Collaborative Research: Super Dual Auroral Radar Network (SuperDARN) Operations, Research and Community Support

合作研究：超级双极光雷达网络 (SuperDARN) 运营、研究和社区支持

• 批准号: 2125323
• 负责人: William Bristow
• 金额: $ 211.73万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125323&HistoricalAwards=false
• 关键词: Collaborative; Research; Super; Dual; Auroral

Space weather is the result of the coupling of energy from the solar wind into geospace and the myriad interactions between electric fields and charged particles with the geomagnetic field and neutral atmosphere. The resulting variability and disturbed conditions leads to harmful impacts on technological systems and even to risk to humans. Scientific imperatives and practical concerns converge in making space weather a rising priority for society and national planners. This collaborative project provides support for a consortium of four U.S. universities to continue the operations of a suite of High Frequency coherent-backscatter radars within the NSF Division of Atmospheric and Geospace Sciences (AGS). This extension will enable a broad range of research across the space science community. The U.S. consortium functions as part of the international Super Dual Auroral Radar Network (SuperDARN) which encompasses more than 35 radars distributed from mid through high to polar latitudes in both the northern and southern hemispheres. The leveraged benefits of the activities include student participation in the engineering and scientific tasks required to operate the network and to achieve the desired data products.SuperDARN is well known for producing maps of the large-scale circulation of ionospheric plasma that can be compared in terms of scientific significance and practical utility to maps of winds and pressure systems in tropospheric weather. The radars observe a wide range of effects in the coupled magnetosphere-ionosphere thermosphere system that bear directly on understanding and modeling space weather events. The award will continue a program of continued observations while also achieving significant refurbishment of eleven U.S. radars located in North America and add the operations of two new radars in Iceland that are being built on separate support. It will also enable, with partial funding and by virtue of the availability of data, a broad range of research by the U.S. SuperDARN scientists on such new themes as convection in the inner magnetosphere, subauroral ionospheric convection, and solar flare impacts on the ionosphere. As a consequence of this award, the U.S. research community will be supplied with data from the entire set of SuperDARN radars and assisted in the application of SuperDARN data and data products. The award will provide new data products to the community include mapping of ionospheric plasma convection at much improved spatial resolution and techniques for applying machine learning to geospace datasets. In coordination with international collaborators simplified methods for data access will be developed that meet the emerging requirements by agencies and journals to make data discoverable and interoperable.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

空间天气是太阳风的能量耦合到地球空间以及电场和带电粒子与地磁场和中性大气之间无数相互作用的结果。由此产生的可变性和扰动条件导致对技术系统的有害影响，甚至给人类带来风险。科学上的迫切需要和实际上的关切汇合在一起，使空间气象成为社会和国家规划者日益重视的一个优先事项。该合作项目为美国四所大学组成的联盟提供支持，以继续在NSF大气和地球空间科学部（AGS）内运行一套高频相干后向散射雷达。这一扩展将使整个空间科学界能够进行广泛的研究。美国联合体是国际超级双极光雷达网的一部分，该雷达网包括分布在北方和南半球中高纬度到极地的35个以上的雷达。这些活动的杠杆效益包括学生参与操作网络和获得所需数据产品所需的工程和科学任务，超级DARN以绘制大规模电离层等离子体环流图而闻名，这些图在科学意义和实用性方面可与对流层天气中的风和压力系统图相媲美。这些雷达观察磁层-电离层热层耦合系统中的广泛影响，这些影响直接关系到对空间天气事件的理解和建模。该合同将继续实施持续观测计划，同时还将对位于北美的11台美国雷达进行重大翻新，并增加位于冰岛的两台新雷达的运行，这两台雷达正在单独支持下建造。它还将利用部分资金和数据的可用性，使美国SuperDARN科学家能够就内磁层对流、极光下电离层对流和太阳耀斑对电离层的影响等新主题进行广泛的研究。作为该合同的结果，美国研究界将获得整套SuperDARN雷达的数据，并协助应用SuperDARN数据和数据产品。该奖项将为社区提供新的数据产品，包括以更高的空间分辨率绘制电离层等离子体对流图，以及将机器学习应用于地球空间数据集的技术。在与国际合作者的协调下，将开发简化的数据访问方法，以满足机构和期刊的新要求，使数据可共享和可互操作。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。