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

该项目的研究目标是构建一个分析框架，通过使用基于集合的自适应采样和采样轨迹协调，以最佳方式瞄准无人驾驶飞机协调观测网络，减少飓风强度预测的不确定性。将首次采用一种基于集合的理论，结合连续自适应采样和快速同化更新，以获得概率流量估计和最佳采样配置。一个新的理论在分散的运动协调将开发占空间和时间变化的流场，超过平台的速度相对于流量。 该框架将使用飓风模型的层次结构进行评估，以评估流量概率预测的改进。拟议的研究将取得广泛适用于环境采样的理论进展，包括基于集合的近连续数据同化，基于集合的自适应采样，以及动态流场中无人平台的分散协调。该研究项目的更广泛意义在于其通过整合下一个多无人机自适应运动协调的新策略生成天气预报模型。更好的预测和减少飓风强度预测的不确定性，通过改进应急管理决策，减少生命和财产损失。强度预报依赖于在低于有人驾驶飞机安全飞行高度的气压、风速和温度观测值的同化。小型无人驾驶飞机可以通过在低至100米的高度飞行来填补观测数据的这一重要空白。一个协调的无人驾驶飞机小组可以通过在规定的空间分布中收集低空观测结果来进一步减少强度预报的不确定性。该项目还将为本科生和研究生提供一个独特的培训机会，在大气科学和动态控制系统的交叉点。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).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.