Machine learning-based retrieval of thermodynamic profiles from GNSS-RO observations: Preliminary results

Radio Occultation (RO) using signals from Global Navigation Satellite Systems (GNSS) is one of the most promising remote sensing techniques for global atmospheric sounding. RO is a limb-sounding technique that uses GNSS signals, refracted during their propagation through the Earth’s atmosphere to a receiver on a low-Earth orbit (LEO) satellite. Over the last decades, RO products have been extensively used for data assimilation in Numerical Weather Prediction (NWP) as well as in climate science.

The RO retrieval of atmospheric profiles is based on accurately measuring phase deviations, which are induced by atmospheric bending of the signal. Over the past two decades, several improvements of the retrieval process have been achieved, but significant challenges remain, including the dependency of certain retrieval steps on external information or the assumption of spherical symmetry.

On the other hand, several RO missions such as the FORMOSAT-3/Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and its successor COSMIC-2 have been initiated over the last two decades. In addition, several commercial companies have launched their own RO payloads, which led to an enormous increase in data amounts in recent years. These large data amounts make it suitable for the application of machine learning (ML) models, which have not been used much by the RO community until now. Only few studies have tested the suitability of ML for replacing classic retrieval algorithms and despite already achieving promising results, they were not able to uncover the full potential of ML, mostly due to the small amounts of data used.

This study presents an initial assessment of the performance of ML-based RO retrievals of temperature, pressure and humidity, trained using RO data from e.g. COSMIC-2 and state-of-the-art reanalysis products such as ERA5. It explores the suitability of various experimental setups and evaluates the sensitivity of the results to different feature setups.