Technology Development to Integrate Innovative Observation Capabilities into Coupled Wildfire Models for Improved Active Fire Forecasting

Accurate wildfire and smoke modeling is extremely sensitive to the initial conditions: how dry are the fuels, where are the strongest winds, where is fire burning, and how high is the smoke being lofted? 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. Note that while WRF-SFIRE provides the specific application in this project, the machine learning development will be applicable to other modeling systems, and to data assimilation and ensemble modeling for fire and smoke forecasting in general. Thus, the proposed project is relevant to the mission objectives to ?address computational challenges for modeling and for data acquisition, fusion, and processing in a real-time environment? and to ?facilitate machine learning and artificial intelligence to create new data products needed for wildfire management?. This project will demonstrate the integration of innovative observations into a coupled wildfire model with the purpose to support wildfire airborne field campaigns. These observations would be assimilated into the fire modeling framework to provide the best possible four-dimensional representation of the atmospheric environment, composition, and fire conditions. These fields will be useful for answering science questions related to campaign objectives, while the data from the campaign would be useful for evaluating fire and smoke models.

Enhances the capabilities for existing science instruments for monitoring pre-fire, active-fire, and post-fire situations, reduces the power and mass of these instruments, and enables unprecedented observations in support of wildfire science through distributed observing systems and the information technologies needed for their support.

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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