DDDAS-SMRP:Targeted Data Assimilation for Disturbance-Driven Systems: Space Weather Forcasting in the Ionosphere and Thermosphere Using a Dynamically Steered Incoherent Scatter Ra

DDDAS-SMRP：干扰驱动系统的定向数据同化：使用动态引导非相干散射 Ra 进行电离层和热层空间天气预报

• 批准号: 0539053
• 负责人: Dennis Bernstein
• 金额: $ 47万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0539053&HistoricalAwards=false
• 关键词: DDDAS; SMRP; Targeted; Data; Assimilation

Scientists and engineers, as well as the general public, are becoming increasingly aware of the threat that solar disturbances pose to humans and technological systems. To improve space weather prediction, this project combines a physics-based model of the ionosphere and thermosphere with targeted measurements, a process known as targeted data assimilation. The proposed research includes extensions of data assimilation algorithms to address nonlinear dynamics, model error, and computational complexity. Dynamically steered experiments involving the Millstone Observatory and EISCAT incoherent scatter radars will demonstrate the ability of targeted data assimilation to improve the quality and value of measurements for space weather prediction. The proposed research is relevant to NOAA. The project's goal is to improve space weather prediction in the ionosphere and thermosphere. The ability to predict the effects of solar storms is needed to protect humans and technological systems. Humans in manned spaced flight, as well as in commercial aircraft, are affected by solar storms, and total radiation dosages are a growing concern. Technological systems are also affected. For example, magnetic field fluctuations induce currents in electric power lines, which can damage components of the power grid causing blackouts and substantial economic losses. The accuracy of GPS,widely used for military and commercial operations, is commonly degraded by these events. The proposed research will greatly benefit the incoherent scatter radar (ISR) community. Since ISRs require high power, they are extremely expensive to run, and thus every radar experiment must be chosen for maximal efficiency. This efficiency is difficult to achieve in practice, however, since radars work in different modes and scan different parts of the ionosphere. This project will improve ISR efficiency by allowing operators to observe what may be happening if the radar mode or look direction were changed. This methodology will fundamentally change the way radars are run, since operators will be able to make informed decisions on what science they can accomplish by changing modes. When an experiment is not yielding useful results, operators will be able examine model results that simulate alternative radar modes, and thus dynamically switch experiments. This ability will dramatically improve the efficiency of the radars.

科学家和工程师以及公众越来越意识到太阳扰动对人类和技术系统构成的威胁。为了改进空间气象预测，该项目将电离层和热层的物理模型与有针对性的测量相结合，这一过程称为有针对性的数据同化。拟议的研究包括扩展的数据同化算法，以解决非线性动力学，模型误差和计算复杂性。利用Millstone天文台和EISCAT非相干散射雷达进行的动态操纵实验将证明有针对性的数据同化能够提高空间气象预测测量的质量和价值。这项研究与NOAA有关。该项目的目标是改进电离层和热层的空间气象预测。预测太阳风暴影响的能力是保护人类和技术系统所必需的。载人航天飞行中的人类以及商用飞机都受到太阳风暴的影响，总辐射剂量越来越令人担忧。技术系统也受到影响。例如，磁场波动在电力线中感应电流，这可能损坏电网的部件，导致停电和重大经济损失。这些事件通常会降低广泛用于军事和商业活动的全球定位系统的精度。所提出的研究将大大有利于非相干散射雷达（ISR）社区。由于ISR需要高功率，因此运行成本极高，因此必须选择最大效率的雷达实验。然而，这种效率在实践中很难实现，因为雷达以不同的模式工作，扫描电离层的不同部分。该项目将通过允许操作员观察如果雷达模式或观察方向改变可能发生的情况来提高ISR效率。这种方法将从根本上改变雷达的运行方式，因为操作员将能够通过改变模式来做出明智的决定。当实验没有产生有用的结果时，操作员将能够检查模拟替代雷达模式的模型结果，从而动态切换实验。这种能力将大大提高雷达的效率。