Hazards SEES Type 2: WIFIRE: A Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience Cyberinfrastructure for Wildfires

加州大学圣地亚哥分校(UCSD)获得拨款，用于建立端到端的网络基础设施(CI)，称为WIFIRE，用于对野火行为进行实时和数据驱动的模拟、预测和可视化。圣地亚哥超级计算机中心(SDSC)和加州大学第二分校S高通研究所将与加州大学雅各布斯工程学院和UMD消防工程系的合作伙伴共同构建这个集成的CI系统，以支持在面对城市动态和气候变化时对不断变化的火灾生态制度的社会生态弹性。WIFIRE CI将网络观测(如异类卫星数据和实时遥感数据)与信号处理、可视化、建模和数据同化方面的计算技术相结合，以提供可扩展的技术和教育解决方案，以监测天气模式以预测野火的蔓延速度。我们由科学家、工程师、技术专家、政府政策经理、私营行业和消防员组成的协作WIFIRE团队将设计和实施CI路径，以实现野火管理的联合创新。科学工作流程将被用作综合分布式方案编制模型，并将简化数据驱动的模拟、预测和可视化工程模块的实施，同时允许与大规模计算设施整合。WIFIRE将通过专门的网络界面和用户指定的环境事件警报，在野火发生前、期间和之后向接收者广播，可扩展到具有不同技能水平的用户。该方法是可扩展的，允许许多传感器接受用户指定的数据处理算法，以在几秒钟内生成阈值警报。将这些传感器数据集成到快速可用的火灾图像数据和模型中，将更好地在地方、州、国家和国际层面实现态势感知、响应和决策支持。WIFIRE的产品将首先传播给项目合作者(SDG&amp；E、CAL FIRE、USFS)，覆盖学术、私人和政府实验室，同时为应急官员和普通公众创造价值。未来，政府机构可能会使用WIFIRE在野火事件期间拯救生命和财产，在发生之前测试响应和疏散场景的有效性，并评估高密度传感器网络在改进火灾和天气预报方面的有效性。因此，WIFIRE的高密度网络将成为未来在全球范围内应用的试验台。该团队涵盖了一系列合作者，并将创建一个具有直观工作流程的开源CI环境，从而为广泛的科学和工程学科产生可重复使用的软件组件，这些组件可以扩展到中学教育。结果通过SDSC的一个互动网站传播，在该网站上，从高中到研究生的学生可以参与上传他们自己的数据记录、数据处理或数据驱动的警报。

The University of California at San Diego (UCSD) is awarded a grant to build an end-to-end cyberinfrastructure (CI), called WIFIRE, for real-time and data-driven simulation, prediction and visualization of wildfire behavior. San Diego Supercomputer Center (SDSC) and Calit2's Qualcomm Institute will build this integrated CI system for supporting social-ecological resilience to the changing fire ecology regime in the face of urban dynamics and climate change, together with collaborators from UCSD's Jacobs School of Engineering and UMD's Department of Fire Protection Engineering. The WIFIRE CI integrates networked observations, e.g., heterogeneous satellite data and real-time remote sensor data, with computational techniques in signal processing, visualization, modeling and data assimilation to provide a scalable, technological, and educational solution to monitor weather patterns to predict a wildfire's Rate of Spread. Our collaborative WIFIRE team of scientists, engineers, technologists, government policy managers, private industry, and firefighters will architect and implement CI pathways that enable joint innovation for wildfire management. Scientific workflows will be used as an integrative distributed programming model and will simplify the implementation of engineering modules for data-driven simulation, prediction and visualization while allowing integration with large-scale computing facilities. WIFIRE will be scalable to users with different skill-levels via specialized web interfaces and user-specified alerts for environmental events broadcasted to receivers before, during and after a wildfire. The approach is scalable which allows many sensors to be subjected to user-specified data processing algorithms to generate threshold alerts within seconds. Integration of this sensor data into both rapidly available fire image data and models will better enable situational awareness, responses and decision support at local, state, national, and international levels.The products of WIFIRE will be initially disseminated to project collaborators (SDG&E, CAL FIRE, USFS), covering academic, private, and government laboratories while generating value to emergency officials, and consequently to the general public. WIFIRE may be used by government agencies in the future to save lives and property during wildfire events, test the effectiveness of response and evacuation scenarios before they occur and assess the effectiveness of high-density sensor networks in improving fire and weather predictions. WIFIRE's high-density network, therefore, will serve as a testbed for future applications worldwide. The team is inclusive across a spectrum of collaborators and will create an open-source CI environment with intuitive workflows that lead to reusable software components for a wide range of science and engineering disciplines that can be extended to secondary education. Results are disseminated via an interactive website at SDSC in which students from high school to graduate level can participate in uploading their own data logging, data processing or data-driven alerts.