CAREER: Predictability of the Whole Atmosphere from Ground to Geospace

GSTDTAP

项目编号	1848544
	CAREER: Predictability of the Whole Atmosphere from Ground to Geospace
	Tomoko Matsuo (Principal Investigator)
主持机构	University of Colorado at Boulder
项目开始年	2019
	2019-03-15
项目结束日期	2024-02-29
资助机构	US-NSF
项目类别	Continuing grant
项目经费	237485(USD)
国家	美国
语种	英语
英文摘要	The focus of this CAREER research is to investigate whether accurate predictability of the atmosphere, extending from the ground to near-Earth space, can be achieved through the inclusion of Earth and geospace observations within the workings of a first-principles model of the whole atmosphere and ionosphere. The approach is based upon the tenet that prediction serves as the ultimate test of the scientific understanding of geophysical systems. Achieving accurate forecasting of near-Earth space environmental conditions would be critical to society needs regarding radio communication, navigation, positioning, and satellite tracking. Effective numerical prediction of geospace conditions would allow improved protection of important space assets and related systems in the event of natural hazards. A full time graduate student and two undergraduate students would be supported in this award. Coursework concerning data assimilation methodology would be developed that would also addresses portions of the modeling framework that underlies the inclusion of data from Earth and Geospace sensors. The funded research represents a paradigm shift from a deterministic to a probabilistic modeling framework that possibly would be pivotal to the generation of foundational knowledge regarding the predictability of the whole atmosphere dynamical processes. This knowledge would facilitate optimization of observing systems and the targeting of observations to maximize data impact. The project strategies would develop the capacity to understand the role of uncertainty in constructing meaning out of ambiguous facts, which is also a central concern of probabilistic forecasting research. The ingestion of data from geophysical sensors into a comprehensive global circulation model framework using data assimilation techniques would provide significant enhancement of the accuracy of any forecast predictions generated. Thus, the funded research would provide realistic, global and instantaneous probabilistic perspectives on the day-to-day variability of the thermosphere and ionosphere, which up to now, have been difficult to assess. The project achievements in the data assimilation procedures utilized would be made accessible to the community through open source portals. Consequently, this project would help alleviate the geospace community's need for new research tools to optimally combine heterogeneous observational data from distributed arrays of small ground-based instrumentation with current and future satellite data. 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/213107
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Tomoko Matsuo .CAREER: Predictability of the Whole Atmosphere from Ground to Geospace.2019.