Review of the paper “A neural network-based method for generating synthetic 1.6 μm near-infrared satellite images” by Florian Baur et al., MS No.: egusphere-2023-353

This paper focuses on the development of a neural network to estimate the reflectance emerging from the atmosphere at 1.6 micron from SEVIRI on Meteosat Second Generation. This approach should also be suitable for other near-infrared channels on instruments such as AHI, ABI or FCI. This is a nicely written paper discusses the neural network performance when trained on reflectances calculated using DOM with both IFS and ICON-D input profiles.

I believe that this paper would be suitable for publication subject to minor revision addressing the specific comments detailed below.

Specific comments

L37: here you should explain what you mean by 3D effects, e.g. reflection on complex topography etc. A reference could also be useful.

L50: here do you mean little information on discriminating the cloud phase?

L66: It would be really interesting if you could show here an example of the water and ice cloud jacobians for the 1.6 micron channel, possibly by comparing to those for the visible (and/or thermal IR) channels.

L84: How do you assess that the statement “are not very important” is true? Please add a reference to a paper where this is discussed and/or add a few explanations.

L97: Do you mean interpolate the reflectance for the specific value of the albedo at a given location? If so you should expand your text here as you discuss this interpolation only later in the paper.

L133: How many hidden layers, and did you test the effects of having more or fewer layers? how did you initialize the weights?

L136: What distribution did you use for the random numbers? If uniform, which intervals?

L138: How many epochs were used in the training?

Fig 2 caption: Please specify units of effective particle radius

L234 (“exceeds a threshold value of 1”): Is this ok also for low optical depth clouds? From Fig 3 there are quite a few profiles with log(tau_w) ~ 0.1. Did you test having a threshold dependent on cloud optical depth categories? And did you test the radiative effects of the use of different thresholds?

L235 (“where z_sfc is the height…”): do you mean here the height of the highest model level below the bottom cloud layer?

L244 (“exceeds a threshold value…”): I guess this at least partially answers my previous question. But I don't understand if this is done also for cth or only for ctp. And did you test other values (i.e. tau_t/5 etc.)

L255: It is not clear how these parameterizations are consistent with minimum and maximum values of IWP accepted by RTTOV

L262: If I understand correctly, please replace “full vertical profiles” with “full idealised vertical profiles

Fig 12 caption (“8 hidden layers with 25 nodes”): These numbers are inconsistent with those in the figure. Please correct the caption or the figure. Also, please check the total number of weights and biases is as stated.

My comments can be found in the supplemental PDF document.