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Abstract We compared the performance of DREAM3D simulations in reproducing the long‐term radiation belt dynamics observed by Van Allen Probes over the entire year of 2017 with various boundary conditions (BCs) and model inputs. Specifically, we investigated the effects of three different outer boundary conditions, two different low‐energy boundary conditions for seed electrons, four different radial diffusion (RD) coefficients (D_LL), four hiss wave models, and two chorus wave models from the literature. Using the outer boundary condition driven by GOES data, our benchmark simulation generally well reproduces the observed radiation belt dynamics insideL* = 6, with a better model performance at lowerμthan higherμ, whereμis the first adiabatic invariant. By varying the boundary conditions and inputs, we find that: (a) The data‐driven outer boundary condition is critical to the model performance, while adding in the data‐driven seed population doesn't further improve the performance. (b) The model shows comparable performance withD_LLfrom Brautigam and Albert (2000,https://doi.org/10.1029/1999ja900344), Ozeke et al. (2014,https://doi.org/10.1002/2013ja019204), and Liu et al. (2016,https://doi.org/10.1002/2015gl067398), while withD_LLfrom Ali et al. (2016,https://doi.org/10.1002/2016ja023002) the model shows less RD compared to data. (c) The model performance is similar with data‐based hiss models, but the results show faster loss is still needed inside the plasmasphere. (d) The model performs similarly with the two different chorus models, but better capturing the electron enhancement at higherμusing the Wang et al. (2019,https://doi.org/10.1029/2018ja026183) model due to its stronger wave power, since local heating for higher energy electrons is under‐reproduced in the current model. 

We present a reconstruction of radiation belt electron fluxes using data assimilation with low-Earth-orbiting Polar Orbiting Environmental Satellites (POES) measurements mapped to near equatorial regions. Such mapping is a challenging task and the appropriate methodology should be selected. To map POES measurements, we explore two machine learning methods: multivariate linear regression (MLR) and neural network (NN). The reconstructed flux is included in data assimilation with the Versatile Electron Radiation Belts (VERB) model and compared with Van Allen Probes and GOES observations. We demonstrate that data assimilation using MLR-based mapping provides a reasonably good agreement with observations. Furthermore, the data assimilation with the flux reconstructed by NN provides better performance in comparison to the data assimilation using flux reconstructed by MLR. However, the improvement by adding data assimilation is limited when compared to the purely NN model which by itself already has a high performance of predicting electron fluxes at high altitudes. In the case an optimized machine learning model is not possible, our results suggest that data assimilation can be beneficial for reconstructing outer belt electrons by correcting errors of a machine learning based LEO-to-MEO mapping and by providing physics-based extrapolation to the parameter space portion not included in the LEO-to-MEO mapping, such as at the GEO orbit in this study. 

Abstract Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.