Convenor – Sarah Gibson
Co-convenors - Enrico Camporeale, Kyung-Suk Cho, Giuseppe Consolini, Christina Plainaki and Earle Williams
The science behind Space Weather is becoming increasingly multidisciplinary.
From solar eruptions, to solar-wind /magnetosphere/ionosphere interactions, to complex couplings of the Earth's global electrical circuit and Schumann resonances, to space-weather impacts on other planetary environments, the scientific puzzles to solve are complex and require advances in modeling. Nowadays, forecasting models range fromcompletely empirical, such as the prediction of geomagnetic indexes based on statistical regression analysis, to physics-based, for example, state-of-the-art MHD simulations of Coronal Mass Ejection propagation. The paradigm of 'grey-box modeling' lives between these two extrema: data-driven reduced models that on one hand stem froma physics description, and on the other hand rely on data analysis to fit the free parameters. This approach is highly effective for interpreting space-weather-related data. It can also be a useful tool in support of space missions throughout the solar system, as seen for example in global radiation modeling that includes the parameterization of space weather conditions in plasma- interaction scenarios. All of these modeling approaches benefit from mathematical techniques that have been typically studied in contexts outside that of space weather. This topic is thus a fertile ground for a broad range of interdisciplinary collaborations.
We encourage contributions pertaining to recent progress in the effective incorporation of data into space weather modeling and prediction at any point along the chain from sun to planets. Moreover, we welcome approaches that are less traditional in the space weather community but possess potential for significant progress in forecasting and understanding space weather, and that draw upon ""lessons learned"" or ""best practices"" fromapplications to non-space-weather problems."