The variations of high-latitude thermosphere and ionosphere mesoscale structures during the March 2013 St. Patrick’s Day major geomagnetic storm

Wednesday, 13 February 2019
Fountain III/IV (Westin Pasadena)
Wenbin Wang1, Jing Liu2, Alan Geoffrey Burns1 and Liying Qian3, (1)NCAR, Boulder, CO, United States, (2)National Center for Atmospheric Research, High Altitude Observatory, Boulder, CO, United States, (3)NCAR High Altitude Observatory, Boulder, CO, United States
Abstract:
The thermosphere and ionosphere (T-I), as a coupled nonlinear system, respond significantly to the large amount of energy and momentum that are deposited from the magnetosphere into the T-I system at high latitudes during geomagnetic storms. These storm-time changes in neutral temperature, winds and composition thus occur first at high latitudes, and are then transmitted to middle and low latitudes through dynamics, chemistry and electrodynamical processes. These changes in the thermosphere have significant impacts on the storm-time behavior of the ionosphere through chemical and transport processes, and vice versa. Thus it is of fundamental importance to describe and understand the temporal and spatial variations of the thermosphere and ionosphere at high latitudes during geomagnetic storms not just for understanding the behavior of the T-I system, but also for predicting space weather for applications. In this presentation, we use the a magnetosphere thermosphere ionosphere model (LTR) that couples the LFM global magnetosphere MHD code, the RCM inner magnetosphere model, and the TIEGCM global thermosphere ionosphere model to investigate the storm-time changes in the thermosphere and ionosphere at high latitudes, focusing on understanding the generation, development and dissipation of mesoscale structures, such as the polar tongue of ionization (TOI), patches, subauroral polarization steams (SAPS), and their interaction with the global, large-scale structures in the T-I system during the March 2015 St. Patrick’s Day major geomagnetic storm. Further understanding is achieved by diagnostically analyzing the model outputs.