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Solar energetic particles are one of the main sources of particle radiation seen in space. In the first part of September 2017 the most active solar period of cycle 24 produced four large X-class flares and a series of (interplanetary) coronal mass ejections, which gave rise to radiation storms seen over all energies and at the ground by neutron monitors. This paper presents comprehensive cross comparisons of in situ radiation detector data from near-Earth satellites to give an appraisal on the state of present data processing for monitors of such particles. Many of these data sets have been the target of previous cross calibrations, and this event with a hard spectrum provides the opportunity to validate these results. As a result of the excellent agreement found between these data sets and the use of neutron monitor data, this paper also presents an analytical expression for fluence spectrum for the event. Derived ionizing dose values have been computed to show that although there is a significant high-energy component, the event was not particularly concerning as regards dose effects in spacecraft electronics. Several sets of spacecraft data illustrating single event effects are presented showing a more significant impact in this regard. Such a hard event can penetrate thick shielding; human dose quantities measured inside the International Space Station and derived through modeling for aircraft altitudes are also presented. Lastly, simulation results of coronal mass ejection propagation through the heliosphere are presented along with data from Mars-orbiting spacecraft in addition to data from the Mars surface.

Data from the Deal dosimeter payload on the Rapid Pathfinder satellite provide daily maps of the radiation environment on a sphere at 1200 km altitude. Through the use of magnetic coordinates, these dosimeter maps can be projected down to lower altitudes, providing valuable information for satellite anomaly resolution for vehicles in low Earth orbit (LEO). Unfortunately, the Deal data are not widely available, and the mission has a limited lifetime. As an alternative, we present a method to estimate the Deal daily maps using belt index data from NOAA's Polar-orbiting Environmental Satellite (POES) vehicles. The method addresses only trapped radiation but could readily be supplemented with POES's own measurements of solar particle radiation reaching LEO.

[1] Indications of the space environment hazard at any point in space and time along geosynchronous orbit (GEO) can be obtained using the set of rules described in this paper. These rules are implemented using real-time Geostationary Operational Environmental Satellite particle sensor data and the magnetic index Kp. These rules should be useful for both real-time and posthoc analysis of GEO spacecraft anomalies. The hazards covered are surface charging, internal charging, single-event effects due to solar particle events, and total dose (solar arrays). The system provides a “hazard quotient,” the ratio of the instantaneous to mission-averaged likelihood of an anomaly due to each hazard, based on environmental measurements. With the exception of total dose, the hazard quotients are derived from lists of on-orbit anomalies or their proxies, and it is assumed that the probability of future anomalies will share the same functional dependence on the environment exhibited by the anomalies in the lists. Hazard quotients are potentially more valuable to satellite operators than are raw measurements, as hazard quotients directly convey the statistical relationship between the radiation environment and the likelihood of an anomaly.

[1] Following the work of Koons (2001), we examine the statistical properties of extremely high fluxes of MeV electrons in the outer zone. We extend the analysis to include a variety of timescales and energies using observations from Los Alamos monitors at geosynchronous orbit and to include outer zone fluxes observed from L ∼ 2–8 by spacecraft in highly elliptical Molniya orbits. We use the statistical formalism of the generalized extreme value distribution, which represents the probability distribution of the maximum value taken out of a sample of fixed size. By taking the maximum flux observed in many nonoverlapping intervals of several hours to several days, we can determine whether the maximum flux is likely to have a finite upper limit or an exponential or power law tail. Our analysis indicates that MeV electron fluxes over a broad range of energies, L shells, and timescales have a finite upper limit, a true worst case. However, the statistical estimate of this upper limit is inherently uncertain. We compare our upper limits to the internal charging specifications provided by Fennell et al. (2000). We discuss several possible physical explanations for the flux limits.

[1] We explore the statistical limits on the complexity of data-derived models of magnetic storm phenomena, including magnetic indices, plasmapause evolution, and outer radiation belt dynamics. Specifically, we estimate the limits on the number of free parameters justifiable by application of Occam's razor, or the rule of parsimony. These limits arise from the strong intercorrelation of geomagnetic phenomena, which decimates the effective sample size of independent observations of magnetic storm phenomena. We show that the resulting paucity of distinct magnetic storms over the history of magnetic indices and satellite observations severely limits the justifiable complexity of data-derived models. Our analysis applies to a wide variety of models with a finite number of constant free parameters but not to models with time-varying parameters nor to nearest-neighbors models.

[1] The trapped radiation specifications, such as AE-8 and AP-8, are based upon a simple and reasonable framework that happens to be insufficient to the modern demands placed upon them. This manuscript outlines an improved framework for the next generation of radiation models. While existing radiation specifications tend to provide mean fluxes on a grid in magnetic coordinates, scientific and engineering users are starting to demand capabilities that are far beyond this simple framework, such as error bars on total mission dose and real-time data assimilation. One framework that can provide these added capabilities while still remaining fairly simple is the joint probability distribution, described here. The joint distribution can describe the behavior of individual fluxes on a grid in magnetic coordinates while also describing how those fluxes vary together. This covariance is a crucial ingredient for data assimilation and for proper error bars and percentiles on composite quantities like dose or fluence to a shielded device. This manuscript describes the core math of the proposed framework, an approach to building the model from in situ observations and/or numerical simulations, how to use the model to calculate error bars and percentiles on composite quantities, how to use the model in data assimilation, and how to make a first estimate of the dependence of worst case fluences on aggregation time.