the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jan 2024
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat/CALIPSO observations
Abstract. A significant fraction of liquid clouds are not captured in existing CloudSat radar-based products because the clouds are masked by surface clutter or have insufficient reflectivities. To account for these missing clouds, we train a random forest regression model to predict cloud optical depth and cloud top effective radius from other CloudSat and CALIPSO observables that do not include the radar reflectivity profile. By assuming a subadiabatic cloud model, we are then able to retrieve a vertical profile of cloud microphysical properties for all liquid-phase oceanic clouds that are detected by CALIPSO’s lidar but missed by CloudSat’s radar. Daytime estimates of cloud optical depth, cloud top effective radius, and cloud liquid water path are robustly correlated with coincident estimates from the MODIS instrument onboard the Aqua satellite. This new algorithm offers a promising path forward for estimating the water contents of thin liquid clouds observed by CloudSat and CALIPSO at night, when MODIS observations that rely upon reflected sunlight are not available.
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Status: final response (author comments only)
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RC1: 'Comment on amt-2023-266', Anonymous Referee #1, 14 Jan 2024
Schulte et al. develop a new satellite retrieval algorithm that can estimate vertical profiles of cloud liquid water content in cases where the cloud water content is too small or too close to the surface to be retrieved by the standard CloudSat retrieval algorithms. The new algorithm is validated by comparing with other satellite products, and the authors convincingly argue that the new data can complement existing CloudSat data products. The paper is clear, concise, and fits well in the scope of Atmospheric Measurement Techniques. However, I have a few comments about how the paper could be improved with additional validation and quality-control analysis. For this reason, I recommend major revision. I believe that the paper will be suitable for publication if my comments are addressed.
General Comments
The authors make a compelling case that their algorithm achieves additional coverage of cloud water content relative to the standard CloudSat radar retrieval algorithms (2B-CWC-RVOD and 2B-CWC-RO) in cases where the clouds are too thin to be detected by the radar. However, I wonder what fraction of the additional coverage corresponds to truly optically thin clouds that cover the entire horizontal pixel and what fraction corresponds to clouds that partially cover the pixel and therefore appear thin. The first case could lead to high quality retrievals of cloud water content because it satisfies the assumption of a horizontally uniform cloud used in the sub-adiabatic cloud model, but the second case may not. It would strengthen the paper if the authors could perform a quality control analysis that estimates the frequency of occurrence of these two cases. This could be done with the MODIS flag for partly cloudy pixels, or perhaps with some other information from CloudSat/CALIPSO.
The case studies in Section 3.1 demonstrate that the new coverage attainted by the author’s retrieval algorithm can complement the data from the existing CloudSat operational retrievals without any clear discontinuities or artifacts. This shows that the new retrieval algorithm can add value to the existing retrieval algorithms when the new algorithm detects a cloud, but the existing algorithms do not. However, I think it would improve the paper if the authors could also do a statistical comparison of the cloud water content in the range bins in which both the new algorithm and the existing algorithm detect a cloud. This would show a more complete evaluation of how well the new data fits with and complements the existing data.
Specific Comments
Fig. 1: panel (b) has a different latitude range than the other panels. Consider changing this so that all panels have the same latitude range to make it easier to compare.
Section 2.2: This section clearly describes the method for estimating the vertical profile of cloud liquid water content relative to the height above cloud base, l(z), and the cloud geometric thickness, H. However, I believe that the height of the cloud base also needs to be known in order to estimate the cloud water content profile as a function of the height above sea level. I could not find the explanation of how the cloud-base height is estimated. Can you please explain this?
Fig. 2: It is difficult to distinguish colors between 10^3 and 10^5 counts. Consider adding contours of counts to improve the clarity of the figure.
Section 3.1 Case studies: Throughout the paragraph starting on line 243, it would help to refer the panel labels in Fig. 6 (e.g. Fig. 6a, Fig. 6b, etc.). This would be clearer than the wording “the next panel down” etc., which is currently used in the text.
Citation: https://doi.org/10.5194/amt-2023-266-RC1 - AC1: 'Reply on RC1', Rick Schulte, 28 Mar 2024
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RC2: 'Comment on amt-2023-266', Anonymous Referee #2, 19 Jan 2024
- AC1: 'Reply on RC1', Rick Schulte, 28 Mar 2024
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