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

野火是一种代价高昂的自然灾害。较新的建模系统将数值天气预报模型与用于模拟火灾行为的传统工具相结合，使它们能够更真实地模拟火灾如何展开，然而，应用它们来准确预测火灾？S增长是一个困难的预测挑战。主要的挑战是，随着天气预报的准确性随着时间的推移而下降，误差会累积，而且一些过程无法被该模型预测到，例如在火灾之前放样燃烧的灰烬(可能会引发新的火灾)和灭火。S团队最近的工作将大气-荒地火灾环境耦合天气-火灾行为模拟系统与可见光和红外成像辐射计套件(VIIRS)仪器的卫星火灾探测数据相结合，以点燃已经在进行的火灾，从而能够准确地预测未来12-24小时的火灾增长；这些模拟序列可以保持对从卫星检测到火灾直至扑灭的合理预测。限制预测技能的剩余挑战是复杂、非线性自然系统建模的传统方法所共有的，包括累积误差和以最佳方式利用所有可用的数据源。该团队将研究如何将新的、未得到充分利用的传感器和数据源与建模更紧密地结合在一起，从而潜在地改变野火检测和预测。S将把先进的技术整合到团队中，将系统过渡到运营中，以更早的野火检测、更快的反应和更好的火灾预报造福社会。目标是开发创新的动态数据系统技术，改进野火检测和增长预测。这项工作将致力于三个目标，1)开发和应用算法(由其他数据指导)，以提取新的和现有的(但未得到充分利用的)野火探测和地图数据源；2)开发和应用算法，将野火探测和监测的异步数据与天气-野地火灾模型相结合；以及3)衡量野火探测时间和预测火灾增长的改善。这些方法包括创建一个自适应控制系统，根据新数据的到达启动预测；允许传感器通知算法在哪里寻找其他未得到充分利用的数据集；创建用于智能、迭代处理大型数据集的方法；以及使用模型预测来驱动这些智能搜索。这些技术可以广泛应用于目前以传统方式完成的其他非线性系统，这些系统依赖于固定的检测算法和规则的、周期性的输入数据到达来严格调度例程、重复建模。

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.