Hilburn
Organization: CIRA/CSU
Co-Author(s): Jan Mandel, Adam Kochanski, Assad Oberai, Louis Nguyen, Andrew Michaelis, Derek Mallia

Abstract:
Accurate wildfire and smoke modeling is extremely sensitive to the initial conditions: how dry are the fuels, where are the strongest winds, and where is fire burning? How and where these elements of the combustion triangle come together has a dramatic influence on the subsequent fire spread and smoke production. Current observational capabilities lack the coverage, resolution, and timeliness to produce the firefighter-scale forecasts that are needed to significantly improve wildfire management. However, improved observations alone will not lead to improved forecasts because there is a significant technological challenge in connecting observations with models. This proposal will synthesize innovative observation capabilities,including mobile Doppler RADAR observations, the northern California Doppler wind LIDAR network, extremely high-resolution hyperspectral wind fields, and low latency satellite fire detections to develop the technology needed for utilizing these observations in coupled atmosphere-fire forecasting. The scope of this project is not necessarily limited to just these observations, but the technology developed here will be applicable to many other types of observational capabilities that are established through FireSense, integrating novels observations of fuels, the fire state, and the atmosphere. A major aspect of the technology development will be in the development of strategies for the cost-effective use of cloud computing and high-performance computing to obtain the lowest possible latency. We also recognize that physical models alone are unlikely to meet latency requirements, especially for providing probabilistic information from ensemble modeling techniques. Thus, the other major aspect of technology development is the use of machine learning to accelerate the physical model and data assimilation components of the coupled modeling system.