Energy-dependent Boundaries of Earth's Radiation Belt Electron Slot Region

The variations in radiation belt boundaries reflect competition between acceleration and loss physical processes of energetic electrons, which is an important issue for radiation belts of planets with an internal magnetic field (e.g., Earth, Jupiter, and Saturn). Based on high-quality measurements from Van Allen Probes spanning the years 2014–2018, we develop an empirical model of the energy-dependent boundaries of Earth's electron radiation belt slot region, showing that the lower boundary follows a logarithmic function of the electron energy while the upper boundary is controlled by two competing energy-dependent processes, namely compression and recovery. The compression process relates linearly to a 15 hr averaged Kp index, while the recovery process is found to be approximately proportional to time. Detailed data-model comparisons demonstrate that our model, using only the Kp index and time epoch as inputs, reconstructs the slot region boundaries in real time for 200 keV to 2 MeV electrons under varying geomagnetic conditions. Such a data-driven empirical model is prerequisite to understanding the dynamic changes of the slot region in response to both solar and geomagnetic activities. The model can be readily incorporated into future global simulations of radiation belt electron dynamics in Earth's inner magnetosphere and provide new insights into the study of Saturn's and Jupiter's radiation belt variability.

Uncertainty quantification of the DTM2020 thermosphere model

Aims The semi-empirical Drag Temperature Models (DTM) calculate the Earth's upper atmosphere's temperature, density, and composition. They were applied mainly for spacecraft orbit computation. We developed an uncertainty tool that we implemented in the DTM2020 thermosphere model. The model is assessed and compared with the recently HASDM neutral density released publicly in 2020. Methods The total neutral density dataset covers all high-resolution CHAMP, GRACE, GOCE, and SWARM data spanning almost two solar cycles. We constructed the uncertainty model using statistical binning analysis and least-square fitting techniques, allowing the development of a global sigma error model to function the main variabilities driving the thermosphere state. The model is represented mathematically by a nonlinear manifold approximation in a 6-D space parameter. Results The results reveal that the altitude parameter presents the most notable error range during quiet and moderate magnetic activity ( [[EQUATION]] ). However, the most considerable uncertainty appears during severe or extreme geomagnetic activities. The comparison with density data provided by the SET HASDM database highlights some coherent features on the mechanisms occurring in the thermosphere. Moreover, it confirms the tool's relevance to provide a qualitative database of neutral densities uncertainties values deduced from the DTM2020 model.

Some statistics of Ionospheric total electron content variations at mid-latitude zones of Mongolia

This work is focused on the correlation of ionosphere total electron content (TEC) with solar and geomagnetic activities of the space weather at mid-latitude zone. In our analysis, we investigate the TEC time series obtained from dual-frequency GNSS (Global Navigation Satellite System) observations at three continuous GPS/GNSS stations HOVD (48.00N, 91.66E), CHOB (48.08N, 114.53E) and DALN (43.56N, 104.42) for 2013. The statistical analyses are performed on 15 minute averaged yearly TEC values, which reveal the semi-annual anomaly and high correlation with the activities of the Sun and the rotation of the Earth. Phase overlapping seasonal variations of TEC and Sunspot, and Solar flux (10.7) indices, and Earth rotations (LOD) and Atmospheric angular moment (AAM) are observed in our data analyses. Sudden ionospheric storm changes in TEC with geomagnetic storm induced by the extreme solar flare and 2013 events were investigated. The result shows that GPS derived TEC behaves as an indicator of these events showing sudden increase in TEC during the event.

Singular Spectrum Analysis of the Total Electron Content Changes Prior to M ≥ 6.0 Earthquakes in the Chinese Mainland During 1998–2013

Early studies have shown evidence of the seismo-ionospheric perturbations prior to large earthquakes. Due to dynamic complexity in the ionosphere, the identification of precursory ionospheric changes is quite challenging. In this study, we analyze the total electron content (TEC) in the global ionosphere map and investigate the TEC changes prior to M ≥ 6.0 earthquakes in the Chinese Mainland during 1998–2013 to identify possible seismo-ionospheric precursors. Singular spectrum analysis is applied to extract the trend and periodic variations including diurnal and semi-diurnal components, which are dominated by solar activities. The residual ΔTEC which is mainly composed of errors and possible perturbations induced by earthquakes and geomagnetic activities is further investigated, and the root-mean-square error is employed to detect anomalous changes. The F10.7 and Dst index is also used as criterion to rule out the anomalies when intense solar or geomagnetic activities occur. Our results are consistent with those of previous studies. It is confirmed that the negative anomalies are dominant 1–5 days before the earthquakes at the fixed point (35°N, 90°E) during 0600–1000 LT. The anomalies are more obvious near the epicenter area. The singular spectrum analysis method help to establish a more reliable variation background of TEC and thus may improve the identification of precursory ionospheric changes.

Analysis of first four moderate geomagnetic storms of the 2015 year

This essay investigates of first four moderate geomagnetic activities (04 January storm, 07 January storm, 17 February storm, and 24 February storm) of 2015 in the 24th solar cycle. It tries to understand these storms with the aid of the zonal geomagnetic indices. It predicts the zonal geomagnetic indices (Dst, ap, AE) of the storms by an artificial neural network model. The phenomena that occurred in January and February are discussed on the solar wind parameters (Bz, E, P, N, v, T) and the zonal geomagnetic indices obtained from NASA. In the study, after glancing at the 2015-year general appearance, binary correlations of the variables are indicated by the covariance matrix, and the hierarchical cluster of the variables are presented by the dendrogram. The artificial neural network model is governed by the physical principles in the paper. The model uses the solar wind parameters as inputs and the zonal geomagnetic indices as outputs. The causality principle forms the models by cause-effect association. Back propagation algorithm is specified as Levenberg–Marquardt (trainlm) and 35 neural numbers are utilized in the artificial neural network. The neural network model predicts the Dst, ap, and AE indices of January and February geomagnetic storms with an accuracy that deserves discussion. Estimating the geomagnetic activities may support interplanetary works.

Geomagnetic activity effects on CO2‐driven trend in the thermosphere and ionosphere: ideal model experiments with GAIA

<p>We examine impacts of geomagnetic activity on CO<sub>2</sub>-driven trend in the Ionosphere and Thermosphere (IT) using the GAIA whole atmosphere model. The model reveals three salient features. (1) Geomagnetic activities usually weakens the CO<sub>2</sub>-driven trend at a fixed altitude. Among the IT parameters analyzed, the thermosphere mass density is the most robust indicator for CO<sub>2</sub> cooling effect even with geomagnetic activity influences. (2) Geomagnetic activities can either strengthen or weaken the CO<sub>2</sub>-driven trend in hmF2 and NmF2, depending on local time and latitudes. This renders the widely used linear fitting methods invalid for removing geomagnetic effects from observations. (3) An interdependency exists between the efficiency of CO<sub>2</sub> forcing and geomagnetic forcing, with the former enhances at lower geomagnetic activity level, while the latter enhances at higher CO<sub>2</sub> concentration. This could imply that the CO<sub>2</sub>-driven trend would accelerate in periods of declining geomagnetic activity, while magnetic storms may have larger space weather impacts in the future with increasing CO<sub>2</sub>. These findings provide a preliminary model framework to understand interactions between the CO<sub>2</sub> forcing from below and the geomagnetic forcing from above.</p>