A document by Cameron James Patterson. Click on the document to view its contents.

Assessing space weather modeling capability is a key element in improving existing models and developing new ones. In order to track improvement of the models and investigate impacts of forcing, from the lower atmosphere below and from the magnetosphere above, on the performance of ionosphere-thermosphere models, we expand our previous assessment for 2013 March storm event [Shim et al., 2018]. In this study, we evaluate new simulations from upgraded models (Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model version 4.1 and Global Ionosphere Thermosphere Model (GITM) version 21.11) and from NCAR Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X) version 2.2 including 8 simulations in the previous study. A simulation of NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model version 2 (TIE-GCM 2) is also included for comparison with WACCM-X. TEC and foF2 changes from quiet-time background are considered to evaluate the model performance on the storm impacts. For evaluation, we employ 4 skill scores: Correlation coefficient (CC), root-mean square error (RMSE), ratio of the modeled to observed maximum percentage changes (Yield), and timing error(TE). It is found that the models tend to underestimate the storm-time enhancements of foF2 (F2-layer critical frequency) and TEC (Total Electron Content) and to predict foF2 and/or TEC better in the North America but worse in the Southern Hemisphere. The ensemble simulation for TEC is comparable to results from a data assimilation model (Utah State University-Global Assimilation of Ionospheric Measurement (USU-GAIM)) with differences in skill score less than 3% and 6% for CC and RMSE, respectively.

This paper describes the development of the fluxgate magnetometer on the Fengyun-4B satellite. The Fengyun-4 is the second generation of China’s geostationary orbit satellite with the function of monitoring the space environment in GEO orbit. A fluxgate magnetometer (FGM) is deployed on this satellite to observe the magnetic field as a necessary input to space weather forecasting. This payload adopts three 3-axis fluxgate sensors to obtain space magnetic field data by excluding the satellite’s interference. Each three-axis fluxgate sensor has an independent signal processing circuit. FGM uses digital signal processing technology to acquire magnetic field signals. First, the analog signal is oversampled using a high-speed ADC, then digital signal processing, such as phase-sensitive demodulation, integration, and filtering, is performed inside the FPGA, and the feedback signal is output to the feedback coil through the DAC. This signal processing loop constitutes an ADC system, and the quantization accuracy of the output digital quantity can reach 18 bits. the FGM performs in-orbit calibration during satellite rotation maneuver and Alfven wave events. Comparison with the GOES-16 satellite and Tsyganenko magnetic field model proves FGM to be effective in monitoring the magnetic field of the space environment. Through joint observations with GOES-16 satellite and geomagnetic stations, FY-4B describes the development of a typical magnetic storm on November 4, 2021. The Ground calibration and in-orbit preliminary results show the FY-4B satellite magnetometer outputs 20 bits of digital resolution data at a 30 Hz sampling rate, with the noise lower than 3×10-4nT2/Hz@1Hz in the ±600nT range.

The solar radio flux at 10.7 cm, known as F10.7, is a critical operational space weather index. However, without a clear backup, any interruption to the service can result in substantial errors in model outputs. In this paper we show the impact of one such outage in March 2022 and present a number of alternative solutions for any future outages. The approach resulting in the smallest reconstruction error of F10.7 uses the solar radio flux observations at alternative wavelengths (the best giving a percentage error of 3.1%). Alternatively, use of Sunspot Number, a regular, robust alternative observation, results in a mean percentage error of 8.2% and is also a reliable fallback solution. Additionally, analysis of the error on the use of the conversion between the 12-month rolling sunspot number (R12) and its conversion to F10.7 as used by the IRI is included.

We directly estimate the in situ current density of the Earth’s ring current (RC) using the curlometer method and investigate its morphology using the small spatial separations and high accuracy of the Magnetospheric Multiscale mission (MMS). Through statistical analysis of data from September 2015 to the end of 2016, covering the region of 2-8 RE (Earth radius, 6371 km), we reveal an almost complete near-equatorial (within ) RC morphology in terms of radial distance and local time (MLT) which complements and extends that found from previous studies. We found no evidence of RC enhancement on the dusk-side during geomagnetic active periods, but details of local time (MLT) asymmetries in, and the boundary between, the inner (eastward) and outer (westward) currents are revealed. We propose that part of the asymmetry demonstrated here suggests that in addition to the overall persistence of the westward RC, two large banana-like currents are directly observed, one which could arise from a peak of plasma pressure near ~4.8 RE on the noon side and the other from a valley of plasma pressure which could arise near ~4.8 RE on the night side.

Reliable short-time prediction of thermospheric mass density along the satellite orbit is always essential but challenging for the operation of Low-Earth orbit (LEO) satellites. In this paper, three machine-learning prediction algorithms are investigated, including the Bidirectional Long Short-Term Memory (Bi-LSTM), the Transformer, and the Light Gradient Boosting Machine (LightGBM) ensemble model of the above models. We use satellite data from CHAMP, GOCE, and SWARM-C to evaluate the robustness and accuracy of different density variations. The comparison demonstrates that all models achieve compelling predictions and are much better than NRLMSISE-00. The LightGBM ensemble model (LE-model) consistently outperforms others in accuracy and stability. Furthermore, when the obtained density data from the newly launched satellites are limited, the trained LE-model can provide a valid prediction for the new satellite orbit by transfer learning. This study offers a promising insight into the short-time prediction of thermospheric mass density using ensemble-transfer learning and may be advantageous to future research on space whether.

The information on plasma pressures in the outer part of the inner magnetosphere is important for simulations of the inner magnetosphere and the better understanding of its dynamics. Based on 17-year observations from both CIS and RAPID instruments onboard the Cluster mission, we used machine- learning-based models to predict proton plasma pressures at energies from ~40eV to 4MeV in the outer part of the inner magnetosphere (L*=5-9). The location in the magnetosphere, and parameters of solar, solar wind, and geomagnetic activity from the OMNI database are used as predictors. We trained several different machine-learning-based models and compared their performances with observations. The results demonstrate that the Extra-Trees Regressor has the best predicting performance. The Spearman correlation between the observations and predictions by the model data is about 68%. The most important parameter for predicting proton pressures in our model is the L* value, which is related to the location. The most important predictor of solar and geomagnetic activity is the solar wind dynamic pressure. Based on the observations and predictions by our model, we find that no matter under quiet or disturbed geomagnetic conditions, both the dusk-dawn asymmetry at the dayside with higher pressures at the duskside and the day-night asymmetry with higher pressures at the nightside occur. Our results have direct practical applications, for instance, inputs for simulations of the inner magnetosphere or the reconstruction of the 3-D magnetospheric electric current system based on the magnetostatic equilibrium, and can also provide valuable guidance to the space weather forecast.

The influences of subauroral polarization streams (SAPS) on storm-enhanced density (SED) and tongue of ionization (TOI), an important topic in the field of magnetosphere-ionosphere-thermosphere coupling, however, remain undetermined. The Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) with/without an empirical SAPS model has been used to investigate the impacts of SAPS on SED and TOI. The modeled TEC and ion drift velocities agree reasonably well with the observations of GNSS and DMSP satellites on 17 March 2013. The TIEGCM simulations show that SAPS can significantly affect the electron density of SED and TOI depending on the relative location of SAPS and SED. SAPS reduces the electron density at the eastward edge of SED where they are overlapped, and enhances SED at its westward edge. A term-by-term analysis of the O+ ion continuity equation in the F-region shows that the electron density depletions at the eastward edge of SED are mainly due to increased local plasma loss rates because of SAPS elevated plasma-neutral temperatures and O/N2 reduction because of thermosphere upwelling. The electron density enhancements in the westward edge of SED are mainly due to SAPS-induced westward plasma E×B transports and O/N2 increment because of thermospheric downwelling. Moreover, SAPS-induced electron depletions in the throat region weaken TOI as plasmas undergo anti-sunward convection into the polar cap.