EarthCube Data Capabilities: Collaborative Proposal: Assimilative Mapping of Geospace Observations

高层大气和磁层之间最活跃的电磁能量和动量交换过程发生在极地地区，极光就是明证。由于这些过程而沉积到高层大气中的能量引起剧烈的全球扰动，包括全球温度和中性物质密度的增加以及等离子体密度的变化。这些近地空间环境干扰可能对无线电通信、导航、定位和卫星跟踪产生负面影响。为了满足研究界对各种工具的需求，以便将地面和空间仪器的各种数据联合收割机结合起来，更好地说明磁层的电磁能量和动量沉积，该项目将开发和部署一个开放源码Python软件，以最佳方式融合这些数据集。 该项目将使个人调查人员能够方便地获得强大的数据分析工具和再分析数据产品。拟议的传播活动旨在促进开源和开放数据协作群体的基层发展，促进地球空间界的文化变革，使其能够充分受益于大数据时代的机遇。该项目将通过让研究机构的一名博士后、两名研究生和几名本科生参与一个多方面的项目，扩大他们的教育和培训经验。拟议的软件和基础设施将用于开发研究生课程材料。受地球空间观测能力的最新进展和大数据机遇的启发，该提案旨在（1）开发和部署一个开放源码的Python软件和相关的网络应用程序，用于地球空间观测的同化映射，与现有的地球空间社区数据资源和标准互操作，以及（2）创建完全可复制的，经过验证的再分析数据产品，可从已建立的数据存储库访问，以最大限度地提高国家科学基金会和其他联邦机构对该计划投资的科学回报。现有数据同化和数据分析工具是作为研究人员早期EarthCube试点项目的一部分开发的，其能力将得到扩展，以利用地球空间科学的最新发展和发现。拟议的网络应用服务和软件将自动化数据收集、预处理和质量控制步骤，以减少非专家的障碍。地球空间观测同化制图软件将以符合第一性原则的方式，并严格考虑到与每次观测有关的不确定性，最佳地结合各种地球空间观测数据，从而提供磁层-电离层耦合的连贯、同步和半球间图像。通过与社区科学工作组合作组织的讲习班，研究人员将利用共同的、可访问的、该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

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.