The Community Coordinated Modeling Center has been leading community‐wide space science and space weather model validation projects for many years. These efforts have been broadened and extended via the newly launched International Forum for Space Weather Modeling Capabilities Assessment (https://ccmc.gsfc.nasa.gov/assessment/). Its objective is to track space weather models' progress and performance over time, a capability that is critically needed in space weather operations and different user communities in general. The Space Radiation and Plasma Effects Working Team of the aforementioned International Forum works on one of the many focused evaluation topics and deals with five different subtopics (https://ccmc.gsfc.nasa.gov/assessment/topics/radiation-all.php) and varieties of particle populations: Surface Charging from tens of eV to 50‐keV electrons and internal charging due to energetic electrons from hundreds keV to several MeVs. Single‐event effects from solar energetic particles and galactic cosmic rays (several MeV to TeV), total dose due to accumulation of doses from electrons (>100 keV) and protons (>1 MeV) in a broad energy range, and radiation effects from solar energetic particles and galactic cosmic rays at aviation altitudes. A unique aspect of the Space Radiation and Plasma Effects focus area is that it bridges the space environments, engineering, and user communities. The intent of the paper is to provide an overview of the current status and to suggest a guide for how to best validate space environment models for operational/engineering use, which includes selection of essential space environment and effect quantities and appropriate metrics.
The reliable and accurate calculation of incident particle radiation fluxes from space radiation monitor measurements, i.e. count-rates, is of great interest and importance. Radiation monitors are relatively simple and easy to implement instruments found on board multiple spacecrafts and can thus provide information about the radiation environment in various regions of space ranging from Low Earth orbit to missions in Lagrangian points and even interplanetary missions. However, the unfolding of fluxes from monitor count-rates, being an ill-posed inverse problem, is not trivial and prone to serious errors due to the inherent difficulties present in such problems. In this work we present a novel unfolding method which uses tools from the fields of Artificial Intelligence and Machine Learning to achieve good unfolding of monitor measurements. The unfolding method combines a Case Based Reasoning approach with a Genetic Algorithm, which are both widely used. We benchmark the method on data from European Space Agency’s (ESA) Standard Radiation Environment Monitor (SREM) on board the INTEGRAL mission by calculating proton fluxes during Solar Energetic Particle Events and electron fluxes from measurements within the outer Radiation Belt. Extensive evaluation studies are made by comparing the unfolded proton fluxes with data from the SEPEM Reference Dataset v2.0 and the unfolded electron fluxes with data from the Van Allen Probes mission instruments Magnetic Electron Ion Spectrometer (MagEIS) and Relativistic Electron Proton Telescope (REPT).
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