23 Oct 2023
 | 23 Oct 2023
Status: this preprint is currently under review for the journal AMT.

GNSS Radio Occultation Climatologies mapped by Machine Learning and Bayesian Interpolation

Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher

Abstract. Global Navigation Satellite Systems (GNSS) radio occultation (RO) is a space-based remote sensing technique that measures the bending angle of GNSS signals as they traverse the Earth's atmosphere. Profiles of the microwave index of refraction can be calculated from the bending angles. High accuracy, long-term stability, and all-weather capability make this technique attractive to meteorologists and climatologists. Meteorologists routinely assimilate RO observations into numerical weather models. RO-based climatologies, however, are complicated to construct as their sampling density is highly non-uniform and too sparse to resolve synoptic variability in the atmosphere.

In this work, we investigate the potential of machine learning (ML) to construct RO climatologies and compare the results of a ML construction with Bayesian interpolation (BI), a state-of-the-art method to generate maps of RO products. We develop a feed-forward neural network applied to COSMIC-2 RO observations and simulate data taken from the atmospheric analyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric temperature, pressure and water vapor are used to calculate microwave refractivity at 2, 3, 5, 8, 15, and 20 km geopotential height, with each level representing a different dynamical regime of the atmosphere. The simulated data are the values of microwave refractivity produced by ECMWF at the geolocations of the COSMIC-2 RO constellation, which fall equatorward of 46 latitude. The maps of refractivity produced using the neural networks better match the true maps produced by ECMWF than maps using BI. The best results are obtained when fusing BI and ML, specifically when applying ML to the post-fit residuals of BI. At the six iso-heights, we obtain post-fit residuals of 10.9, 9.1, 5.3, 1.6, 0.6 and 0.3 N-units for BI and 8.7, 6.6, 3.6, 1.1, 0.3 and 0.2 N-units for the fused BI&ML, respectively. These results are independent of season.

The BI&ML method improves the effective horizontal resolution of the posterior longitude-latitude refractivity maps. By projecting the original and the inferred maps at 2 km iso-height onto spherical harmonics, we find that the BI-only technique can resolve refractivity in the horizontal up to spherical harmonic degree 8 while BI&ML can resolve maps of refractivity using the same input data up to spherical harmonic degree 14.

Endrit Shehaj et al.

Status: open (until 03 Jan 2024)

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  • EC1: 'Comment on amt-2023-205', Peter Alexander, 23 Oct 2023 reply

Endrit Shehaj et al.

Endrit Shehaj et al.


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