Fall Meeting 2022

December 12 - 16, 2022

A recent moderate geomagnetic storm on February 3-4, 2022, has caused the loss of about 40 out of 49 SpaceX Starlink low-orbit satellites, highlighting the importance of advancing the predictive capability of Magnetosphere, Ionosphere and Thermosphere (MIT) system especially under disturbed conditions. The availability of long-term Heliophysics System Observatory datasets and rapidly growing data science techniques has enabled researchers to develop various data-driven modeling approaches that utilize data-assimilation and machine learning techniques. The ongoing challenge is to enhance the data set with new measurements, especially in the thermosphere, and model temperatures, winds, and densities in the thermosphere on a global scale. We must look for new ways to use existing data and/or identify new observations for this purpose. We solicit papers on new measurements, and new applications of data-driven modeling approaches to address MIT-coupling science questions, including papers that demonstrate how data assimilation and machine learning techniques can be used to deepen our understanding of MIT-coupling and enable a better storm-time prediction. Papers that focus on forecasting of the solar wind and magnetospheric conditions that lead to the improved thermosphere and ionosphere’s space weather effects, such as satellite drag and geomagnetically-induced currents, are also solicited.