The authors describe a new method, TCWret-V2, to retrieve aerosol optical depth and composition from FTS and Raman lidar. Aerosols cannot be retrieved from the sun/sky radiometers, such as AERONET, in the nighttime and during the polar night period. The remote sensing of aerosols using FTS is very useful in the Arctic region. In addition, the aerosol composition product is unique and useful for the validation of aerosol transport models and assimilation products. The authors give only one case study for aerosols, but the reasonable results are shown by comparing the AERONET retrievals, and MERRA-2 reanalysis. This manuscript can be published after minor revision.

Specific comments

L53-54: If there are any previous studies regarding to the remote sensing of aerosols using the FTS, you should mention them.

L144-145: Sea salt and dust particles can have larger radius than 1.0 μm. Why is the maximum radius 1.0 μm?

L159: The result of sea salt in Fig. 4a is calculated using the same effective radius as the other particles. When the effective radius of the other particles is 0.70, and 1.25 μm, does the sea salt have comparable radiances with those of the other particles? The light absorbing capability of the particles could affect the radiances. Does the smallest light absorbing capability of sea salt affect the radiances?

L181: Calculation ---> Calibration

L203: Can you show the reliable range of the retrieval AOD from the results in Fig. 5?

L215: In general, AOD of BC would be lowest in the four types. The AOD of BC in the MERRA-2 reanalysis is smallest in the four particles. However, the AOD of BC in the retrieval results is larger than AOD of sea salt. The authors show that the AOD of sea salt is underestimated and the AOD of BC is overestimated in Fig. 5. Should we interpret that the AOD of sea salt is underestimated and the AOD of BC is overestimated in Fig. 7?

L217-219: The authors do not show the uncertainties of the retrieved effective radius. The effective radii of sulfate, dust, and BC are 0.3 μm in Fig. 7. Does the a priori information affect these results?

L275: Organic carbon (OC) is one of the major components in the tropospheric aerosols. The authors do not consider OC in the retrieval method, but actually OC would affect the retrieval results. What is the influence of OC?

L282-319: Matrices are printed in boldface, and vectors in boldface italics. See “submission” of AMT web page.

L310: Averaging kernels are described in only this section. Were the averaging kernels used in this study?

The author are targeting an important topic, the temporal change and retrieval of different aerosol classes in the Arctic. This is one of the topics which have to be determined and discussed in the context of the phenomenom of Arctic amplification and climate warming. They are using the information and synergy of different intrument types, present a joint observation scheme and a flow diagram of the aerosol retrieval scheme.The aerosol classes were determined for two different atmospheric conditions, a cloud case and a cloud free case. Most of the work is done and hidden behind those two schemes and this and the calculation of results and error bars could be describbed in more detail. Unfortunately the examination is limited to two single example days. A temperal change of the aerosol classes for a longer time period would be much more comprehensive. The results are discussed and a daily cycle is presented for different aerosol classes. Overall the manuscript is well written and after some minor changes almost ready for publication.

No additional specific comments.

Review of Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-268-RC2, 2022

Title: Ground-based remote sensing of aerosol properties using high

resolution infrared emission and Lidar observations in the high Arctic"

Author(s): Denghui Ji et al.,

General Comment: The article describes in detail the characterization of aerosol and cloud optical properties by application of an inversion retrieval methodology using sophisticated models for combined measurements of Raman Lidar and FTS (Fourier Transform Infrared Spectrometer) instruments in the station of Ny-Ålesung (Svalbard).  Certainly, the authors restrict the study to two specific cases: aerosol-only case (on 10 June 2022) and cloud-only case (11 June 2022) observing the limitations of FTS data and the help of Raman Lidar measurements for a correct discrimination of aerosol and clouds. In that way that the combination of both instruments allows a better characterization of aerosols and cloud properties. The measurements made by Lidar-Raman and FTS are highly complicated, so these results are of great interest and especially since the study is carried out in Artic areas.    

1. However, I found a little poor the aerosol characterization and I recommend to the authors to add supplementary information from AERONET for a major aerosol characterization, taking into account that only two days are characterized. More detailed information will benefit the section of results. The use of SDA algorithm to discriminate coarse and fine particles can be useful. Furthermore, the data allows to combine visible and far infrared information.
2. The paper is not focused in the methodology, already described elsewhere (Richter et al, 2020), hence this section may be shortened, for example the equations may be removed and give a more qualitative description and problems involved. On the contrary, an idea more quantitative of the associated errors to the retrieved parameters, as AOD, would be appreciated.
3. The Appendix A may be included in the Section 3

Specify comments

Line 135, modify the word DSIORT, Stamnes et al. (1988))

Line  222, Capital letter are used along the document for TCWret V1 and V2:  modify Tcwret V1 and aerosol parameters using Tcwret V2.

For all of this, I considers that he paper may be accepted for publication after the recommended revision of the above points.