This work investigates the capability of machine learning models, trained with space and ground GNSS observations, to produce fields of refractivity and IWV that describe the spatiotemporal morphology of atmospheric rivers (ARs) and quantify moisture associated with them to a degree sufficient for atmospheric studies. The reconstructed fields can be used to monitor ARs. It studies what LEO radio occultation (RO) constellation is appropriate to quantify the structure, location and timing of ARs.

Uncertainty estimation for GNSS radio occultation (RO) soundings in the planetary boundary layer (PBL) depends on the algorithms used to process the RO data. We compare the refractivity retrievals from three RO processing centers — each with their own retrieval algorithm — in the PBL, finding a strong underestimation of refractivity in regions with the strongest refractivity gradients, especially in JPL processing, as well as areas of weak overestimation of refractivity near the Poles.

This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.

AERO and VISTA spacecraft will use commercial magnetometers to measure space weather events near Earth’s aurora. The small size of AERO and VISTA necessitate the use of magnetometers with small size, weight, and power. The magnetometers selected exhibit good precision, but additional calibration is needed to achieve good accuracy. This work evaluates a method for calibration by regression which has reduced the magnetic observed error by a factor of ca. 50, meeting mission requirements.