文章基本信息

标题：Thermosphere-Ionosphere Modeling With Forecastable Inputs: Case Study of the June 2012 High-Speed Stream Geomagnetic Storm
本地全文：下载
作者：Xing Meng ; Anthony J. Mannucci ; Olga P. Verkhoglyadova 等
期刊名称：Space Weather
印刷版ISSN：1542-7390
出版年度：2020
卷号：18
期号：2
页码：1-20
DOI：10.1029/2019SW002352
语种：English
出版社：American Geophysical Union
摘要：Forecasting conditions in the thermosphere and ionosphere is a key outcome expected from space weather research. In this work, we perform numerical simulations using the first-principles models Global Ionosphere-Thermosphere Model (GITM) and Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM) to address the reliability of thermospheric-ionospheric forecasts. When considering forecasts applicable to periods of geomagnetic activity, careful consideration is required of model inputs, which largely determine how the models will simulate disturbed conditions. We adopt an approach to drive the models with solar wind parameters and the 10.7 cm solar radio flux. This aligns our investigation with recent research and operational activities to forecast solar wind conditions on the Earth a few days in advance. In this work, we examine a weak geomagnetic storm, the June 2012 high-speed-stream event, for which we drive GITM and TIE-GCM with observed solar wind and F10.7 values. We find general agreement between the simulations and observation-based Global Ionospheric Maps of the total electron content (TEC) response. However, overestimated TEC response is found in the middle to low latitudinal region of the American sector and surrounding areas for both GITM and TIE-GCM during similar time periods. By conducting numerical modeling experiments and comparing the modeling results with observational data, we find that the overestimated TEC response can be almost equally attributed to the solar wind driving and F10.7 driving during the June 2012 event. We conclude that the accuracy of the high-latitude electric field and the solar irradiance is crucial to reproduce the TEC response in forecastable-mode modeling.