Evaluation of Total Electron Content Prediction Using Three Ionosphere‐Thermosphere Models

Abstract

Prediction of ionospheric state is a critical space weather problem. We expand on our previous research of medium-range ionospheric forecasts and present new results on evaluating prediction capabilities of three physics-based ionosphere-thermosphere models (Thermosphere Ionosphere Electrodynamics General Circulation Model, TIE-GCM; Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics Model, CTIPe; and Global Ionosphere Thermosphere Model, GITM). The focus of our study is understanding how current modeling approaches may predict the global ionosphere for geomagnetic storms (as studied through 35 storms during 2000-2016). Prediction approach uses physics-based modeling without any manual model adjustment, quality control, or selection of the results. Our goal is to understand to what extent current physics-based modeling can be used in total electron content (TEC) prediction and explore uncertainties of these prediction efforts with multiday lead times. The ionosphere-thermosphere model runs are driven by actual interplanetary conditions, whether those data come from real-time measurements or predicted values themselves. These model runs were performed by the Community Coordinated Modeling Center (CCMC). Jet Propulsion Laboratory (JPL)-produced global ionospheric maps (GIMs) were used to validate model TEC estimates. We utilize the True Skill Statistic (TSS) metric for the TEC prediction evaluation, noting that this is but one metric to assess predictive skill and that complete evaluations require combinations of such metrics. The meanings of contingency table elements for the prediction performance are analyzed in the context of ionosphere modeling. Prediction success is between about 0.2 and 0.5 for weak ionospheric disturbances and decreases for strong disturbances. We evaluate the prediction of TEC decreases and increases. Our results indicate that physics-based modeling during storms shows promise in TEC prediction with multiday lead time.

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

“…The persistence model considers that the VTEC(i + t) is equal to the VTEC(i), where t takes values of 1 and 24 for the 1 h and 24 h forecasting, respectively, i.e., we assume the state of the frozen ionosphere with respect to the previous hour or previous day. Persistence forecast is the most common baseline method to measure the forecast performance in supervised machine learning, as well as, in data-driven, physics-based and traditional statistical VTEC forecasting [24,31,[48][49][50]. The models for the 1 h forecast of the low-latitude VTEC have reduced RMSE of about 60% for the test period and about 70% during the geomagnetic storm with respect to the baseline.…”

Addressing Gaps in Space Weather Operations and Understanding With Small Satellites

“…First-principles models of the coupled ionosphere-thermosphere (IT) system are the foundation of many forecasting efforts and rely on accurate driver specifications (Mannucci et al, 2016). Since the IT system is highly sensitive to driving (e.g., Siscoe & Solomon, 2006) uncertainties in energy inputs and energy budget drive large errors in modeling of IT state (Deng et al, 2013;Verkhoglyadova et al, 2017) and potentially its forecasting (Verkhoglyadova et al, 2020). Mannucci et al (2020) emphasized the need for continuously available low latency observations directly relevant to space weather.…”

Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach

“…Traditionally, empirical and physical modeling approaches, which couple ionosphere and thermosphere information, are expected to be essential tools for estimating and forecasting these upper atmospheric disturbances (Codrescu et al., 2012; Jin et al., 2012; Shinagawa, 2009; Shinagawa et al., 2021; Verkhoglyadova et al., 2020). For example, Tao et al.…”

“…Traditionally, empirical and physical modeling approaches, which couple ionosphere and thermosphere information, are expected to be essential tools for estimating and forecasting these upper atmospheric disturbances (Codrescu et al, 2012;Jin et al, 2012;Shinagawa, 2009;Shinagawa et al, 2021;Verkhoglyadova et al, 2020). For example, Tao et al (2020) shows that the Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy (GAIA) simulation system provides a global distribution of ionospheric total electron content (TEC).…”