EAGER-DynamicData: Transforming Wildfire Detection and Prediction using New and Underused Sensor and Data Sources Integrated with Modeling

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Wildland fires are a costly natural hazard. Newer modeling systems have combined numerical weather prediction models with the traditional tools used to model fire behavior, making them more capable of realistically modeling how fires unfold, however, applying them to accurately anticipate a fire?s growth is a difficult forecasting challenge. The principal challenges are that errors accumulate as the accuracy of weather forecasts decreases with time and that some processes cannot be anticipated by the model such as the lofting of burning embers ahead of the fire (potentially starting new fires) and firefighting. The team?s recent work has combined theCoupled Atmosphere-Wildland Fire Environment (CAWFETM) weather?fire behavior modeling system with satellite-based fire detection data from the Visible and Infrared Imaging Radiometer Suite (VIIRS) instrument to ignite fires already in progress, allowing an accurate forecast of fire growth for the next 12-24 hours; sequences of these simulations can maintain a reasonable forecast of fire growth from the time the satellite detects it until it is extinguished. The remaining challenges limiting the forecast skill are common to traditional approaches to modeling complex, nonlinear natural systems and include accumulating error and optimally exploiting all available data sources. The team will investigate how more tightly integrating new and underused sensor and data sources with the modeling could potentially transform both wildfire detection and prediction. Advances will be integrated into the team?s work transitioning the system into operations, benefiting society with earlier wildfire detection, faster response, and better fire forecasts.The goal is to develop innovative Dynamic Data System techniques that improve wildfire detection and growth forecasting. The work will address three objectives, 1) develop and apply algorithms (steered by other data) to distill new and existing (but underutilized) sources of data on wildfire detection and mapping, 2) develop and apply algorithms to integrate asynchronousdata on wildfire detection and monitoring with coupled weather?wildland fire models, and 3) measure the improvement in wildfire detection time and forecasted fire growth. The methods include creating an adaptive control system for initiating forecasts based on the arrival of new data; allowing sensors to inform algorithms where to look in other underutilized datasets; creatingapproaches for intelligent, iterative processing of large datasets; and using model forecasts to drive these intelligent searches. The techniques could have broad application across other nonlinear systems that are currently done in a traditional manner with rigorous scheduling of routine, repeated modeling relying on fixed detection algorithms and regular, periodic input dataarrival.