Predictability of Thermosphere–Ionosphere Conditions Following an Eruptive Solar Event

First-principles models are expected to play a critical role in space weather forecasts. The suite of models available to the solar, heliospheric and geospace communities permit forecasts within the various space plasma regimes from the magnetized solar corona to Earth’s upper atmosphere. Behavior of the thermosphere-ionosphere (T-I) coupled system is known to depend critically on solar wind properties such as dynamic pressure and interplanetary magnetic field (Bz in particular). Successful T-I forecasts with lead times of a few days, following an eruptive solar event, must depend on the accuracy of solar wind forecasts, which are currently limited by various factors. However, it is not yet known how effectively first principles T-I models will forecast conditions even if the solar wind forecast is nearly perfect. To improve our understanding of such forecasts, we have submitted more than 120 multi-day simulation periods to NASA’s Community Coordinated Modeling Center, spanning three coupled T-I models. Approximately 40 T-I storms have been simulated, driven by solar wind and EUV parameters alone. We have developed metrics to assess the capability of first-principles T-I forecasts to capture positive and negative ionospheric storm phases (e.g. increases and decreases in ionospheric total electron content during the storms). Analysis across events and across models provides new information on the predictability of the T-I when driven by solar wind conditions, which invariably must depend on how the magnetosphere responds to solar wind forcing. Statistics are sufficient to evaluate how probabilistic forecasts might be constructed. We will contrast the first-principles approach with machine-learning based approaches to forecasting that are data-driven, showing an example of the latter constructed for predicting high latitude scintillation.