Collaborative Research: CDI-Type II--Integrated Weather and Wildfire Simulation and Optimization for Wildfire Management

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。野火造成巨大的破坏，包括生命损失和财产、基础设施和环境的破坏。野火管理的复杂性源于多个系统组件之间不确定的动态相互作用和依赖关系。其中包括高度动态和非线性的野火行为、天气条件和消防资源管理。在以前的研究中，这些成分在很大程度上是在各自的领域中单独处理的。为了实现有效的野火管理，需要将所有这些组成部分整合为一个整体的决策支持工具。该项目的目标是开发新的模型和计算方法，将天气预报、野火模拟、数据同化和随机优化相结合，以实现有效的野火响应管理。在此过程中，该项目将在野火建模和管理方面取得两项关键的范式转换进展：1)耦合天气和野火建模和数据同化，用于双向交互式动态天气-野火预测；2)集成野火模拟和随机优化，用于野火控制。该项目侧重于计算思维，以理解天气和野火行为的自然系统的复杂性，以及消防资源管理的人造系统。由于与这些系统相关的问题数据的随机性和多尺度性质，该项目还涉及数据同化和并行/分布式计算方法，用于稳健的天气和野火行为预测。该项目的成果将有助于野火管理，通过强有力的消防资源管理决策减轻野火造成的损失。与德克萨斯州林务局的合作将验证并将由此产生的知识、系统和工具转移到真正的野火管理中。研究结果也将有利于其他应急响应应用，如国土安全方面的应用。该项目将为本科生和研究生提供跨学科培训，并将开发一个基于网络的教育和培训模拟环境。该项目还将加强和补充乔治亚州立大学（GSU）、俄克拉荷马大学（OU）和德克萨斯农工大学（TAMU）针对少数族裔学生的现有K-12外展项目，并将共同努力扩大弱势学生在研究和教育中的参与。研究结果将在会议上发表，并在期刊上发表。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Wildfires cause great destruction including the loss of life and damage to property, infrastructure and the environment. The complexity of wildfire management arises from the uncertain dynamic interactions and dependencies among multiple system components. These include highly dynamic and nonlinear wildfire behaviors, weather conditions, and firefighting resource management. In previous research, these components have been largely treated in isolation in their own fields. To achieve effective wildfire management, decision-making support tools that integrate all these components as a whole are needed. The objective of this project is to develop new models and computation methods that integrate weather prediction, wildfire simulation, data assimilation and stochastic optimization for effective wildfire response management. In doing so, the project will make two key paradigm-shifting advances in wildfire modeling and management: 1) coupled weather and wildfire modeling and data assimilation for two-way interactive dynamic weather-wildfire prediction, and 2) Integrated wildfire simulation and stochastic optimization for wildfire containment. The project focuses on computational thinking for understanding the complexity in the natural systems of weather and wildfire behavior, and in the man-made system of firefighting resources management. Due to the stochastic and multiscale nature of the problem data associated with these systems, the project also involves data assimilation and parallel/distributed computational methods for robust weather and wildfire behavior predictions. The results of this project will aid in wildfire management to alleviate losses caused by wildfires through robust firefighting resource management decisions. The collaboration with the Texas Forest Service will enable validation and transfer of the resulting knowledge, systems and tools to real wildfire management. The results of the research will also benefit other emergency response applications such as those in homeland security. The project will provide interdisciplinary training to both undergraduate and graduate students and will develop a web-based education and training simulation environment. The project will also strengthen and complement existing K-12 outreach programs at Georgia State University (GSU), University of Oklahoma (OU), and Texas A&M University (TAMU) for minority students, and will make a concerted effort to broaden the participation of underrepresented students in research and education. Results of the research will be presented at conferences and published in refereed journals.