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Magnetometers deployed on the largest satellite constellation to date are leveraged as a space‐based sensor network to study space‐time variability in auroral field‐aligned currents (FACs). The cubesat constellation of Planet Labs Inc. consists of nearly 200 satellites in two polar Sun‐synchronous orbits, with median spacecraft separations on the order of 375 km, and some occasions of opportunity providing much closer spacing. Each spacecraft contains a magnetoinductive magnetometer, able to sample the ambient magnetic field at 0.1 to 10 Hz with <200‐nT sensitivity. In this study, seven satellites from the Planet constellation were used to investigate space‐time variations in FACs over an active auroral display during a 10‐min interval. The aurora occurred during the early recovery phase of a geomagnetic storm and was characterized by large‐scale vortical motions and embedded rayed structure. Clear signatures of the large‐scale auroral current system were detected by the orbital magnetometers. Estimation of FAC patterns was carried out using three different methods. The results suggest a high degree of spatial and temporal variability during the 10‐min interval. The location of upward and downward current channels relative to the aurora was consistent with theoretical expectations, but current densities were not well correlated with visible features in the available imagery, suggesting unresolved small‐scale structure not captured by the collaborative observations. Advantages, limitations, and caveats in using opportunistic networks of low‐quality space‐based magnetometers to study dynamic auroral phenomena are discussed.

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When calculating satellite trajectories in low-earth orbit, engineers need to adequately estimate aerodynamic forces. But to this day, obtaining the drag acting on the complicated shapes of modern spacecraft suffers from many sources of error. While part of the problem is the uncertain density in the upper atmosphere, this works focuses on improving the modeling of interacting rarified gases and satellite surfaces. The only numerical approach that currently captures effects in this flow regime-like self-shadowing and multiple molecular reflections-is known as test-particle Monte Carlo. This method executes a ray-tracing algorithm to follow particles that pass through a control volume containing the spacecraft and accumulates the momentum transfer to the body surfaces. Statistical fluctuations inherent in the approach demand particle numbers in the order of millions, often making this scheme too costly to be practical. This work presents a parallel test-particle Monte Carlo method that takes advantage of both GPUs and multi-core CPUs. The speed at which this model can run with millions of particles allowed exploring a regime where a flaw in the models initial particle seeding was revealed. Our new model introduces an analytical fix based on seeding the calculation with an initial distribution of particles at the boundary of a spherical control volume and computing the integral for the correct number flux. This work includes verification of the proposed model using analytical solutions for several simple geometries and demonstrates uses for studying aero-stabilization of the Phobos-Grunt Martian probe and pose-estimation for the ICESat mission. Nomenclature k B = Boltzmann constant, J/K T = Temperature, K m = Molecular mass of gas particles, kg A ref = Reference Area, m 2 n = Number density, #/m 3 ρ = mass density (nm), kg/m 3 C D = Drag coefficient, (Drag Force)/ 1 2 ρV 2 A ref Γ = Number flux through surface c mp = Most probable thermal speed, 2k B T ∞ /m S = Speed ratio, V /c mp Subscripts ∞ = Free stream equilibrium conditions W = Properties evaluated at the satellite surface