PathwayThe 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 hurricanes Harvey and Irma, which caused not only damaging severe rain and winds on the ground but traveling ionospheric disturbances in space. Many of the current ionospheric modeling efforts do not account for such impacts of terrestrial weather in spite of growing evidence of their interconnectedness. 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.  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 aimes to:

  1. Develop a new probabilistic modeling paradigm applicable to the forced dissipative nonlinear dynamics exhibited by the topmost layer of the atmosphere.
  2. Incorporate this basic knowledge into the design of ensemble simulations and determine the nonlinear sensitivity of the thermosphere and ionosphere’s internal dynamical and physical processes to the variability and uncertainty of terrestrial and space weather forcing as well as initial conditions.
  3. Assess observation impacts on predictability through analysis of data assimilation and ensemble forecasting experiments by a heuristic adaptation of classic dynamical systems theory.

The projectGeospaceData presents a paradigm shift from a deterministic to probabilistic modeling framework pivotal to the generation of foundational knowledge of the predictability of the whole atmosphere. 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 project timely in directly responding to the geospace community’s need for research tools to optimally combine heterogeneous observational data from distributed arrays of small ground-based instrumentation with current and future satellite data. 

  • Funding sources: $600K (2019-2024) from NSF CAREER Program
  • PI: Tomoko Matsuo (CU-Boulder)
  • 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.