the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jul 2026
A Threshold-Based Method for Cloud Base Height Detection using Ceilometers: Application to Long-term observations in deriving Cloud Vertical Structure
Abstract. Ceilometers are widely used for cloud base height (CBH) detection, primarily through proprietary manufacturer algorithms. Although these algorithms are routinely employed at airports and meteorological stations, their suitability for climatological applications is often limited under complex atmospheric conditions. In this study, we propose an improved threshold-based detection (TBD) method for CBH retrieval that is applicable to both calibrated and non-calibrated ceilometer return signals. The detected cloud layers show good agreement with collocated observations from space-borne active sensors. A comparative analysis between the manufacturer’s algorithm and the proposed TBD approach demonstrates significant improvement in CBH separation. The method is further shown to be adaptable to ceilometers of different makes operating under diverse environmental conditions. The TBD approach is applied to long-term observations (April 2020 – October 2025) from a CL51 ceilometer deployed in the coastal urban environment of Kolkata, eastern India, to investigate cloud processes and characterize cloud vertical structure. A parameter termed normalized cloud occurrence is estimated for single-, double-, triple-, and all-layer cloud cases to qualitatively examine cloud vertical distribution. The close similarity between the occurrence patterns of single-layer and all-layer clouds indicates the dominance of single-layer clouds over the study region, while multi-layer cloud occurrences provide additional insight into cloud vertical structure. Seasonal and diurnal analyses reveal the persistent presence of low-level clouds (< 2 km) throughout the day across all seasons. The CBH of low-level clouds gradually increases after 09:00 local time, peaks during 12:00–15:00, and subsequently decreases, likely driven by solar-heating-induced convection. Such convection facilitates vertical cloud development up to 8–12 km, depending on the season, except during winter. Additionally, a persistent elevated cloud layer near 4 km is observed, likely associated with temperature variations around the 0 °C isotherm. The derived cloud vertical structure has important implications for understanding cloud radiative forcing and improving atmospheric model predictions.
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Status: final response (author comments only)
- CC1: 'Comment on egusphere-2026-3317', Daniel Klaus, 17 Jul 2026
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RC1: 'Comment on egusphere-2026-3317', Anonymous Referee #1, 26 Jul 2026
The study addresses an important aspect of cloud observations by estimating CBH from the Vaisala CL51 ceilometer. The proposed Threshold-Based Method for CBH detection appears promising for long-term cloud observations. However, the method still relies on visual inspection to confirm cloud detection, which limits its applicability for fully automated, continuous observations, particularly at remote or unattended sites. Additionally, the algorithm appears to have limitations in detecting cloud bases below 200 m, which may lead to the omission of low-level cloud events and affect the completeness of long-term cloud climatology.
Comparison with satellite data is not very convincing especially in case of multilayer clouds.
Is TBD method applicable to all ceilometers at different locations or region specific to KNOC
Citation: https://doi.org/10.5194/egusphere-2026-3317-RC1 -
RC2: 'Comment on egusphere-2026-3317', Anonymous Referee #2, 30 Jul 2026
Review of "A Threshold-Based Method for Cloud Base Height Detection using Ceilometers: Application to Long-term observations in deriving Cloud Vertical Structure"The authors present a threshold-based cloud base height detection method and apply it to long-term groundbased ceilometer observations. The resulting cloud base height from the detection method presented here differs from the method used by the ceilometer manufacturer. A comparative analysis is performed and long-term statistics of normalized cloud base height occurrence are presented. The study is well-written and the data processing and cloud base detection method is described clearly. Below are general comments that should be addressed in a revised version of the manuscript.General comments1) There is an inconsinstency about cloud base and cloud top (or cloud vertical structure) in the title and throughout the manuscript. The authors discuss cloud tops based on a distribution of cloud base height. The ceilometer provides information on cloud base only and we cannot infer the cloud vertical extent and cloud top without additional information from radiosondes (relative humidity) or ideally cloud radar. The discussion should be updated and account for the lack of cloud top height information in the groundbased ceilometer data that does not allow any interpretation on the vertical extent of clouds. Finally, it is not clear how we can use cloud base height information in radiative transfer simulations as mentioned in the final paragraph.2) It is not clear why the authors state that the method can be applied to uncalibrated ceilometer signals. While I understand that the method simply relies on a threshold, with additional criteria on spacing etc, there is no unique property of this method that corrects instrument biases or drift. This makes it unsuitable for uncalibrated data, unless the stability of the sensor is known - in which case there is also calibrated data available presumably.3) Supplementary material should not be used to provide 14 additional figures the reader gets pointed at throughout the article. These types of figures should be included in the main text or the appendix. Given the limited space, the authors should decide which ones are really needed, as I do not see a need for a supplement here.4) A very simple climatology of normalized cloud base occurrence is shown for two completely different algorithms. It would have been useful to get also information on the absolute cloud base occurrence, as this is relevant for the surface energy balance and water cycle.5) Are any additional data collected on the site? The results chapter shows interesting views on the diurnal cycle that could be complemented with information on lifting condensation level, radiation data, radiosonde data, rainfall, etc. This will help with the interpretation of the statistics that is currently presented in section 3. Ideally, the information from Figure S1 gets shown together with cloud base occurrence.6) The evaluation of the approach is not convincing. There is only a few hours of case studies and a comparison with CALIPSO (which provides cloud top information rather than cloud base). It would be good to show long-term statistics from the new and the existing method.7) How are the thresholds for the TBD method chosen? Obviously, these seem to be guessed based on individual cases and are likely not optimal. It would be interesting to have a sensitivity study to learn, which parameters are most important for the cloud base statistics.Figures:Most figures need a more descriptive caption. Sometimes it is unclear what the authors are showing. In Figure 4, the right panel is not described at all and the reader has to guess what it is. Only Figure 6 appears to have a detailed caption. Also, I strongly encourage the authors to make sure the figures are of appropriate quality for a publication. Units change for the same physical quantity, colorbars get shuffled, height ranges differ within the same figure, or the figures are simply of very low resolution.Citation: https://doi.org/
10.5194/egusphere-2026-3317-RC2 -
RC3: 'Comment on egusphere-2026-3317', Anonymous Referee #3, 04 Aug 2026
This paper develops a threshold-based cloud base height (TBD) retrieval algorithm for multi-model ceilometers and applies the method to five years of coastal urban observations in Kolkata to analyze cloud vertical structure. The work collects valuable long-term tropical data, compares the new scheme against the manufacturer’s built-in algorithm, and conducts cross-validation with CALIPSO/EarthCARE satellite measurements and European ACTRIS ceilometer datasets, which brings practical value for ground-based cloud observation research. Nevertheless, the manuscript contains a fundamental observational limitation that undermines its core argument: ceilometers are primarily designed to detect cloud base height instead of complete cloud vertical extent. Laser signals are strongly attenuated within liquid hydrometeors, so the instrument cannot reliably capture cloud top height and full vertical cloud layers. The authors overstate the ability of ceilometer data to resolve full vertical cloud structure throughout the title, abstract and discussion. Besides this central conceptual concern, the paper suffers from insufficient uncertainty analysis, limited statistical intercomparison with satellite data, and incomplete validation of fog/precipitation filtering logic. Substantial revisions to methodology, results interpretation and discussion are mandatory before it can be considered for publication in ACP.
- Ceilometers only capture backscatter signals at the lowest cloud base and lose signal penetration inside thick liquid clouds, making them incapable of accurately constraining cloud top altitude and full vertical cloud thickness. The manuscript repeatedly claims its TBD method can retrieve comprehensive cloud vertical structure, which is misleading to readers. The authors must drastically moderate all overemphasized conclusions about vertical cloud extent, add prominent, repeated caveats on laser attenuation bias, and clarify that only cloud base heights are robust retrievals while multi-layer vertical information carries large unquantified errors. All figures interpreting full vertical cloud profiles need revised explanatory text highlighting this critical limitation.
- The TBD method filters fog (<200 m) and precipitating cloud layers via fixed thickness thresholds, but the authors only provide single case examples without bulk statistical evaluation across the 5-year dataset. No quantitative metrics (hit rate, false positive rate, misclassification ratio) are calculated to compare the fog/precipitation discrimination performance between the native CL51 algorithm and the new TBD scheme. The revision must add statistical screening performance derived from the full observation record to prove the TBD filter’s advantages.
- The intercomparison against CALIPSO and EarthCARE only presents several discrete overpass snapshots, without matching hundreds of collocated ground-satellite profiles for statistical analysis. No correlation coefficients, bias or RMSE between TBD-derived CBH and spaceborne cloud base retrievals are provided. The authors must build a large collocated matching dataset and add quantitative statistical comparison to solidify cross-sensor validation.
- Sensitivity tests are missing for key TBD threshold parameters. The TBD method sets fixed rules for minimum cloud layer thickness, temporal continuity window and allowable CBH vertical variation, but the manuscript does not test how adjusting these thresholds alters the derived vertical cloud separation distances shown in Figure 9. Without sensitivity experiments, it is unclear whether the larger inter-layer gaps from TBD reflect real atmospheric cloud structures or artificial algorithm bias. Threshold sensitivity analysis must be supplemented in the revised methodology.
Citation: https://doi.org/10.5194/egusphere-2026-3317-RC3
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The present study addresses an interesting and important problem and has the potential to make a valuable contribution. However, I believe that the current version would benefit from additional methodological detail and stronger support for several of its central conclusions. Addressing the points discussed in the attached PDF would, in my opinion, substantially strengthen the manuscript.