Collaborative Research: Targeting Observations of Tropical Cyclones Using Cooperative Control of Unmanned Aircraft

“该提案将使用2009年美国复苏和再投资法案（公法111-5）提供的资金，并符合2009年3月20日白宫备忘录第2节的要求，题为“确保负责任的复苏法案资金支出”。我还确认，作为认可的项目官员，该提案不支持《复苏法案》A部分第1604条所述的项目。”本项目的研究目标是构建一个分析框架，通过基于集合的自适应采样和采样轨迹协调，优化无人飞机的协调观测网络，以减少飓风强度预测的不确定性。将首次采用基于集成的理论，结合连续自适应采样和快速同化更新，以产生概率流量估计和最佳采样配置。一个新的理论在分散运动协调将发展，以解释空间和时间变化的流场超过相对于流动的平台速度。该框架将使用飓风模型的层次来评估流量概率预测的改进。本研究将取得广泛适用于环境采样的理论进展，包括基于集成的近连续数据同化、基于集成的自适应采样、动态流场中无人平台的分散协调等。该研究项目更广泛的意义在于，通过将下一代天气预报模型与多架无人机自适应运动协调的新策略相结合，它有可能改善飓风预报。通过改进应急管理决策，更好地预报和减少飓风强度预测的不确定性可以减少生命和财产损失。强度预报依赖于同化在低于有人驾驶飞机安全飞行高度的气压、风速和温度观测。小型无人驾驶飞机可以在低至100米的高度飞行，填补这一重要的观测数据空白。一个协调的无人驾驶飞机团队可以通过在规定的空间分布中收集低空观测数据，进一步减少强度预测的不确定性。该项目还将为本科生和研究生提供大气科学和动力控制系统交叉领域的独特培训机会。

"This proposal will be awarded using funds made available by the American Recovery and Reinvestment Act of 2009 (Public Law 111-5), and meets the requirements established in Section 2 of the White House Memorandum entitled, Ensuring Responsible Spending of Recovery Act Funds, dated March 20, 2009. I also affirm, as the cognizant Program Officer, that the proposal does not support projects described in Section 1604 of Division A of the Recovery Act." The research objective of this project is to construct an analytical framework to reduce uncertainty in forecasts of hurricane intensity by optimally targeting a coordinated observing network of unmanned aircraft using ensemble-based adaptive sampling and coordination of sampling trajectories. An ensemble-based theory combined with serial adaptive sampling and rapid assimilation updates will be employed for the first time to yield probabilistic flow estimates and optimal sampling configurations. A new theory in decentralized motion coordination will be developed to account for spatially and temporally variable flow fields that exceed the platform speed relative to the flow. The framework will be evaluated using a hierarchy of hurricane models to assess improvements in probabilistic forecasts of the flow. The proposed research will achieve theoretical advances broadly applicable to environmental sampling, including ensemble-based assimilation of near-continuous data, ensemble-based adaptive sampling, and decentralized coordination of unmanned platforms in dynamic flow fields.The broader significance of this research project lies in its potential to improve hurricane forecasts by integrating next-generation weather prediction models with novel strategies for adaptive motion coordination of multiple unmanned aircraft. Better forecasts and reduced uncertainty in hurricane intensity prediction lead to reduced loss of life and property via improved emergency management decisions. Intensity forecasts are dependent on the assimilation of observations of pressure, wind velocity, and temperature at altitudes lower than manned aircraft can safely fly. Small, unmanned aircraft can fill this important gap in observation data by flying at altitudes as low as one hundred meters. A coordinated team of unmanned aircraft can further reduce the uncertainty in intensity forecasts by collecting low-altitude observations in a prescribed spatial distribution. The project will also provide undergraduate and graduate students with a unique training opportunity at the intersection of atmospheric science and dynamical control systems.