[1] Data assimilation techniques provide algorithms that allow for blending of incomplete and inaccurate data with physics-based dynamic models to reconstruct the electron phase space density (PSD) in the radiation belts. In this study, we perform reanalyses of the radial PSD profile using two independent data sources from the nearly equatorial CRRES Medium Electron A (MEA) observations and the polar-orbiting Akebono Radiation Monitor (RDM) measurements for a 50-day period from 18 August to 6 October 1990. We utilize the University of California, Los Angeles, One-Dimensional Versatile Electron Radiation Belt (UCLA 1-D VERB) code and a Kalman filtering approach. Comparison of the reanalyses obtained independently using the CRRES MEA and Akebono RDM measurements shows that the dynamics of the PSD can be accurately reconstructed using Kalman filtering even when available data are sparse, inaccurate, and contaminated by random errors. The reanalyses exhibit similarities in the locations and magnitudes of peaks in radial profiles of PSD and the rate and radial extent of the dropouts during storms. This study shows that when unidirectional data are not available, pitch angle averaged flux measurements can be used to infer the long-term behavior (climatology) of the radiation belts. The methodology of obtaining PSD from pitch angle averaged and unidirectional fluxes using the Tsyganenko and Stern (1996) magnetic field model is described in detail.

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We present the recent progress in upgrading a predictive model for megaelectron‐volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L‐shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42‐month MeV electron observations from Van Allen Probes. Out‐of‐sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1‐day (2‐day) forecasts of 1‐MeV electron flux distributions with performance efficiency values ~0.87 (~0.82) averaged over the L‐shell range of 2.8–6.6, significantly outperforming the previous version of PreMevE particularly at L‐shells > ~4.5. Interestingly, the linear regression model is often the most successful when compared to other models, which suggests the relationship between dynamics of trapped 1‐MeV electrons and precipitating electrons is dominated by linear components. Results also show that PreMevE 2.0 can reasonably well predict the onsets of MeV electron events in 2‐day forecasts. PreMevE 2.0 is designed to be driven by observations from longstanding space infrastructure to make high‐fidelity forecasts for MeV electrons, thus can be an invaluable space weather forecasting tool for the future.

A 66-year-old woman had fever, rash, and joint pain. Physical examination revealed multiple enlarged lymph nodes. A possibility of lymphoma was considered and FDG PET/CT was performed, which demonstrated elevated FDG activity not only in many lymph nodes but also in the spleen and liver. However, adult-onset Still disease was diagnosed, and the patient responded well to therapy.