The space-atmosphere interaction region is constantly affected by space weather and terrestrial weather forcing. For example, a series of X-class flares on September 4-10 in 2017, including radio-blackout-causing large flares coincided with major聽hurricanes Harvey and Irma, which caused not only damaging severe rain and winds on the ground聽but traveling ionospheric disturbances in space.聽Many of the current ionospheric modeling efforts do not account for such impacts of terrestrial weather in spite of growing evidence of their interconnectedness.聽Moreover, the quest to construct a predictive model of the thermosphere-ionosphere has so far focused on reproducing observed driver-response relationships deterministically. Such modeling approaches, however, fall short of accounting for the role of uncertainties arising from dynamical and physical nonlinearity and the effects of initial conditions and driver uncertainty in determining predictability.聽聽Current approaches also do not routinely and systematically integrate observations into modeling to reduce uncertainties in initial conditions and drivers. These are major drawbacks of the current approach as the community grapples with finding a viable pathway to predictive modeling of the space-atmosphere interaction region.
This project聽aimes to:
- Develop a new聽probabilistic modeling paradigm applicable to the forced dissipative nonlinear dynamics exhibited by the topmost layer of the atmosphere.
- Incorporate this basic knowledge into the design of聽ensemble simulations and determine the nonlinear sensitivity of the thermosphere and ionosphere鈥檚 internal dynamical and physical processes to the variability and uncertainty of terrestrial and space weather forcing as well as initial conditions.
- Assess聽observation impacts on predictability through analysis of聽data assimilation and ensemble forecasting experiments by a heuristic adaptation of classic dynamical systems theory.
The project
聽presents a paradigm shift from a deterministic to probabilistic modeling framework pivotal to the聽generation of foundational knowledge of the predictability of the whole atmosphere.聽This foundational knowledge will facilitate the optimization of observing systems and the targeting of observations to maximize data impacts, exemplifying an interdisciplinary research effort that transforms scientific understanding into tangible engineering solutions to practical problems. This聽project聽timely in directly responding to the geospace community鈥檚 need for research tools to optimally combine heterogeneous observational data from distributed arrays of small ground-based instrumentation with current and future satellite data.聽
- Funding sources: $600K聽(2019-2024) from NSF CAREER Program
- PI: Tomoko Matsuo (CU-羞羞视频)
- Collaborators: NOAA NWS
- Societal relevance: The region's geophysical conditions affect orbit determination, re-entry, descent, and landing of sub-orbital and orbital vehicles, which are highly relevant to interests of the growing commercial space transportation industry.