For this preliminary "rapid-access review", I find no major problems with the draft in my quick read.

I will be very glad to provide a full review of this paper.

Reviewer’s comments on “Near global distributions of overshooting tops derived from Terra and Aqua MODIS observations” by Hong et al. 

General comments

This paper used MODIS IR brightness temperatures to identify overshooting tops (OTs) and compiled a two-decade record of OTs. The authors utilized a Logistic Regression algorithm to come up with the OT probability for each MODIS IR pixel. Those with OT probability higher than 0.9 are classified as OTs.  The Logistic Regression was trained and validated with about 300 hand-picked cases with the collocated CloudSat vertical profile as the ground truth.  Once the OT dataset is created, the authors continue to study season distributions of OT occurrences, their diurnal cycle (based on 4 samples on a 24-hr time scale from Aqua and Terra), and land-ocean contrasts. Results are largely consistent with previous studies.

Using a long data record to compile OTs should contribute to study of the climatology and variabilities of OTs. Initial results all look reasonable. Therefore, the paper is publishable. There are, however, a few concerns which need to be addressed in revision. 

First, using only 287 hand-picked cases to build the Logistic Regression algorithm is probably not robust enough.  Asking the authors to increase the cases to 2000 or 20,000 would probably be unrealistic since the hand-picking procedure is very time consuming.  I’d suggest the authors find another 300 different cases, build another Logistic Regression relationship, and use it to re-process all data. Then, compare some benchmark statistics between the two OT datasets. 

Second, due to parallax shift, MODIS pixels for deep convection are about 5 km away from the CloudSat footprint. See Wang et al. (2011 https://doi.org/10.1029/2011JD016097). The authors cited this paper but didn’t really incorporate the parallax correction in their methodology.  Mismatch between MODIS pixels and OTs could lead to biases in their Logistic Regression algorithm.

Overall, I’d like to recommend the paper be accepted after these issues are addressed.

Specific comments:

(Line 112) “...large spatial resolutions”: sounds a little awkward. “Coarse spatial resolution” may sound better.

(Lines 224-226) A typical non-OTs (NOTs) example should be shown. “Tb11 is cold”: how cold is considered cold enough for NOTs? Any threshold?

(Line 250) “Figure 1b indicates the cirrus anvil in cyan”: the right-side cirrus anvil ends before Tb11 > 260K. I wonder why it doesn’t extend all the way to where Tb11 > 260K, the threshold set for cirrus anvil.

(Line 264) I am a little puzzled to see that only 16% of NOTs are warmer than the tropopause temperature. For the remaining 84%, the cold centers are colder than the tropopause but no overshooting feature?  I guess showing a few examples of NOTs will help.

(Line 360) “...considering the tropopause height variability”: in the presence of overshooting tops, local temperature profile often shows double tropopause: one is associated with the background tropopause and another is caused by the overshoots which are extremely cold. Some examples of the double tropopause can be found near hurricane eyewalls, e.g.,Fig. 2 in https://doi.org/10.1002/2013JD020934 . This kind of overshoot-generated tropopause height variability should be noted.

(Lines 430-441) OTs associated with shallow convection are interesting. How tall are these OTs and what is the mean tropopause height in this region?

(Line 445) “Without no strong convective cores”: should be “without strong convective cores”.

(Line 626) Change “accept” to “except”.

Review of “Near Global Distributions of Overshooting Tops Derived from Terra and Aqua MODIS Observations” by Yulan Hong, Robert J. Trapp, Stephen W. Nesbitt and Larry Di Girolam

This is an interesting paper on providing insight about the frequency and size distribution of overshooting cloud tops into the low stratosphere for deep convective cloud systems on a global basis.  This study represents the first such study using MODIS IR measurements where both TERRA (descending, ~10:30 am/pm LT equator crossing) and Aqua (ascending, ~13:30 pm/am equator crossing) MODIS measurements were employed.  With about 20 years of MODIS daytime and nighttime data from both TERRA and Aqua they are able to derive global seasonal patterns, land-sea contrast, and information on diurnal variability of overshooting cloud tops.  The paper is well written describing current and previous algorithms for detecting overshooting cloud tops.  I would recommend publication after generally minor changes.

* Figure 1: Please separate (a) and (b) parts of the figure more – in the pdf version the labeling for these two parts overlap.

* Line 236:  “cirrus anvil”

* Table 1:  If possible, please include uncertainties for the coefficients in this table.  The table caption lists significance at 99% but it is not certain what the 99% is relative to (all coefficients assumed to be zero under the null hypothesis?).  The table caption also says regression coefficients which the text indicates are the bi (i=1,2,3) constants, but instead xi (i=1,2,3) numbers are referred to in the table.  The xi’s if understood correctly represent the three temperature differences (input training data) to derive the bi’s via regression.  Please help clarify the table and numbers listed for the readers.

* Figure 4:  In the tropics, the tropopause height is consistently about 16-17 km for most any definition of tropopause, dynamical or chemical, but the tropopause height in Figure 4 for the tropics shows about 12 km.  Is this because the tropical band in the figure extends to +/- 25 degrees latitude and there is some higher-latitude subtropical influence?  Or perhaps the vertical axis numbers are mislabeled for the tropics?  Your tropopause height comes from combining MERRA-2 lapse-rate plus PV surface, so in the tropics it should be coming from lapse rate which is about 16-17 km.

* Line 414:  Do you mean “variety” rather than “verity”?

* Line 613:  “times”

* Line 636:  “area” – also in this sentence, could the future work include taking advantage of the long ~20 year records from TERRA/Aqua MODIS IR measurements to investigate climate-related decadal changes/trends in OT events?

* In the Summary is it possible to expand a bit to describe some general science implications originating from these new results?

* Do you have an OT dataset derived from this study that can be listed in the “Data availability” section?