CAREER: Predictability of the Whole Atmosphere from Ground to Geospace

这项CAREER研究的重点是研究是否可以通过在整个大气和电离层的第一性原理模型的工作中包含地球和地球空间观测来实现从地面到近地空间的大气的准确可预测性。这种方法基于这样一个原则，即预测是对地球物理系统科学理解的最终检验。实现对近地空间环境条件的准确预测对于无线电通信、导航、定位和卫星跟踪方面的社会需求至关重要。对地球空间条件进行有效的数值预测将有助于在发生自然灾害时更好地保护重要的空间资产和相关系统。该奖项将支持一名全日制研究生和两名本科生。将编写有关数据同化方法的课程，其中还将涉及建模框架中作为纳入地球和地球空间传感器数据基础的部分。这项资助的研究代表了从确定性到概率建模框架的范式转变，这可能对产生关于整个大气动力学过程的可预测性的基础知识至关重要。这些知识将有助于观测系统的优化和观测的目标，以最大限度地提高数据的影响。项目策略将发展理解不确定性在从模棱两可的事实中构建意义中的作用的能力，这也是概率预测研究的中心问题。利用数据同化技术将地球物理传感器的数据吸收到综合全球环流模式框架中，将大大提高所产生的任何预报预测的准确性。因此，资助的研究将为热层和电离层的日常变化提供现实的、全球的和瞬时的概率视角，而到目前为止，这些变化一直难以评估。所利用的数据同化程序的项目成果将通过开放源码门户向社区提供。因此，该项目将有助于减轻地球空间界对新研究工具的需求，以最佳方式将来自小型地面仪器的分布式阵列的异构观测数据与当前和未来的卫星数据结合起来。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.