Global S&T Development Trend Analysis Platform of Resources and Environment
项目编号 | 1928327 |
EarthCube Data Capabilities: Collaborative Proposal: Assimilative Mapping of Geospace Observations | |
J. Michael Ruohoniemi (Principal Investigator) | |
主持机构 | Virginia Polytechnic Institute and State University |
项目开始年 | 2019 |
2019-09-01 | |
项目结束日期 | 2022-08-31 |
资助机构 | US-NSF |
项目类别 | Standard Grant |
项目经费 | 151561(USD) |
国家 | 美国 |
语种 | 英语 |
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. |
文献类型 | 项目 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/214120 |
专题 | 环境与发展全球科技态势 |
推荐引用方式 GB/T 7714 | J. Michael Ruohoniemi .EarthCube Data Capabilities: Collaborative Proposal: Assimilative Mapping of Geospace Observations.2019. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论