Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D
Round 1
Reviewer 1 Report
Review of "Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D" by Lima et al., 2019.
The submitted manuscript fits within journal scope as its main goal is to estimate cloud top temperature from geostationary satellite imagery.
Overall it is decently written, but, besides minor and specific comments that can be found in the attached file, there is a major flaw that should be addressed.
The authors might be not aware, but the intercomparison vs. CALIOP retrievals is not performed vs. actual measurements, but against the NASA model GEOS-5 which is providing temperature at different atmospheric levels. Usually, intercomparisons are done the other way around, i.e. the observations are used to validate models, not the opposite. In my opinion all this section doesn't make sense, and it is not a negligible part of the manuscript. I don't want to overturn the manuscript, but I think it makes more sense to use lidar data (CALIPSO) to validate the algorithm in classifying clouds (transparent, opaque...) using their optical depth.
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Review of remotesensing-600531
Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D
By C.B. Lima , S.S. Prijith , M.V.R. SeshaSai , P.V.N. Rao , K. Niranjan , M.V. Ramana
<General Comments>
This paper developed a new cloud detection algorithm and a new cloud top temperature retrieval algorithm for a geostationary satellite INSAT-3D, moreover, validated and inter-compares these algorithms with the other sensors’ products such as MODIS and CALIOP. This topic is one of the most important and attracts wide interest for atmospheric science such as climate change and weather forecast. While the contents of an introduction part are clearly written, the explanation of the validation methods is too insufficiency for readers to understand. Furthermore, I have serious concerns about some approaches for the validation. Overall, I cannot recommend this article for publication because it has extensive problems.
<Specific Comments>
1) The most serious problem of this article is that the readers cannot reproduce this study with only information of this paper.
1-1) Where is the study area? For geostationary satellites, the resolution of edge pixels in images is low, but how did you handle them? Did you use only pixels in India? If so, what are the latitude and longitude of the four corners? The data period used for this study is also unknown.
1-2) Please write down the formulas of the used processing. When a method is referred, the reader must read the cited document because the method is not written. For example, which does (BTDTIR1, MIR) mean (TIR1 – MIR), (MIR – TIR1), or the others?
1-3) The details of validation and comparison methods are not written in this paper. Therefore, I cannot judge whether these comparison methods and results are appropriate or not. Furthermore, the readers cannot reproduce these comparisons.
2) Is my recognition that this cloud detection algorithm is just a pre-processing for CTT estimation? If the cloud detection algorithm itself is a product, the accuracy is too bad despite verification only in cases cloud judgment is easy. I think that the rule of the cloud detection algorithm is to detect only clouds that their CTT can be estimated. For these reasons, please clarify the rule of the detection algorithm in this study.
3) How do you decide the combination of used features (cloud detection tests) and their thresholds?
4) Lines 209-210
Are all pixels judged as cloudy in the gradually cold season by this test which uses the highest BT in the same pixels during previous 30 days?
5) Lines 228-229
Does it mean that the highest negative value is used even if there is a positive value exists for the previous 30 days?
6) Lines 246-247 SST threshold test
Although a descending local equational crossing time of Terra is 10:30am, did you apply the SSTCLIM to all times?
7) Lines 253 and Line 582
Why did you use two values about the lapse rate (10Kkm-1 and 6.5Kkm-1) in this study?
8) Line 254
Dose this formula mean that surface pixels are judged as cloudy when the surface temperature of the pixels is under 294K?
9) Lines 263-264
How do you decide sun-glint areas? Please write the formula for sun-satellite geometry. Did you apply the “considering reflectance” to not only water pixels but also land pixels? Moreover, please write the formula to calculate the “considering reflectance”.
10) Table 2
I cannot understand why do you compare your results with results in different comparison methods and regions?
11) Lines 608-609 and 626-633
Does this mean that you decided thresholds of the present algorithm by using CALOP data and you also used the CALIOP data for inter-comparison? Training data to construct an algorithm and decide its thresholds, validation data, and test data for accuracy evaluation must be separated.
On the other hand, you used CALIOP data for validation, to decide used features (tests).
<Technical Corrections>
1) Line 41 “them”
Please clarify what “them” refers to.
2) Line 99 “INAST-3D”
Is this INSAT-3D?
3) Line 100 “an interval of 15 minutes” and line 105 “a temporal interval of 30 minutes”
There are two geostationary satellites, INSAT-3D and INSAT-3DR which observes at a temporal interval of 30 minutes. Do you mean that observations can be made every 15 minutes by using the two satellite?
First, is it really 15 minutes intervals?
Second, please add the explanation that the observations are made at 15 minutes intervals by using the two satellite.
4) 2. Data section
Please create a table for central wavelengths, spatial resolutions, and so on of INSAT-3D..
5) Line 125 “cloud aerosol discrimination score”
Since this is the first appeared, please add a description.
6) Line 136 “SST”
Since this is the first appeared, please write out the abbreviations in full spelling like “Sea surface temperature (SST)”.
7) Figure 1 and 2
The readers cannot understand the meaning of the merges of branches. Do they mean sum or product?
Moreover, the readers cannot understand the directions of flows. Please make them not lines but arrows.
8) Line 224 “previous one”
Please add the explanation of “i” and “i-1”.
9) Line 229
Please revise “lowest positive value” to “the lowest positive value”.
10) Line 283 “partial clouds”
What is the definition of "partial clouds" in this study? Is it a 4x4 pixel cloud?
11) Figure 5 and others “Mean Absolute Bias”
Is “Mean Absolute Bias” “Mean Absolute Error”? If not, please show the formula.
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Reviewer 3 Report
I found this paper is very well written and the methodology is sound. The results of this paper undoubtedly will motivate worthwhile effect to future research.
Some minor comments are in the attached file,
Comments for author File: 
Comments.pdf
Author Response
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Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
I am happy that the authors addressed all my previously raised issues and now the manuscript is ready for publication. Please make an overall check to the sentences and check some plots that look like at lower resolution
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Reviewer 2 Report
The manuscript has been revised well against several problems. However, in my opinion it requires major revision before ready for publication because some serious problems have been not revised enough. Please see the attached file.
Comments for author File: Comments.pdf
Author Response
Please see the attachment.
Author Response File: Author Response.docx
