VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO

Round 1

Reviewer 1 Report

Title: VIIRS Edition 1 cloud properties for CERES. Part 2: Evaluation 2 with CALIPSO

 

In order to establish reliable long-term climate data records, it is important to determine the accuracy of cloud and radiation measurements from instruments retrieved from sensors. In this manuscript, the cloud amount, cloud phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The methods and new products proposed in this paper can provide important technical and data support for cloud  science, climate change, radiation budget and other related studies. The paper might be accepted after addressing the following comments:

 

1)    Need more detailed description of the comparison method used in this manuscript and the results should be compared to other paper that have done the same kind of validation work.

The new calculation  (CV1S ) proposed in this paper has a high verification accuracy of cloud top height (single layer water cloud) during the day and night. However, the accuracy of ice clouds is significantly lower than that of water clouds. This result is close to that of Nakajima et al (2019). However, the latest CARE cloud top parameter inversion algorithm by Xu et al (2021) significantly improves the problems of existing algorithms. Compared with previous methods, CARE's new algorithm significantly improves the retrieval accuracy of ice clouds over the ocean. Therefore, it is recommended to further evaluate the results of this paper in combination with the relevant research (or other relevant algorithms) proposed above.   Nakajima, Takashi Y., Haruma Ishida, Takashi M. Nagao, Masahiro Hori, Husi Letu, Riko Higuchi, Naoya Tamaru, Naritoshi Imoto, and Akihiro Yamazaki. "Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products." Progress in Earth and Planetary Science 6, no. 1 (2019): 1-25.  

 

Ri, Xu, Letu Husi, Takashi Y. Nakajima, Chong Shi, Tana Gegen, Jun Zhao, Peng Zhang, Liangfu Chen, and Jiancheng Shi. "Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) Cloud Top Property Product from Himawari-8/AHI: Algorithm Development and Preliminary Validation." In AGU Fall Meeting Abstracts, vol. 2021, pp. A14B-08. 2021.  

2)    Which studies are the formulas in line 173-184 based on?

3)    The abstract can be further expanded by stressing the innovation point of this study.

4)    What is the basis of horizontal averaging (HA) distances of 1/3, 1, 5, 20, and 80 km in Figure 2: Mean zonal cloud fractions from CV1S and CALIOP for four horizontal averaging scales, JAJO 2015-16, using all matched data.

5)    Why the trends of HR and Bias are same in the Figure 3: Dependence of CV1S cloud detection metrics on CALIOP horizontal averaging, JAJO, 279 2015-2016, for 0/100 matched pixels.

 

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

General comment:

 

Based on the consistency comparison between CV1S and CM4A in the first part, this study determines the accuracy and possible error sources of CV1S compared with CALIPSO lidar cloud products, which is very important for improving the inversion algorithm in the future. Through a large number of detailed comparisons, it was concluded that the CERES VIIRS cloud product has the same level of accuracy as CERES MODIS and can be used to provide climate data records. The work of this paper is meaningful, considering a large number of influencing factors in the comparison process, and providing a great deal of knowledge to the scientific community. A small flaw is that the content of the article is too large due to the detailed data analysis, and it is suggested that some content can be described concisely, while expressions that are not too close to the topic of the article can be omitted. In general, the article can be published with minor modifications.

 

Specific comment:

 

1. The introduction section is relatively general and can be expanded on the content, especially the introduction of the cited reference can be more specific. For example, which parameters of the cloud are compared, how accurate the comparison is, etc

 

2. One problem with active and passive comparison is that lidar observes a line, while passively obtains information on a surface, where there is issue of pixel uniformity. How do you think about that?

 

3. L173-184 Whether the formula is followed by a number needs to be unified.

 

4. L185 Whether any content is missing from before the sentence.

 

5. Figure 1. What is the spatial resolution of this graph?

 

6. L236/L277/L389 When inserting a table and a figure, try to keep the statements as continuous as possible.

 

7. L451 What does this mean?   ” The CM4A difference from [20] is -1.31 ± 3.07 km for CM4A.”

 

8. As can be seen from Figure 13b, CVIS' detection accuracy for supercooled water clouds is good, could you introduce how VIIRS identifies supercooled water clouds at night in this algorithm? Do you have any considerations for improving the algorithm in the future?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf