Collaborative Research: Linking Lower Atmosphere Weather to Space Weather Using Mesoscale Modeling and Artificial Intelligence

Weather occurring near Earth's surface, such as hurricanes, severe thunderstorms, and other extreme meteorological events, can produce atmospheric gravity waves that propagate upward through the atmosphere and into near-Earth space. Although numerous individual case studies have demonstrated links between terrestrial weather and ionospheric disturbances, there is still lack of a comprehensive understanding of when, where, and why weather-generated gravity waves produce detectable ionospheric effects. The project will provide the first systematic investigation of how gravity waves generated by more than 400 meteorological events evolve through the atmosphere and influence the ionosphere. As the waves reach the ionosphere, the electrically charged region of the upper atmosphere that supports satellite communications, radio transmissions, and Global Navigation Satellite Systems (GNSS), they can generate traveling ionospheric disturbances (TIDs). These disturbances can degrade the accuracy and reliability of technologies that modern society depends on for navigation, communications, and space-based services. The resulting knowledge will improve the ability to characterize upper-atmospheric variability and strengthen the scientific foundation for future forecasting capabilities, which are necessary for societal resilience. Interdisciplinary training opportunities will be provided for early career researchers, which will help develop the future skilled workforce. The overarching goal of this project is to determine the end-to-end processes through which meteorological gravity waves propagate into the ionosphere and generate TIDs. The project will result in (a) multiscale morphology and climatology of gravity waves in the lower atmosphere & mesosphere and associated TIDs in the ionosphere, (b) determination of the effects of background atmospheric conditions on gravity-wave propagation and identification of the processes that link lower atmospheric gravity waves to ionospheric disturbances, and (c) simulation of some representative events and quantification of the relative contributions of nonhydrostatic dynamics and background atmospheric variability to gravity wave propagation and TID generation. The project combines large-scale observational analyses with innovative artificial intelligence techniques, including deep-learning-based pattern recognition, to automatically identify and classify gravity-wave and TID signatures across diverse meteorological events. By combining extensive observations, advanced artificial intelligence (AI) techniques, and next-generation numerical models, the project will establish a comprehensive understanding of the physical processes that connect terrestrial weather to space weather. 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.