The prediction of post-sunset equatorial plasma depletions (EPDs), often called ionospheric plasma bubbles, has remained a challenge for decades. In this study, we introduce the Ionospheric Bubble Probability (IBP), an empirical model predicting the occurrence probability of EPDs derived from 9 years of CHAMP and 8.5 years of Swarm magnetic field measurements. The model predicts the occurrence probability of EPDs for a given longitude, day of year, local time and solar activity, for the altitude range 350-500 km, and low geographic latitudes of ± 45◦. IBP has been found to successfully reconstruct the distribution of EPDs as reported in previous studies from independent data. IBP has been further evaluated using one-year of partly untrained data of the Ionospheric Bubble Index (IBI). IBI is a Level 2 product of the Swarm satellite mission used for EPD identification. The relative operating characteristics (ROC) curve shows positive excursion above the no-skill line with Hanssen and Kuiper’s Discriminant (H&KSS) score of 0.66, 0.73, and 0.65 at threshold model outputs of 0.22, 0.18, and 0.18 for Swarm A, B, and C satellites, respectively. Additionally, the reliability plots show proximity to the diagonal line with a fairly decent Brier Skill Score (BSS) of 0.317, 0.320, and 0.316 for Swarm A, B, and C respectively. These tests indicate that the model performs significantly better than a no-skill forecast. The IBP model offers a compelling glimpse into the future of EPD forecasting, thus demonstrating its potential to reliably predict EPD occurrences. The IBP model is made publicly available.

The total energy transfer from the solar wind to the magnetosphere is governed by the reconnection rate at the magnetosphere edges as the IMF $B_z$ turns southward. The delayed response of the ring current to solar wind driving can account for the anomalous growth of the SYM-H under northward IMF $B_z$. The geomagnetic storm on 21-22 January 2005 is considered to be anomalous as the SYM-H index that signifies the strength of ring current, grows and has a sustained peak value lasting more than 6 hrs under northward IMF $B_z$ conditions. In this work, first the standard WINDMI model is utilized to estimate the growth and decay of various magnetospheric currents by using several solar wind-magnetopsehre coupling functions. However, it is found that the WINDMI model driven by any of these coupling functions is not fully able to explain the enhancement of SYM-H under northward IMF $B_z$. The SYM-H variations during the entire duration of the storm were only reproduced when the effects of the dense plasma sheet were included in the WINDMI model. The limitations of directly-driven models relying purely on the solar wind parameters and not accounting for the state of the magnetosphere are highlighted by this work.

Version 5 (v05) of the thermospheric wind data from the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument on the Ionospheric CONnections (ICON) mission has been recently released, which largely avoids local-time dependent artificial baseline drifts that are found in previous versions of the ICON/MIGHTI wind data. This paper describes monthly climatologies of zonal-mean winds and tides based on the v05 ICON/MIGHTI data under geomagnetically quiet conditions (Hp30 < 3o) during April 2020-March 2022. Green-line winds in the lower thermosphere (90-110 km) and red-line winds in the middle thermosphere (200-300 km) are analyzed, as these data cover both daytime and nighttime. The altitude and latitude structures of zonal-mean winds and tides are presented for each month, and the results are compared with the widely-used empirical model, Horizontal Wind Model 2014 (HWM14). The v05 wind retrieval algorithm does not involve HWM14. The ICON/MIGHTI and HWM14 results are in general agreement, providing a validation of the v05 ICON/MIGHTI data. The agreement is especially good for the zonal-mean winds. The tidal amplitudes in HWM14 are often too small compared with those from ICON/MIGHTI as well as previous studies. A more accurate description of tides in the thermosphere is key to the future improvement of HWM.

Space-based measurements of the Earth's magnetic field with a good spatiotemporal coverage are needed to understand the complex system of our surrounding geomagnetic field. High-precision magnetic field satellite missions form the backbone for sophisticated research, but they are limited in their coverage. Many satellites carry so-called platform magnetometers that are part of their attitude and orbit control systems. These can be re-calibrated by considering different behaviors of the satellite system, hence reducing their relatively high initial noise originating from their rough calibration. These platform magnetometer data obtained from non-dedicated satellite missions complement the high-precision data by additional coverage in space, time, and magnetic local times. In this work, we present an extension to our previous Machine Learning approach for the automatic in-situ calibration of platform magnetometers. We introduce a new physics-informed layer incorporating the Biot-Savart formula for dipoles that can efficiently correct artificial disturbances due to electric current-induced magnetic fields evoked by the satellite itself. We demonstrate how magnetic dipoles can be co-estimated in a neural network for the calibration of platform magnetometers and thus enhance the Machine Learning-based approach to follow known physical principles. Here we describe the derivation and assessment of re-calibrated datasets for two satellite missions, GOCE and GRACE-FO, which are made publicly available. We achieved a mean residual of about 7 nT and 4 nT for low- and mid-latitudes, respectively.

The equatorial electrojet (EEJ) is an important manifestation of ionospheric electrodynamics. Day-to-day changes of the EEJ result from E-region dynamo processes that are primarily driven by highly variable atmospheric waves propagating up from the lower and middle atmosphere. Progress has been made in our understanding that upward propagating tides are one of the major contributors to the day-to-day variability in the EEJ, however current models are limited in their ability to capture the vertical propagation of tides from the lower and middle atmosphere to the upper atmosphere due to difficulties to adequately represent many processes that influence it. In this study, we thus propose a new data-driven approach to modeling day-to-day variability by taking advantage of widely available ground-based magnetic field measurements. The new approach based on an ensemble transform adjustment method is applied to the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) lower boundary conditions (LBCs) at about 97 km altitude in order to make the model’s tidal characteristics to be more consistent with observed magnetic perturbations associated with the EEJ. In this method, TIE-GCM ensemble simulations are driven by high-latitude ionospheric convection and auroral particle precipitation patterns specified by the AMGeO and by atmospheric waves and tides based on MERRA meteorological reanalysis. As part of forward modeling, the 3D Dynamo electrodynamic module is used to calculate magnetic perturbations on the ground and at low Earth orbit altitudes. A detailed analysis of the 21-day period from March 1 to 22, 2009 has shown that the modeled EEJ with the LBCs adjusted using ground-based magnetic perturbation data improves the agreement of the model to independent magnetic field observations from CHAMP. The use of routinely available ground-based magnetometer data to constrain the TIE-GCM LBCs could provide an opportunity to investigate how day-to-day tidal variability drives equatorial electrodynamics variability.

The Gravity field and steady-state ocean circulation explorer (GOCE) is part of ESA’s Earth Explorer Program. The satellite carries magnetometers that control the activity of magnetorquers for navigation of the satellite but are not dedicated as science instruments. However, intrinsic steady states of the instruments can be corrected by alignment and calibration, and artificial perturbations, e.g., from currents, can be removed by their characterisation correlated to housekeeping data. The leftover field then shows the natural evolution and variability of the Earth’s magnetic field. This article describes the pre-processing of input data as well as calibration and characterisation steps performed on GOCE magnetic data, using a high precision magnetic field model as reference. For geomagnetic quiet times, the standard deviation of the residual is below 13 nT with a median residual of (11.7, 9.6, 10.4) nT for the three magnetic field components (x,y,z). For validation of the calibration and characterisation performance, we selected a geomagnetic storm event in March 2013. GOCE magnetic field data shows good agreement with results from a ground magnetic observation network. The GOCE mission overlaps with the dedicated magnetic field satellite mission CHAMP for a short time at the beginning of 2010, but does not overlap with the Swarm mission or any other mission flying at low altitude and carrying high-precision magnetometers. We expect calibrated GOCE magnetic field data to be useful for lithospheric modelling and filling the gap between the dedicated geomagnetic missions CHAMP and Swarm.