J. Space Weather Space Clim.
Volume 6, 2016
Scientific Challenges in Thermosphere-Ionosphere Forecasting
|Number of page(s)||11|
|Published online||26 April 2016|
On forecasting ionospheric total electron content responses to high-speed solar wind streams
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
* Corresponding author: firstname.lastname@example.org
Accepted: 23 March 2016
Conditions in the ionosphere have become increasingly important to forecast, since more and more spaceborne and ground-based technological systems rely on ionospheric weather. Here we explore the feasibility of ionospheric forecasts with the current generation of physics-based models. In particular, we focus on total electron content (TEC) predictions using the Global Ionosphere-Thermosphere Model (GITM). Simulations are configured in a forecast mode and performed for four typical high-speed-stream events during 2007–2012. The simulated TECs are quantified through a metric, which divides the globe into a number of local regions and robustly differentiates between quiet and disturbed periods. Proposed forecast products are hourly global maps color-coded by the TEC disturbance level of each local region. To assess the forecasts, we compare the simulated TEC disturbances with global TEC maps derived from Global Positioning System (GPS) satellite observations. The forecast performance is found to be merely acceptable, with a large number of regions where the observed variations are not captured by the simulations. Examples of model-data agreements and disagreements are investigated in detail, aiming to understand the model behavior and improve future forecasts. For one event, we identify two adjacent regions with similar TEC observations but significant differences in how local chemistry versus plasma transport contribute to electron density changes in the simulation. Suggestions for further analysis are described.
Key words: Ionosphere (general) / Total electron content (TEC) / Modelling / Forecasting / Storm
© X. Meng et al., Published by EDP Sciences 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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