In-situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near-Earth space and beyond. However, on-orbit telemetry constraints can severely limit the capability of spacecraft to transmit high-cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission’s science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist-In-The-Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high-cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low-cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One-Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data’s potential for scientific utility. The technique’s performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e-POP/Swarm-Echo and MMS missions.

Understanding extreme space weather events is of paramount importance in efforts to protect technological systems in space and on the ground. Particularly in the thermosphere, the subsequent extreme magnetic storms can pose serious threats to low-Earth orbit (LEO) spacecraft by intensifying errors in orbit predictions. Extreme magnetic storms (minimum Dst $\leq$ –250 nT) are extremely rare: only 7 events occurred during the era of spacecraft with high-level accelerometers such as CHAMP (CHAllenge Mini-satellite Payload) and GRACE (Gravity Recovery And Climate experiment), and none with minimum Dst $\leq$ –500 nT, here termed magnetic superstorms. Therefore, current knowledge of thermospheric mass density response to superstorms is very limited. Thus, in order to advance this knowledge, four known magnetic superstorms in history, i.e., events occurring before CHAMP’s and GRACE’s commission times, with complete datasets, are used to empirically estimate density enhancements and subsequent orbital drag. The November 2003 magnetic storm (minimum Dst = –422 nT), the most extreme event observed by both satellites, is used as the benchmark event. Results show that, as expected, orbital degradation is more severe for the most intense storms. Additionally, results clearly point out that the time duration of the storm is strongly associated with storm-time orbital drag effects, being as important as or even more important than storm intensity itself. The most extreme storm-time decays during CHAMP/GRACE-like sample satellite orbits estimated for the March 1989 magnetic superstorm show that long-lasting superstorms can have highly detrimental consequences for the orbital dynamics of satellites in LEO.

The accurate determination of the Field Line Resonance (FLR) frequency of a resonating geomagnetic field line is necessary to remotely monitor the plasmaspheric mass density during geomagnetic storms and quiet times alike. Under certain assumptions the plasmaspheric mass density at the equator is inversely proportional to the square of the FLR frequency. The most common techniques to determine the FLR frequency from ground magnetometer measurements are the amplitude ratio and phase difference techniques, both based on geomagnetic field observations at two latitudinally separated ground stations along the same magnetic meridian. Previously developed automated techniques have used statistical methods to pinpoint the FLR frequency using the amplitude ratio and phase difference calculations. We now introduce a physics-based automated technique, using non-linear least square fitting of the ground magnetometer data to the analytical resonant wave equations, that reproduces the wave characteristics on the ground, and from those determine the FLR frequency. One of the advantages of the new technique is the estimation of physics-based errors of the FLR frequency, and as a result of the equatorial plasmaspheric mass density. We present analytical results of the new technique, and test it using data from the Inner-Magnetospheric Array for Geospace Science (iMAGS) ground magnetometer chain along the coast of Chile and the east coast of the United States. We compare the results with the results of previously published statistical automated techniques.