NOAA Space Weather Prediction Center (SWPC) is using the Geospace suite from the University of Michigan’s Space Weather Modeling Framework (SWMF, Tóth et al., 2005; Gombosi et al., 2021) to track a satellite’s orbit (actual or predicted) and calculates the predicted in situ parameters, such as plasma density, velocity, electric field, and magnetic field perturbations (δB). Currently, SWMF only provides such satellite orbit tracking in the magnetosphere and specifies δB on the ground to predict Geomagnetically Induced Currents (GICs). Calculation of δB along the LEO satellite trajectory has not been implemented between the lower boundary of the ionosphere and 1.5 R_E altitude (2.5 R_E from earth’s center, the inner boundary of the magnetosphere magnetohydrodynamic model). Recently, SWPC’s Whole-Atmosphere-Model + Ionosphere-Plasmasphere-Electrodynamics (WAM-IPE) model (e.g., Fang et al., 2018) transitioned into operations, providing a 2-day forecast of the ionosphere and thermosphere conditions every 6 h. WAM-IPE does not calculate magnetic perturbations in the ionosphere or on the ground at this point. Both the SWMF and WAM-IPE models are driven by real-time IMF and solar wind data from L1, and do not ingest any real-time in-situ magnetic field data from LEO or geosynchronous satellites within their model domains. Predictive capability (up to about 45 min) comes from ballistic propagation time between L1 and earth or the upstream model boundary (32 R_E for SWMF Geospace suite).

Predicted FAC δB along the polar orbiting LEO satellite may be compared to the measurements of magnetic field perturbations for real-time model validation to assess a model’s ability to predict FAC position and intensity in real time during space weather events. Specifications of δB at ionospheric altitudes are necessary in any future RT-coupled geospace model because adaptations to the δB calculation in the ionosphere will be needed (e.g., Egbert et al., 2021).

Ionospheric models specifying the ambient ionospheric conditions can be used to assess the conditions that favor the occurrence of ionospheric irregularities and scintillations. Certain favorable ambient ionosphere conditions need to occur, together with certain electrodynamic and thermospheric conditions, may drive the formation of ionospheric irregularities. The Department of Defense (DoD) uses the Utah State University Global Assimilation of Ionospheric Measurements (USU-GAIM) model (Schunk et al., 2004) to specify ambient ionosphere conditions, such as three-dimensional (3D) electron density (including peak electron density, NmF2, and peak value altitude, hmF2), ion density, total electron content (TEC), and critical radio frequency. The DoD is also working on the implementation of a Global Assimilation of Ionospheric Measurements Full-Physics (GAIM-FP) model, which uses a more sophisticated Ensemble Kalman filter technique together with a physics-based ionosphere-plasmasphere model including D-region physics (Scherliess et al., 2017). Currently, the GAIM models do not use any real-time in situ magnetic field observations; however, the real-time magnetic field data can potentially be incorporated into GAIM-FP to help constrain the high-latitude electromagnetic forcing that is central in assimilating ionospheric plasma density distributions.

Furthermore, future development of operational ionospheric models can incorporate real-time high-latitude electrodynamic patterns to improve the magnetospheric forcing and therefore the ambient ionosphere conditions. The Assimilative Mapping of Ionospheric Electrodynamics (AMIE) can provide this specific high latitude forcing in near-real time using magnetic field measurements from a constellation of polar orbiting LEO satellites, such as GDC, together with other real-time space and ground-based observations when available. AMIE is a widely used data assimilation algorithm specifically designed to obtain snapshots of ionospheric electrodynamic fields by synthesizing simultaneous observations from various ground- and space-based instruments (e.g., ion drift/electric field, magnetic field, particle precipitation) (Richmond and Kamide, 1988; Richmond, 1992). When constrained with adequate data, AMIE provides a more realistic specification of high latitude forcing than those from empirical models. Lu. (2017) extensively discussed the effects of different data inputs on the AMIE outputs and demonstrated that global electric potential and FACs can be contained by the magnetic field measurements from a pair of satellites on different orbital planes, that together cover a broad polar region.

AMIE can assimilate real-time magnetic field data δB, or its temporal [d(δB)/dt] or along-track spatial [d(δB)/dx] modulations over polar cap passes from 2 or more widely spaced satellites to provide real-time specifications of ionospheric electrodynamics. AMIE can update the real-time global electric potential pattern in the southern and northern high latitude regions at least 2 times every satellite orbit (∼90 min) with multiple satellites widely spaced in longitude. Since AMIE assimilates magnetic field data for the entire polar cap pass (∼15 min) to update each specification, the latency is expected to be ∼20 min from data acquisition.

DoD forecasts neutral density as part of its operation to produce the North American Aerospace Defense (NORAD) satellite catalog in support of the command and control of space forces. The US Space Force 18th Space Defense Squadron (18 SDS) runs the High Accuracy Satellite Drag Model (HASDM) to predict thermospheric density up to the next 72 h (Storz et al., 2005). HASDM incorporates the Jacchia-Bowman 2008 (JB 2008) Empirical Thermospheric Density Model (Bowman et al., 2008) as a background density model. As demonstrated in Bowman et al. (2008), the estimate of storm-time atmospheric drag effects on LEO space objects can be greatly improved using the Dst index as a driver for the JB2008 model. JB2008 uses Dst during geomagnetic storms if the minimum Dst < −75 nT. Currently, forecasted Dst is used in JB2008 based on solar wind kinetic energy and location observables (Tobiska et al., 2013). This Dst forecast could be augmented using near-RT space based magnetic observations.

Predicting atmospheric drag for collision avoidance is a serious and ongoing concern for all space-faring nations, which underscores the importance of a real-time proxy for the Dst index. Several studies have demonstrated the feasibility of using space-based magnetic field measurements from LEO satellites to extract a RT proxy for the provisional Dst index. Le and Russell (1998) showed that δB (the difference between the measured and model field strength) over the magnetic pole is a good proxy for the Dst index near the perigee of the Polar spacecraft (∼5,000 km altitude). Burke et al. (2011) showed that a provisional Dst can be extracted from δB_Z (the perturbation in the magnetic northward component) at the magnetic equator from the DMSP satellite at ∼840 km. Le et al. (2011) demonstrated the feasibility of extracting a timely proxy for the provisional Dst index using δB_Z from the low-inclination C/NOFS satellite (∼400–867 km). Most recently, Papadimitriou et al. (2021) presented three major geomagnetic indices, Dst, ap (or Kp) and, AE, based on Swarm data with high degree of accuracy using post-processed science grade magnetic field data. Cianchini et al. (2022) applied deep learning technique to estimate the Dst index quickly directly from Swarm magnetic field data.

The earth’s internal magnetic field and the magnetic field generated by the ring current are aligned with the earth’s magnetic axis at the magnetic equator and magnetic pole, where δB_Z and δB are essentially the same. But using δB for the Dst proxy is more advantageous in real time because it is not affected by data-model directional misalignment due to potential errors in real-time attitude data. The error in the attitude data has little effect on the field strength, B = √(B_X ²+B_Y ²+B_Z ²), in contrast to the magnetic field components.

For a polar orbiting LEO satellite, the magnetic field measurements can be used to extract the real-time proxy for Dst at least twice per orbit near the magnetic poles (every ∼45 min). Due to local time asymmetry of the ring current, especially during storms, δB or δB_Z data at the magnetic equator should be averaged over a wide range of local times from multiple spacecraft to obtain a better proxy for Dst. Thus, the real-time proxy for the Dst index is available at a ∼45 min cadence from the measurements at the magnetic poles from a single polar orbiting satellite, and more frequently from multiple satellites when the measurements at the magnetic equator are added. For an equatorial satellite, magnetic measurements from a single satellite can specify the ring current’s temporal evolution, quantify its local time asymmetry and extract a timely proxy for the provisional Dst index at high cadence, as demonstrated in Le et al. (2011) using C/NOFS data.

Recent work by Lukianova (2020) has demonstrated that the equatorward boundary of large-scale FACs derived from the Swarm satellite constellation may contain “early warning” information about storm time energy deposition. Though energy is entering into the magnetosphere-ionosphere system during the initial phase, Dst is often positive or weakly negative due to magnetopause compression and enhancement of magnetopause current. When using Dst as a proxy for energy input, this initial energy is often not captured. Lukianova (2020) shows that the FAC equatorward boundary moves to lower latitude in the initial phase, and thus could provide a timely indicator of energy build-up prior to the storm main phase. Along the orbit of a polar orbiting satellite, the time rate of change of the magnetic field perturbation, d(δB)/dt, or the along-track spatial gradient, d(δB)/dx, can be used to identify the instantaneous FAC boundaries, as it would exhibit a sudden change across such boundaries. The d(δB)/dt or d(δB)/dx data can be used in real-time to monitor the FAC boundaries and when the equatorward FAC boundary moves to lower latitude (by comparing with the previous boundary crossing) to provide an early warning of a magnetic storm before it can be detected in the Dst index. A polar orbiting satellite crosses the equatorward boundary of the FAC region every ∼20–25 min (4 times per orbit), at a cadence adequate for monitoring storm evolution.

USGS is responsible for developing capabilities for real-time mapping of geomagnetic-field disturbances and magnetic-storm-induced geoelectric fields, which is of particular importance for evaluating the vulnerability of electric-power-grid systems (Love et al., 2020). Signals of ultra-low-frequency (ULF, f < 1 Hz) geomagnetic pulsations are often considered as a source of noise in some geophysical analysis techniques, such as aeromagnetic surveys and transient electromagnetics. USGS is exploring the feasibility of developing near real-time space weather products such as pulsation maps to monitor these geomagnetic pulsations, as a part of the Geomagnetic Hazard Map project at the USGS Geomagnetism Program (Xu et al., 2013). Due to the limitation of the spatial resolution of USGS ground stations, the satellite magnetic field spectral power in ULF frequency bands can be used in real time to aid the interpretation of these maps and flag reliability of geomagnetic measurements during conjunctions between the USGS magnetometers and the satellite.