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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

基本信息

批准号：
1934973
负责人：
Alex Chartier
金额：
$ 39.19万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934973&HistoricalAwards=false
关键词：
Collaborative Research Super Dual Auroral

项目摘要

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.

空间天气是太阳风能量进入地球空间的结果，是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现实的关切汇聚在一起，使空间天气成为社会和国家规划者日益优先考虑的问题。该合作项目为四所美国大学组成的联合体提供支持，以继续在美国国家科学基金会大气和地球空间科学部门(AGS)内运行一套高频相干后向散射雷达。这一扩展将使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(SuperDARN)的一部分，该雷达网包括分布在南北半球从中纬度到高纬度到极地纬度的35多个雷达。这些活动的杠杆作用包括学生参与操作网络和实现所需数据产品所需的工程和科学任务。SuperDARN以制作大规模电离层等离子体循环地图而闻名，该地图在科学意义和实用价值方面可与对流层天气中的风和气压系统地图进行比较。雷达观测到磁层-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。该合同将继续执行一项持续观测计划，同时还将对位于北美的11部美国雷达进行重大整修，并增加冰岛两部新雷达的运行，这两部雷达是在独立支持的基础上建造的。它还将利用部分资金和现有数据，使美国超级DARN科学家能够就诸如内磁层对流、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。作为这一奖项的结果，美国研究界将获得整套SuperDARN雷达的数据，并协助SuperDARN数据和数据产品的应用。该奖项将向该社区提供新的数据产品，包括以大大提高的空间分辨率绘制电离层等离子体对流的地图，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，使数据可被发现和可互操作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。