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

EAGER-DynamicData：使用新的和未充分利用的传感器以及与建模集成的数据源来改变野火检测和预测

• 批准号: 1462247
• 负责人: Janice Coen
• 金额: $ 10万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462247&HistoricalAwards=false
• 关键词: EAGER; DynamicData; Transforming; Wildfire; Detection

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.

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测模型与用于模拟火灾行为的传统工具相结合，使它们更能够逼真地模拟火灾如何展开，然而，将它们应用于准确预测火灾？的增长是一个很难预测的挑战。主要的挑战是，随着天气预报的准确性随着时间的推移而降低，误差会积累，并且模型无法预测一些过程，例如火灾前燃烧余烬的放样（可能引发新的火灾）和消防。团队？最近的工作结合了耦合大气-荒地火灾环境（CAWFETM）天气？利用来自可见光和红外成像辐射计套件（VIIRS）仪器的基于卫星的火灾探测数据，使用火灾行为建模系统点燃已经发生的火灾，从而能够准确预测未来12-24小时的火灾发展情况;这些模拟的顺序可以保持对从卫星探测到火灾直至其被扑灭的火灾发展情况的合理预测。限制预测技能的其余挑战是传统方法对复杂非线性自然系统建模的常见挑战，包括累积误差和最佳利用所有可用数据源。该团队将研究如何更紧密地将新的和未充分使用的传感器和数据源与建模相结合，从而可能改变野火检测和预测。先进将融入团队？我们的工作是将系统转化为操作，通过更早的野火检测，更快的响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，以改善野火检测和增长预测。这项工作将解决三个目标，1）开发和应用算法（由其他数据引导），以提取新的和现有的（但未充分利用）的数据源野火检测和映射，2）开发和应用算法，以整合bandrousdata野火检测和监测与耦合天气？荒地火灾模型，和3）测量野火检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，用于根据新数据的到来启动预测;允许传感器通知算法在其他未充分利用的数据集中查找;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进行的其他非线性系统，这些系统具有严格的常规调度，依赖于固定检测算法和定期定期输入数据到达的重复建模。