Comments on “GNSS Radio Occultation Climatologies mapped by Machine Learning and Bayesian Interpolation”

General comment:

This paper is concerned with constructing GNSS RO-based climatologies by machine learning (ML) method, and proposes three kinds of approaches: Bayesian Interpolation (BI), a feed-forward neural network (Multilayer Perceptrons, MLPs), and the combination of BI and ML (BI &ML) where the ML is applied to BI residuals. Applications of these methods to real and simulated COSMIC-2 RO data indicate that, the maps of refractivity produced by the MLPs better match the true maps than those by BI, and BI & ML yields the best GNSS RO refractivity maps. The methods are novel and the results exhibit the potential for producing GNSS RO climatologies.

specific comments:

“BI & ML” is about learning on residuals, which is a key strategy suggested by the authors. I think it is better to provide some reviews for learning on residuals in the introduction.

technical corrections：

For the convenience of readers, Figure 3, Figure 4, Table 1 and Table 2 had better be indicated for COSMIC -2 RO data.