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the Creative Commons Attribution 4.0 License.
| 12 Feb 2026
Combined Lidar-Polarimeter Dust Retrievals using Spheroidal and Hexahedral Particle Shape Models
Abstract. Accurately retrieving the properties of mineral dust aerosol is critical for quantifying its impacts on climate and air quality. However, these retrievals are often hindered by overly simplistic assumptions about particle shape. To address this, we test the ability of two non-spherical particle models—a conventional spheroid model and a more recent hexahedral model from the TAMUdust2020 database—to simultaneously reproduce co-located lidar (HSRL-2) and polarimeter (RSP) observations. We performed this test on two Saharan dust events observed over the Atlantic Ocean during the ORACLES 2018 campaign. The test was conducted via a combined atmosphere and surface retrieval using the Generalized Retrieval of Atmosphere and Surface Properties (GRASP), for which we augmented the standard spheroid kernel with a newly implemented hexahedral kernel. Each shape model was then evaluated across three distinct retrieval configurations: HSRL-only, RSP-only, and a combined synergistic HSRL+RSP retrieval. This combined approach leverages the polarimeter’s high sensitivity to total column absorption and particle size with the lidar’s precise vertical profiling. Significantly, this work also presents the first reported one-step synergistic retrieval for desert dust using combined High Spectral Resolution Lidar (HSRL) and polarimeter observations.
We find the hexahedral model consistently provides physically plausible estimates of dust size, refractive indices (n, k), and single-scattering albedo (SSA), whereas the spheroid model requires unrealistic values—such as negligible absorption and an uncharacteristically low real refractive index (n ≤ 1.45)—to match the stringent constraints imposed by the synergistic lidar and polarimeter observations. The spheroid-based retrievals also led to significant cross-instrument inconsistencies, with divergent size and refractive index estimates between RSP-only and RSP+HSRL retrievals. We found that this is rooted in the spheroid model’s limited ability to reproduce high observed particle depolarization ratios within the size distributions and complex refractive indices in the range expected for coarse-mode Saharan dust. Ultimately, the hexahedral model provides a consistent and more physically realistic retrieval of coarse-mode dust properties that fits all observations within their measurement uncertainties.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-5865', Anonymous Referee #2, 27 May 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-5865/egusphere-2025-5865-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-5865-RC1 -
RC2: 'Comment on egusphere-2025-5865', Kirk Knobelspiesse, 03 Jul 2026
Review of “Combined Lidar-Polarimeter Dust Retrievals using Spheroidal and Hexahedral Particle Shape models” Regmi et al AMT
This is an important paper comparing the use of the spheroidal nonspherical aerosol models from Dubovik et al 2006, which are in common use, to hexahedral models for the representation of dust in polarimeter, lidar and combined polarimeter+lidar retrieval algorithms. Overall, I think the manuscript is presented in a compelling manner and it should be published after minor revisions. Several parts of the manuscript are overly detailed or unnecessary, which I will note.
I must also apologize for the delay in my review.
Now on to some specific comments:
- I’m always nervous about claiming novelty in the abstract of a manuscript, since an author needs to be very sure that that is the case. You claim that this is the first reported one step synergistic retrieval of desert dust by a polarimeter and HSRL. That may be the case, but there is another manuscript with some of the same coauthors that was published a few months ago:
Litvinov, P., Chen, C., Dubovik, O., Zhai, S., Matar, C., Li, C., Lopatin, A., Fuertes, D., Lapyonok, T., Bindreiter, L., Dornacher, M., Lehner, A., Dandocsi, A., Gasbarra, D., and Retscher, C.: Synergetic retrieval from multi-mission spaceborne measurements for enhanced aerosol and surface characterization, Atmos. Meas. Tech., 18, 7679–7716, https://doi.org/10.5194/amt-18-7679-2025, 2025.
Perhaps the uniqueness of this manuscript is the comparison to an HSRL style lidar. In any case this manuscript was finalized just as you submitted this one, so it is understandable that it was not mentioned. That should be fixed in review though, and I think a few sentences would be of value about how this work differs with / complements that of Litvinov et al 2025 and the SYREMIS approach described in that paper.
- My main problem with this work is that it represents only two scenes of dust observed by a lidar and polarimeter so it is difficult to know if the conclusions are widely applicable. I look forward to confirmation by future analysis from other campaigns. I think the uniqueness of case 2 should be mentioned in the abstract/conclusions, since I understood it to be atypical as described in section 4.2.2.
- Here’s an aside. If you or other readers of this review are looking for example airborne datasets, I recommend looking at:
Groß, S., Ewald, F., Stevens, B., Wirth, M., Dekoutsidis, G., Ehrlich, A., Kouklaki, D., Kruger, K., Rosenburg, S., Volkmer, L., von Bismark, J., Hirsch, L., Luebke, A. E., Marinou, E., Mayer, B., Pinol Sole, M., Wendisch, M., Windmiller, J., Amiridis, V., Koopman, R., Kubota, T., and Rapp, M.: Persistent EarthCARE underflight studies of the ITCZ and organized convection (PERCUSION): contribution to EarthCARE validation, Atmospheric Measurement Techniques, 19(11), 3933--3959 , https://doi.org/10.5194/amt-19-3933-2026, 2026.
For a list of older campaigns funded by NASA see table 5.1 in: Da Silva, A. M., Maring, H., Seidel, F., Behrenfeld, M., Ferrare, R., and Mace, G.: Aerosol, Cloud, Ecosystems (ACE) Final Study Report, National Aeronautics and Space Administration, https://ntrs.nasa.gov/citations/20205007337, 2020.
The challenge will be finding field campaigns that have both polarimeter and lidar and observations of dust.
Alternatively, an investigator could look at combined EarthCARE and PACE datasets. Those spacecraft have orbits such that they overlap once a day. See https://github.com/seanremy/pace-earthcare-matchups for a tool to find coincident observations. Thus PACE HARP2 or SPEXone polarimeter data could be combined with EarthCARE ATLID observations, the challenge being finding dust since orbit path overlaps tend to occur at about 40˚S.
- An interesting aspect of TAMUdust2020database is that it uses different methods for forward/side scattering than backscattering. How is transition between the two managed, and does it matter in the multiple scattering sense domain?
- I think your parameterization scheme is useful for those planning polarimeter + lidar retrieval algorithms, where three aerosol modes are used with a spherical and a non spherical coarse mode. However, you used the HSRL dust mixing ratio product to constrain retrievals. Do the same data used to make that product get used in your test retrieval? If so, how are you ensuring there is no circular logic? What is it about the dust mixing ratio product that contains information not already in use during a retrieval?
- Frankly, I was a little disappointed to see HSRL as the only lidar configuration. Can you speculate on the results if you had included a lidar that does not have sensitivity to extinction, ie a non-HSRL type lidar?
Now for some more specific comments
- Lines 53-55: please cite PACE, HARP2, SPEXone and AOS mission publications, if they exist. Here’s some that I know:
Werdell, P. J., Behrenfeld, M. J., Bontempi, P. S., Boss, E., Cairns, B., Davis, G. T., Franz, B. A., Gliese, U. B., Gorman, E. T., Hasekamp, O., Knobelspiesse, K. D., Mannino, A., Martins, J. V., McClain, C. R., Meister, G., and Remer, L. A.: The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances, B. Am. Meteorol. Soc., 100(9), 1775-1794 , https://doi.org/10.1175/BAMS-D-18-0056.1, 2019.
Werdell, P. J., Franz, B., Poulin, C., Allen, J., Cairns, B., Caplan, S., Cetinić, I., Craig, S., Gao, M., Hasekamp, O., Ibrahim, A., Knobelspiesse, K., Mannino, A., Martins, J. V., McKinna, L., Meister, G., Patt, F., Proctor, C., Rajapakshe, C., Ramos, I. S., Rietjens, J., Sayer, A., and Sirk, E.: Life after launch: a snapshot of the first six months of NASA's plankton, aerosol, cloud, ocean ecosystem (PACE) mission. in: Sensors, Systems, and Next-Generation Satellites XXVIII 131920E) SPIE., 2024.
Werdell, P. J., Cairns, B., Caplan, S. A., Cetinić, I., Foley, S. R., Franz, B. A., Gao, M., Fasnacht, Z. T., Huemmrich, K. F., Ibrahim, A., Knobelspiesse, K. D., Mannino, A., McKinna, L. I. W., Poulin, C., Rajapakshe, C., and Sayer, A. M.: Advancing Earth System Science With the NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Satellite Mission, Global Change Biology, 32(4), e70869 , https://doi.org/https://doi.org/10.1111/gcb.70869, 2026.
McBride, B. A., Martins, J. V., Puthukuddy, A., Xu, X., Borda, R. F., Barbosa, H. M. J., Hasekamp, O., and Remerh, L. A.: The Hyper-Angular Rainbow Polarimeter-2 (HARP2): a wide FOV polarimetric imager for high-resolution spatial and angular characterization of aerosol and cloud microphysics. in: 70th International Astronautical Congress (IAC) , 2019.
Hasekamp, O. P., Fu, G., Rusli, S. P., Wu, L., Noia, A. D., aan de Brugh, J., Landgraf, J., Smit, J. M., Rietjens, J., and van Amerongen, A.: Aerosol measurements by SPEXone on the NASA PACE mission: expected retrieval capabilities, J. Quant. Spectrosc. Ra., 227, 170 - 184 , https://doi.org/https://doi.org/10.1016/j.jqsrt.2019.02.006, 2019.
Fu, G., Rietjens, J., Laasner, R., van der Schaaf, L., van Hees, R., Yuan, Z., van Diedenhoven, B., Hannadige, N., Landgraf, J., Smit, M., Knobelspiesse, K., Cairns, B., Gao, M., Franz, B., Werdell, J., and Hasekamp, O.: Aerosol Retrievals From SPEXone on the NASA PACE Mission: First Results and Validation, Geophysical Research Letters, 52(4), e2024GL113525 , https://doi.org/https://doi.org/10.1029/2024GL113525, 2025.
Vane, D., Moran, V., Piepmeier, J., Braun, S., Kirschbaum, D., Trepte, C., and Ivanco, M.: The Atmosphere Observing System (AOS): A core component of NASA's Earth System Observatory (ESO). in: 2022 IEEE Aerospace Conference (AERO) 1-7) , 2022.
Also you should note that AOS is cancelled.
- Line 63: please cite supporting literature for statement that HSRL can distinguish aerosol types.
- Lines 76-77: please cite ORACLES campaign publications, like this one:
Redemann, J., Wood, R., Zuidema, P., Doherty, S. J., Luna, B., LeBlanc, S. E., Diamond, M. S., Shinozuka, Y., Chang, I. Y., Ueyama, R., Pfister, L., Ryoo, J.-M., Dobracki, A. N., da Silva, A. M., Longo, K. M., Kacenelenbogen, M. S., Flynn, C. J., Pistone, K., Knox, N. M., Piketh, S. J., Haywood, J. M., Formenti, P., Mallet, M., Stier, P., Ackerman, A. S., Bauer, S. E., Fridlind, A. M., Carmichael, G. R., Saide, P. E., Ferrada, G. A., Howell, S. G., Freitag, S., Cairns, B., Holben, B. N., Knobelspiesse, K. D., Tanelli, S., L'Ecuyer, T. S., Dzambo, A. M., Sy, O. O., McFarquhar, G. M., Poellot, M. R., Gupta, S., O'Brien, J. R., Nenes, A., Kacarab, M., Wong, J. P. S., Small-Griswold, J. D., Thornhill, K. L., Noone, D., Podolske, J. R., Schmidt, K. S., Pilewskie, P., Chen, H., Cochrane, S. P., Sedlacek, A. J., Lang, T. J., Stith, E., Segal-Rozenhaimer, M., Ferrare, R. A., Burton, S. P., Hostetler, C. A., Diner, D. J., Seidel, F. C., Platnick, S. E., Myers, J. S., Meyer, K. G., Spangenberg, D. A., Maring, H., and Gao, L.: An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol--cloud--radiation interactions in the southeast Atlantic basin, Atmospheric Chemistry and Physics, 21(3), 1507--1563 , https://doi.org/10.5194/acp-21-1507-2021, 2021.
- Line 106: you cite a paper by Wang et al 2024 about SEM images of particle shape. I worry that assessing shape that way doesn’t necessarily correspond to shape as it is relative to polarimeter/lidar observations.
- Lines 187-190: I had understood that the Dubovik spheroid model uses a distribution of aspect ratios. Is that something you could consider too?
- Line 213: reduced accuracy for what?
- Line 216 / Table 1: please identify size parameter units
- Line 232 (eqn 4) why isn’t this defined as C = 2Rv / D ?
- Section 2.3 generally: I’m still not entirely convinced that this effort to harmonize the meaning of the size parameter is worthwhile. Ultimately, it will always be an apples to oranges comparison. What matters is how well a model fits the data and if the retrieved parameters are reasonable, even if the treatment of shape in that model varies.
- Line 287: please define what makes RSP measurements ‘high quality’
- Line 299: spell out acronyms and cite relevant papers for AirHARP, AirHARP2 and AirMSPI2
- Line 309: I know enough about RSP to know that it has SWIR channels that were probably inoperable for transit flights because cooling wasn’t available. This should be noted somewhere and discussed. Presumably those SWIR channels contain unique information about the coarse size mode.
- Line 316: You used fixed uncertainties of 3% and 0.002 for I and DoLP respectively for RSP. However, there is a more detailed uncertainty model for that instrument. It probably wouldn’t change your results, but for your awareness check appendix B of:
Knobelspiesse, K., Tan, Q., Bruegge, C., Cairns, B., Chowdhary, J., van Diedenhoven, B., Diner, D., Ferrare, R., van Harten, G., Jovanovic, V., Ottaviani, M., Redemann, J., Seidel, F., and Sinclair, K.: Intercomparison of airborne multi-angle polarimeter observations from the Polarimeter Definition Experiment, Appl. Optics, 58(3), 650--669 , https://doi.org/10.1364/AO.58.000650, 2019.
- Line 320: are these details about GRASP and RSP geometry really necessary?
- Line 359-359: The section about RSP does not speculate on the sources of uncertainty like is done for HSRL here. I think the HSRL sources of uncertainty is not necessary if not also describing the same for RSP.
- Line 357: that GRASP cannot account for systematic uncertainty is a major shortcoming of that algorithm, and the assessments of uncertainty for both RSP and HSRL2 are for combined systematic and random uncertainty. So in this assessment random uncertainties are overestimated and systematic are underestimated. Can you speculate on the impact of this on retrievals and subsequent manuscript conclusions?
- Line 379: how about dust shape? Is this uniformly distributed vertically? I recall seeing presentations that it may not be, at least the Saharan plume.
- Table 2: I’m confused by the caption text “decreasing wavelength starting at 0.355 to 0.555 and increasing from 0.67 to 1.064.” Why wouldn’t you just present them in wavelength order?
- Table 2: Define what is meant by ‘concentration’
- Table 2: ‘sphere fraction’. Is that by volume, optical depth, etc?
- Line 389: define a lagrange multiplier
- Line 437: this classification scheme – was it applied for all test scenarios, including RSP only?
- Line 476: “Exact same” is redundant colloquial term. Simplify to ‘same’
- Line 487: I think you mean ‘increasing wavelength’
- Figure 2: Define ‘hex_rv_eff’ etc in the labels and identify somewhere units of wavelength (which here are in microns but elsewhere in the text are in nm)
- Line 578: Do you mean a chi squared error metric is minimized? Or maybe defined what the weighting is for the RMSE minimization.
- Figures 7 and 8: I find the increase in retrieved refractive index for both case 1 and 2 for the longest and shortest wavelengths to indicate potentially non physical behavior. Could this be an artifact of the smoothness constraints or some other aspect of GRASP?
- Line 649/650. This statement about not using spheroid models if using lidar depolarization is important and should be repeated more strongly in abstract/conclusions
Citation: https://doi.org/10.5194/egusphere-2025-5865-RC2
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