EarthCube Data Capabilities: Collaborative Proposal: Assimilative Mapping of Geospace Observations
EarthCube Data Capabilities: Collaborative Proposal: Assimilative Mapping of Geospace Observations
批准号:
1928358
负责人:
Brian Anderson
金额:
$21.74万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-01 至 2024-08-31
中文摘要
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英文摘要
The most dynamic electromagnetic energy and momentum exchange processes between the upper atmosphere and the magnetosphere take place in the polar regions as evidenced by the aurora. Energy deposited into the upper atmosphere as a result of these processes causes dramatic global disturbances, including global temperature and neutral mass density enhancement and plasma density changes. These near-Earth space environment disturbances can negatively impact radio communication, navigation, positioning, and satellite tracking. In response to the research community's need for tools to combine heterogeneous data from ground-based and space-based instrumentation to better specify electromagnetic energy and momentum deposition from the magnetosphere, the project will develop and deploy an open-source Python software to optimally fuse these data sets. The project will empower individual investigators by providing easy access to a powerful data analysis tool and reanalysis data products. The proposed dissemination activities are intended to promote grassroots development of an open-source and open-data collaborative cohort, to catalyze a cultural change within the geospace community necessary for it to fully benefit from opportunities of the Big Data era. The project will serve to broaden the education and training experiences of one postdoc, two graduate and several undergraduate students at the researcher's institutions by engaging them in a multi-faceted project. The proposed software and infrastructure will be used to develop graduate course material. Inspired by recent advancements in geospace observing capabilities and the opportunities of Big Data, the proposal aims (1) to develop and deploy an open-source Python software and associated web-applications for Assimilative Mapping of Geospace Observations that are interoperable with established geospace community data resources and standards, and (2) to create fully reproduceable, validated reanalysis data products that can be accessed from established data repositories to maximize the scientific return on the program investment from the National Science Foundation and other federal agencies. The capabilities of existing data assimilation and data analysis tools, developed as part of the researcher's earlier EarthCube pilot project, will be extended to take advantage of the latest development and findings in the geospace sciences. The proposed web application service and software will automate data collection, pre-processing, and quality control steps to mitigate hurdles for non-experts. The Assimilative Mapping of Geospace Observations software will provide a coherent, simultaneous and inter-hemispheric picture of magnetosphere-ionosphere coupling by optimally combining diverse geospace observational data in a manner consistent with first-principles and with rigorous consideration of the uncertainty associated with each observation. Through workshops organized in partnership with community science working groups, the researchers will engage the geospace community in the collaborative geospace system science campaigns and science-driven process of data product validation using the common, accessible, expandable data analysis tools.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.
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