This paper develops a seasonally dependent correction to TROPOMI methane observations over the Denver/Julesburg region by applying a machine learning algorithm to TROPOMI-GOSAT differences with monthly resolution.

I found the results to be of some though limited interest because they show how a method previously developed for correcting TROPOMI data on a global scale can be customized for local application to improve the correction. But I’m puzzled by the motivating premise of this paper that the TROPOMI retrieval does not account for seasonally variable surface albedo.  I believe that it does because surface albedo is co-retrieved with methane for every spectrum. I’m also concerned about the use XCH4 as a predictor variable (which turns out to be the most important predictor) because it makes the correction to the variable depend on the variable itself – the authors expressed concern that using wind speed as predictor variable would propagate as an aliasing factor in inversions, but using XCH4 as a predictor variable seems worse in that regard. There is also a lot of chatty prose and repetition in this paper, some of which does not follow standard scientific practice of conciseness and focus.  We don’t need to hear about Mark Twain or Horace Greeley.

Specific comments:

1. Line 12, Abstract: not sure what ‘many satellite products’ means. UV/Vis retrievals indeed often assume fixed surface albedo, but that wouldn’t apply to the TROPOMI methane retrieval which calculates its own surface albedo and would thus account for seasonality, unless I'm missing something.
2. Line 66: here and below, the description of machine learning seems pretentious to me. Here it’s just being used as a non-parametric statistical fit.
3. Line 174: here and elsewhere, a driving motivation for the paper is to apply seasonality to the albedo correction from Lorente et al. 2021. I couldn’t find a description of the Lorente correction but my understanding is that it is an improved polynomial spectral representation of the albedo co-retrieved with methane.  If so it would have seasonality.
4. Line 294, Figure 5: it would be good to show the actual TROPOMI data before the correction. Data over rivers are bad and really should be removed from the dataset, but maybe they are not flagged as bad in the retrieval?

See attached PDF.

Review of “Deep Transfer Learning Method for Seasonal TROPOMI XCH4 Albedo Correction” by Bradley et al.

This paper addresses the seasonality of albedo induced biases in TROPOMI XCH4 retrievals over the Denver-Julesburg basin and attempts to remove albedo biases beyond existing correction schemes by applying machine learning models, explicitly set up for each month of the year, to XCH4 differences between TROPOMI and GOSAT. The manuscript addresses relevant scientific questions within the scope of AMT and the authors present novel ideas. I find that the paper will be improved if the comments of the two previous reviewers will be taken into account. I contribute some additional points of consideration for the revision here.

General comments:

G1: I find that the biases, which the authors are trying to correct for, appear mostly linear (see Figure 2). I am not convinced that using a machine learning correction is necessary and I fear that the introduction of such highly non-linear models will lead to overfitting. How have you ensured that you are fitting signal and not noise?

G2: I am very surprised to see such strong dependence of the XCH4 bias correction on land cover type, which is much more typically observed (and intuitively understood) in trace gas retrievals from coarse-spectral-resolution sensors like AVIRIS-NG. Please comment in the text why such hyper-local corrections might be physically plausible.

G3: The Pearson correlation coefficient, which has been chosen as a metric for the performance of different albedo corrections here, is hard to interpret (correlation between albedo and Delta_XCH4 (TROPOMI-GOSAT) is almost the same for the uncorrected data and the proposed correction in the month of December). Please explain your reasoning in more detail and provide additional metrics to measure how your new correction performs in comparison to the existing corrections by both Lorente et al. and Balasus et al..

Minor comments:

M1: Line 15-17: the 5-6 ppb correction occurs only at some times during the year. Be more specific here and in the text, for instance in Line 291-293.

M2: Line 44-46: Rephrase this sentence to make sure that readers understand that a) the performance of proxy retrievals is not generally unaffected by albedo and b) GOSAT XCH4 can also exhibit some (weak) albedo bias, but less than TROPOMI XCH4 due to a number of reasons (imaging grating vs FTS, spectral resolution and band, etc.).

M3: Line 85-86: “Satellite methods for…are going to be biased…” -> “Satellite methods for…can be biased…”

M4: Line 86-88: Rephrase, because CH4 emissions from oil and gas operations can be strongly time-dependent.

M5: Line 98-100: move this sentence to section 2.2 ?

M6: Figs. 2,3/ Lines 175-181: Fig. 2 indicates that a small bias exists only in winter (|R| > 0.1). So based on your threshold in Pearson’s correlation coefficient, no correction would be needed in summer? This appears to be supported by Fig. 3 which seems to indicate that the Lorente et al. correction works well in 6 out of 12 months.

M7: Line 198-201: Please provide more reasoning with respect to the choice of the threshold in |R| and possibly replace the reference Kuckartz et al. 2013, since this German reference may be a challenging resource for many readers (if you don’t replace it, double-check the page number).

M8: Fig. 4: Can you comment why albedo in the NIR appears to be more important than the albedo in the SWIR? This appears counter intuitive.

Technical comments:

T1: Line 55: “Kansas. (Petron et al…).” ->  “Kansas (Petron et al …).”

T2 : Line 61 : (CAFO)s -> (CAFOs)

T3 : Line 64 : Collocation -> Colocation

T4: Line 119 : Balasus et. al. (Balasus et al., 2023) -> Balasus et al. (2023)