the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of cloud height, optical thickness, and phase retrievals from the CHROMA algorithm applied to Sentinel-3 OLCI data
Abstract. We previously developed the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm for the Ocean Color Instrument (OCI) on the new NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Here, we apply CHROMA to observations from the Ocean Land Colour Instrument (OLCI) to guide expectations for PACE, as it will take some time to obtain large-scale validation data for OCI. We use cloud top height (CTH), phase, and (for liquid clouds) cloud optical thickness (COT) data from the ground-based Atmospheric Radiation Measurement (ARM) network to evaluate the OLCI retrievals. We found that OLCI and Moderate Resolution Imaging Spectroradiometer (MODIS) CTH compare similarly well to the ARM reference. OLCI has a tendency to underestimate CTH as CTH increases, and algorithm assumptions about cloud geometric thickness may contribute to this. ARM COT from multifilter shadowband radiometers (MFRSR) and Sun photometers are well-correlated with one another, albeit with a roughly 30 % offset on average; OLCI and MODIS COT agree more closely with the MFRSR data. OLCI retrieval uncertainty estimates show skill at telling low-uncertainty cases from high-uncertainty ones, although CTH uncertainties are underestimated. Additionally, we compare the OLCI data to satellite retrievals based on thermal infrared measurements from MODIS and and Sea and Land Surface Temperature Radiometer (SLSTR) data. Differences are broadly consistent with physical expectations based on the A-band vs. thermal techniques, although one key challenge in such aggregated comparisons is different cloud masking sensitivities and algorithm failure rates meaning additional sampling differences are introduced. We conclude by discussing the transition to and possible enhancements for PACE OCI.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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RC1: 'Comment on egusphere-2025-2005', Anonymous Referee #1, 17 Jul 2025
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The authors have made a thorough, comprehensive yet clearly explained evaluation of OLCI CHROMA cloud parameter retrievals.
I recommend publication with only minor remarks.
Introduction. The authors have used ARM ground-based data but not ACTRIS Cloudnet ground-based data (https://cloudnet.fmi.fi/, Illingworth et al. (2007)), although this network delivers very similar data, among which CTH and cloud phase data, and is also used for satellite cloud product validation (e.g., Compernolle et al., 2021, Vinjamuri et al. (2023)). Please provide some rationale why ACTRIS Cloudnet was not considered. Please also include a paragraph with references on how ARM and Cloudnet was previously used in satellite cloud product validation.
Page 6, line 148. Please provide some numbers how the CTP 60 mb uncertainty goal translates to CTH uncertainty. This is currently provided on page 13, line 273, but it should (also) go here.
Page 6, line 148. Please provide reference for standard atmospheric profile.
Page 6, line 149. Where do these numbers for COT uncertainty goals 25%, 35% come from? Also from Werdell et al 2019? Please indicate.Page 7, line 181 and page 8, line 1. 'the retrieved CTH'. Retrieved by the satellite, right? Please make more clear by stating 'where h is the CTH retrieved by the CTH'
As indicated by the authors, the CTH retrieved by the satellite can have an error, in which case the parallax correction will also have an error. Have the authors considered to combine the CTH of ARM (which is the ground-truth) with the viewing zenith angle of the satellite to estimate the correction? Is there a reason why this was not considered?
Table 1 and 2. This table has 3 main categories: 'COT<3', 'single-layer COT>=3', 'multi-layer COT>=3'. However, from fig 3 it is clear that there is also a strong dependence on CTH.
I would therefore propose to have within each category, below each row with 'All', a row for low clouds (e.g., CTH<3 km or CTH<4 km) and a row for high clouds (CTH >3 km or > 4 km).Page 12, line 255-256. Â "while the ARM data may be sensitive to lower cloud droplet concentrations (and retrieve a higher top) than satellite remote sensing, this would not account for a multi-km error"
In Sneep et al. (2008), figure 1, large differences are obtained between the cloud pressure from MODIS based on thermal infrared, and those based on retrieval using the O2 A absorption, using O2-O2 absorption and rotational Raman scattering.ÂGiven these large differences, is it therefore not imaginable that the CTH negative bias of OLCI CHROMA is because it is based on O2 A absorption? Note also that in the S5P TROPOMI Cloud routine validation, large discrepancies are obtained between the cloud top height from OCRA\ROCINN CAL (based on O2A absorption) and that of Cloudnet (Lambert et al., 2025, table 2). Â
Page 20, line 379. The authors motivate why they use CTP instead of CTH. Still it would be good to have results for CTH as well, as it gives a point of intercomparison with the OLCI vs ARM comparisons. In particular, it would be interesting to have a figure similar to Fig 3 (CTH difference vs CTH) for OLCI CHROMA vs MODIS and vs SLSTR, to check if discrepancy increases with CTH.
References
Illingworth et al. (2007). Illingworth, A. J.; Hogan, R. J.; OtextquotesingleConnor, E. J.; Bouniol, D.; Delanoë, J.; Pelon, J.; Protat, A.; Brooks, M. E.; Gaussiat, N.; Wilson, D. R.; Donovan, D. P.; Baltink, H. K.; van Zadelhoff, G.-J.; Eastment, J. D.; Goddard, J. W. F.; Wrench, C. L.; Haeffelin, M.; Krasnov, O. A.; Russchenberg, H. W. J.; Piriou, J.-M.; Vinit, F.; Seifert, A.; Tompkins, A. M. & Willén, U.Cloudnet. Bulletin of the American Meteorological Society, 88(6), 883-898. https://doi.org/10.1175/BAMS-88-6-883
Vinjamuri et al. (2023). Vinjamuri, K. S.; Vountas, M.; Lelli, L.; Stengel, M.; Shupe, M. D.; Ebell, K. & Burrows, J. P. Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic. Atmos. Meas. Tech., 16(11), 2903-2918. https://doi.org/10.5194/amt-16-2903-2023
Compernolle et al. (2021). Compernolle, S.; Argyrouli, A.; Lutz, R.; Sneep, M.; Lambert, J.-C.; Fjæraa, A. M.; Hubert, D.; Keppens, A.; Loyola, D.; O'Connor, E.; Romahn, F.; Stammes, P.; Verhoelst, T. & Wang, P.Â
Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI emO_2--emO_2, MODIS, and Suomi-NPP VIIRS Atmos. Meas. Tech., 2021, 14, 2451-2476
https://doi.org/10.5194/amt-14-2451-2021Sneep, M.; de Haan, J. F.; Stammes, P.; Wang, P.; Vanbauce, C.; Joiner, J.; Vasilkov, A. P. & Levelt, P. F. Three-way comparison between OMI and PARASOL cloud pressure products J. Geophys. Res., Wiley-Blackwell, 2008, 113, D15S23
https://doi.org/10.1029/2007jd008694Lambert et al. (2025), Quarterly Validation Report of the Copernicus Sentinel-5 Precursor OperationalÂ
Data Products #27: April 2018 –  May 2025.Â
Lambert, J.-C., A. Keppens, S. Compernolle, K.-U. Eichmann, M. de Graaf, Â
D. Hubert, B. Langerock, M.K. Sha, E. van der Plas, T. Verhoelst, T. Wagner, Â
C. Ahn, A. Argyrouli, D. Balis, K.L. Chan, M. Coldewey-Egbers, I. De Smedt, Â
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K. Michailidis, A. Pseftogkas, S. Nanda, S. Niemeijer, A. Pazmiño, G. Pinardi, Â
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Version 27.01.00, 227 pp., 15 June 2025.Citation: https://doi.org/10.5194/egusphere-2025-2005-RC1 -
RC2: 'Comment on egusphere-2025-2005', Anonymous Referee #2, 17 Jul 2025
reply
Review of the paper "Evaluation of cloud height, optical thickness, and phase retrievals from the CHROMA algorithm applied to Sentinel-3 OLCI data" by Sayer et al.
This paper introduces the CHROMA algorithm which was developed to retrieve cloud information such as cloud height and optical thickness. As the authors explain, this algorithm was intended to be used for the OCI instrument on the new PACE mission. The algorithm was applied to OLCI data instead. The results presented in this paper are therefore also interesting to the OLCI community.
The topic of this paper is certainly fitting for AMT. The paper is interesting to read and well written. I think this paper deserves publication.
I came across a few minor typos:
- page 6, line 125: "although is" -- this should probably be something like "although it/this is"?
- page 7, line 172: "0.1oresolution" --> "0.1o resolution" (space missing)Citation: https://doi.org/10.5194/egusphere-2025-2005-RC2
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