Review of “amt-2024-198”



Overall this is a significantly well-written article, clear and logical in its premises and in the material presented. The topic described is perfectly adherent to the purpose of AMT as it describes the improvements achieved in deriving the height of aerosol layers by including the surface in the inversion of TROPOMI measurements of the oxygen band.

In the spirit of improving its scientific impact, I list specific comments below. In general, these are minor corrections that should not cause much effort. I would appreciate more contrast and integration with the scientific findings of three relevant papers focusing on retrieval of aerosol layer height. I believe that this would strengthen not only the present paper but also help the community of interested developers and users to advisely both use the product and implement next endeavours. The three papers are Sanders et al (2013, 2015) and Kylling et al (2018).

Sanders, A. F. J. and de Haan, J. F.: Retrieval of aerosol parameters from the oxygen A band in the presence of chlorophyll fluorescence, Atmos. Meas. Tech., 6, 2725–2740, https://doi.org/10.5194/amt-6-2725-2013, 2013. 

Sanders, A. F. J., de Haan, J. F., Sneep, M., Apituley, A., Stammes, P., Vieitez, M. O., Tilstra, L. G., Tuinder, O. N. E., Koning, C. E., and Veefkind, J. P.: Evaluation of the operational Aerosol Layer Height retrieval algorithm for Sentinel-5 Precursor: application to O₂ A band observations from GOME-2A, Atmos. Meas. Tech., 8, 4947–4977, https://doi.org/10.5194/amt-8-4947-2015, 2015

The exact questions are:

1. From the contrast with the findings of Sanders et al., can the authors answer whether the surface shall eventually be always fitted?
2. From the contrast with the findings of Kylling et al (2018) can the authors make an effort and compare their knowledge of the problem at hand with the discussion points provided in Section 4 of that paper? There, other algorithms based on the fit of the oxygen A band have been compared and some open points have been left unanswered.

Specific comments

P2 L53: here it seems reasonable to add the Kylling et al paper (https://doi.org/10.5194/amt-11-2911-2018).

P3 L79: it will be interesting to understand if and why the conclusions by Sanders et al 2015 are confirmed or not by this paper.

P4 L115: please add reference to the Kylling er at (2018) paper for consistency. There also other algorithms are described using complementary techniques.

P4 Section 2.1: I am not sure that the title of this section is fully appropriate.

The first paragraph presents the most important specifications of the sensor, but the second presents the details of the algorithm. I suggest a more descriptive and appropriate title, if the authors do not want to add a separate section.

About the algorithm and its details: there is an omission in the description and that is the spectroscopy used and especially what form of line (e.g. Voigt, Gaussian, Rautian, speed-dependent and so on) was used to recreate the oxygen band. I invite the authors to provide details. I ask this because depending on the height of the tropospheric column where we place ourselves, one line shape will be more appropriate than others. I am well aware that this is beyond the scope of this paper, but for future reference it is interesting to know how the authors set up the RT calculations.

P5 L132: Can the authors justify the choice of the Henyey-Greenstein function and the value of asymmetry parametr? First, the advantage of a Mie ot T-Matrix over the H-G phase function is that they better describe aerosol particle scattering. Even more important is the interrelation to the size distribution. Size distribution, asymmetry parameter and single scattering albedo determine the backscattering efficiencies of the particles.

For clouds and a g=8.44, for instance, a H-G phase function causes a 60% deviation in backscatter when compared to a Mie phase function (Hansen 1969). For aerosols in accumulation mode, the adoption of H-G gives rise to a 12% discrepancy against Mie-based approach. (Marshall et al., 1995)

Moreover, assuming that for typical TROPOMI line-of-sights we are in the backward scattering direction, the H-G phase function would clearly understimate the signal (Fig. 1 in Seidel et al, 2010). This result is in line with the findings of Marshall et al, because the H-G implies the overestimation of the asymmetry parameter therefore understimating the aerosol signal for the same measurement at TOA.

J. Hansen, “Exact and approximate solutions for multiple scattering by cloudy and hazy planetary atmospheres,” J. Atmos. Sci. 26, 478–487 (1969)

Stephen F. Marshall, David S. Covert, and Robert J. Charlson, "Relationship between asymmetry parameter and hemispheric backscatter ratio: implications for climate forcing by aerosols," Appl. Opt. 34, 6306-6311 (1995)

Seidel, F. C., Kokhanovsky, A. A., and Schaepman, M. E.: Fast and simple model for atmospheric radiative transfer, Atmos. Meas. Tech., 3, 1129–1141, https://doi.org/10.5194/amt-3-1129-2010, 2010.

P6 L161: For the casual reader, please rephrase or shortly explain what “Pre-whitening is applied” mean.

P6 L176: “In section 3.3 the effect of different weights in the a priori error covariance matrix is described for retrievals over ocean.”

Why only over the ocean and not also for pixels over land, since the authors state these are the cases where they see a gain in accuracy (P4 L94)? I find inconsistent to show the weights for ocean pixels, which have lost accuracy in some cases, while not showing those for land pixels, which are the true improvement of this version of the algorithm.

P6,7 L187-189: “Note that the ALH does not take different aerosol types into account, but assumes weakly absorbing aerosols, because in the O 2 A-band the penetration depth is controlled by the scattering of the aerosol layer, not the absorption.”

Fair enough. But again, the scattering part in extinction needs to be correctly assumed. See my comment above about the H-G phase function and size distributions. The variety of aerosol types you analyse cannot be captured by a single aerosol model.

P7 Eq 4: Can the authors justify the choice for this definition of aerosol layer height? (see Section 2.1.1 in Kylling et al, 2018)

P13 L300: In fact, Figure 4 is even more convincing than Figure 3 in demonstrating the algorithm's improvements. The authors should also not overlook the fact that CALIOP itself is not free from errors arising from the assumption of a lidar ratio that, especially in cases of high optical thickness, does not describe properly multiple scattering (see e.g. Cuesta et al., 2009, 2015).

This consideration naturally leads me to manage an expectation of my own, which can be summarized in the following question: How does the algorithm behave as a function of the optical thickness of the aerosol layer?

I would like the authors to develop the issue and answer the question above and also this one: how does the addition of the surface in the state vector correlate with the accuracy of the aerosol layer height via the surface-AOT correlation in the oxygen band continuum (i.e., for wavelengths shorter than 758/9 nm)?

I suspect that what is gained in fitting the surface is lost in determining AOT, which parameter becomes a de facto error sink.

For the avoidance of doubt: I am not asking to validate the AOT derived from the oxygen band. I am clear about its limitations and that a multi-spectral approach is more appropriate. What I am asking is to inspect the trends between TROPOMI AOT_O2, ALH accuracy (TROPOMI - CALIOP) and goodness of the surface fit.

Cuesta, J., Marsham, J. H., Parker, D. J., and Flamant, C.: Dynamical mechanisms controlling the vertical redistribution of dust and the thermodynamic structure of the West Saharan atmospheric boundary layer during summer, Atmos. Sci. Lett., 10, 34–42, https://doi.org/10.1002/asl.207, 2009

Cuesta, J., Eremenko, M., Flamant, C., Dufour, G., Laurent, B., Bergametti, G., Höpfner, M., Orphal, J., and Zhou, D.: Three-dimensional distribution of a major desert dust outbreak over East Asia in March 2008 derived from IASI satellite observations, J. Geophys. Res.-Atmos., 120, 7099–7127, https://doi.org/10.1002/2014JD022406, 2015

Typos and style

P6 L161: “to scale the elements of the state vector elements”. Perhaps a repetition?

P6 L181: “a set of nice different cases”. Perhaps nine?

P6 L186: “CALIOP L1 data”. Spurious sentence.