The Van Allen Probes mission operations materialized through a distributed model in which operational responsibility was divided between the Mission Operations Center (MOC) and separate instrument specific SOCs. The sole MOC handled all aspects of telemetering and receiving tasks as well as certain scientifically relevant ancillary tasks. Each instrument science team developed individual instrument specific SOCs proficient in unique capabilities in support of science data acquisition, data processing, instrument performance, and tools for the instrument team scientists. In parallel activities, project scientists took on the task of providing a significant modeling tool base usable by the instrument science teams and the larger scientific community. With a mission as complex as Van Allen Probes, scientific inquiry occurred due to constant and significant collaboration between the SOCs and in concert with the project science team. Planned cross-instrument coordinated observations resulted in critical discoveries during the seven-year mission. Instrument cross-calibration activities elucidated a more seamless set of data products. Specific topics include post-launch changes and enhancements to the SOCs, discussion of coordination activities between the SOCs, SOC specific analysis software, modeling software provided by the Van Allen Probes project, and a section on lessons learned. One of the most significant lessons learned was the importance of the original decision to implement individual team SOCs providing timely and well-documented instrument data for the NASA Van Allen Probes Mission scientists and the larger magnetospheric and radiation belt scientific community.

An empirical model of radiation belt relativistic electrons (μ = 560–875 MeV G−1 and I = 0.088–0.14 RE G0.5) with average energy ∼1.3 MeV is developed. The model inputs solar wind parameters (velocity, density, interplanetary magnetic field (IMF) |B|, Bz, and By), magnetospheric state parameters (SYM‐H and AL), and L*. The model outputs the radiation belt electron phase space density (PSD). The model is operational from L* = 3 to 6.5. The model is constructed with neural networks assisted by information theory. Information theory is used to select the most effective and relevant solar wind and magnetospheric input parameters plus their lag times based on their information transfer to the PSD. Based on the test set, the model prediction efficiency (PE) increases with increasing L*, ranging from −0.043 at L* = 3 to 0.76 at L* = 6.5. The model PE is near 0 at L* = 3–4 because at this L* range, the solar wind and magnetospheric parameters transfer little information to the PSD. Using solar wind observations at L1 and magnetospheric index (AL and SYM‐H) models solely driven by solar wind, the radiation belt model can be used to forecast PSD 30–60 min ahead. This baseline model can potentially complement a class of empirical models that input data from low earth orbit (LEO).

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The Van Allen Probes (VAPs) are the only modern National Aeronautics and Space Administration (NASA) spacecraft broadcasting real‐time data on the Earth's radiation belts for space weather operations. Since 2012, the Korea Astronomy and Space Science Institute (KASI) has contributed to the receipt of these data via a 7 m satellite‐tracking antenna and used these beacon data for space weather operations. An approximately 15 min period is required from measurement to acquisition of Level‐1 data. In this paper, we demonstrate the use of VAP data for monitoring space weather conditions at geostationary orbit (GEO) by highlighting the Saint Patrick's Day storm of 2015. During that storm, Probe‐A observed a significant increase in the relativistic electron flux at 3 RE. Those electrons diffused outward resulting in a large increase of the electron flux >2 MeV at GEO, which potentially threatened satellite operations. Based on this study, we conclude that the combination of VAP data and National Oceanic and Atmospheric Administration‐Geostationary Operational Environmental Satellite (NOAA‐GOES) data can provide improved space environment information to geostationary satellite operators. In addition, the findings obtained indicate that more data‐receiving sites would be necessary and data connections improved if this or a similar system were to be used as an operational data service.