Time-Dependent Systematic Biases in Inferring Ice Cloud Properties from Geostationary Satellite Observations
Round 1
Reviewer 1 Report
Review ‘Time-dependent systematic biases in inferring ice cloud properties from geostationary satellite observations’
Authors present possible error sources of retrieved COT and CER by sub-sampling, including SZA, VZA, pixel resolution and cloud types. The reason for doing this study is that three bands (0.64 um,0.86 um and 2.24 um) used in retrieving COT and CER have different pixel resolutions of 0.5 km, 1 km, and 2 km, respectively. This study tried to quantify these error sources. The method is very straight forward and has very detailed description. There are a few comments and suggestions:
(1) Instead of describing angles and distances used in equations, a simple diagram may be easier than reading so many words.
(2) A few places mixed high cloud and ice cloud. High clouds include cirrus, cirrostratus, and cumulonimbus in the manuscript. Since authors only checked CTP, which is less than 440 mb, I don’t think the liquid contribution to COT has been eliminated, especially for cumulonimbus clouds. Therefore, I think the discussion should be for ice layer with the CTPs falls in 440-0 mb.
(3) Do you have enough samples in each 2x2 km2 pixel that only contains cirrus, and/or cirrostratus? Whether your conclusions will be different from current ones?
(4) I cannot find reference 39, please check it.
(5) Line 460-462: do author imply that resampling error carried the COT retrieval error?
(6) Sunlight angle is actually 180-scattering angle, in which the scattering angle is one definition to be easily understood.
(7) There are quite few places that line numbers show in front of the line, please be carefully check your PDF file.
Author Response
Thank you so much for your valuable comments and suggestions. Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
It would be more interesting if, in addition to trying to highlight the biases due to the methodology or the sampling ,the manuscript includes characterisations of the properties and comparisons with other analyses done in different areas
Author Response
Thank you so much for your comments and suggestions. Please see the attachment.
Author Response File: Author Response.docx
Reviewer 3 Report
In this paper, the author examine various 18 error sources of retrieval biases of cloud optical thickness (COT) and effective radius (CER) caused 19 by sub-sampling, including the solar zenith angle (SZA), viewing zenith angle, pixel resolutions, 20 and cloud types. It is very fundamental for cloud optical properties retrieval using GEO satellite. Although the manuscript is generally well presented, some problems must be addressed before it can be accepted by Remote Sensing.
1. What is the nadir point of GOES-17?
2. Line 110, it should be “during the daytime”.
3. I think the case with VZA>65 degree will introduce more errors. The ATBD of daytime cloud optical and microphysical properties of GOES-R set it as 65 degree.
See: Walther, A., Straka, W., & Heidinger, A.K. (2011). ABI algorithm theoretical basis document for daytime cloud optical and microphysical properties (DCOMP). NOAA Goes-R ATBD
4. Figure 3 is not clear. Also I am confused to how do you get the Nakajima-King table error for different cases? You used observation data?
5. In Figure 4, 300% relative error in CER is too huge. How to explain it?
Author Response
Thank you so much for your valuable comments and suggestions. Please see the attachment.
Author Response File: Author Response.docx
Round 2
Reviewer 3 Report
I have no question. It is ok!
