This work describes the features of a simple, fast, accurate, and physically based spectral radiative transfer model (SSolar-GOA) in the solar wavelength range under clear skies. The model is intended for a wide community of users for many different applications, was designed to be easily replicated, and has sufficient accuracy. The validation of the model was carried out through extensive comparison with simulated spectra from the LibRadtran and with direct and global spectral measurements.

A new full 3-D time-dependent model, based on SOCOL-AERv2, of beryllium atmospheric production, transport, and deposition has been developed and validated using directly measured data. The model is recommended to be used in studies related to, e.g., atmospheric dynamical patterns, extreme solar particle storms, long-term solar activity reconstruction from cosmogenic proxy data, and solar–terrestrial relations.

Earth’s Van Allen belts are studied by solving particular kinds of equations that could be notoriously difficult when different physical processes are acting together. In this article, we describe a numerical code that can solve these equations with unprecedented freedom from the numerous restrictions of existing models, even the ones that no other can solve. The abilities of our code could mean a breakthrough in Van Allen belt studies from the diffusive into the non-diffusive transport regime.

This paper describes a numerical treatment (ring average) to relax the time step in finite-difference schemes when using spherical and cylindrical coordinates with axis singularities. The ring average is used to develop a high-resolution thermosphere–ionosphere coupled community model. The technique is a significant improvement in space weather modeling capability, and it can also be adapted to more general finite-difference solvers for hyperbolic equations in spherical and polar geometries.

This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. This paper shows that use of MMEs for forecasting thermospheric densities can reduce errors by 60 %.