the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2021
Latent heating profiles from GOES-16 and its comparison to heating from NEXRAD and GPM
Abstract. Latent heating (LH) is an important quantity in both weather forecasting and climate analysis, being the essential factor driving convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in the look-up table (LUT) that relates instantaneous radar profiles to corresponding heating profiles. These radars, first on TRMM and then Global Precipitation Measurement (GPM), provide a continuous record of LH. However, with observations approximately 3 days apart, its temporal resolution is too coarse to be used to initiate convection in forecast models. In operational forecast models such as High-Resolution Rapid Refresh (HRRR), convection is initiated from LH derived from ground based radar. Despite the high spatial and temporal resolution of ground-based radars, one disadvantage of using it is that its data are only available over well observed land areas. This study suggests a method to derive LH from the Geostationary Operational-Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when coupled to a LUT similar to those used by the TRMM and GPM radars, can equally be used to derive LH profiles for convective regions using model simulations coupled to a convective classification scheme and channel 14 (11.2 μm) brightness temperature. Convective regions detected by GOES-16 are assigned LH from the LUT, and they are compared with LH from NEXRAD and one of Dual-frequency Precipitation Radar (DPR) products, Goddard Convective-Stratiform Heating (CSH). LH obtained from GOES-16 show similar magnitude with NEXRAD and CSH, and vertical distribution of LH is also very similar with CSH. Overall, GOES LH appear to have the ability to mimic LH from radars, although the area identified as convective is roughly 25 % smaller than the current HRRR model, while the heating is correspondingly higher.
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Preprint
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Interactive discussion
Status: closed
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RC1: 'Comment on amt-2021-97', Anonymous Referee #1, 21 Jun 2021
This manuscript describes an approach for estimating latent heating profiles from high-temporal resolution geostationary satellite observations to fill the need for more frequent observations for assimilation into NWP models. While ground-based radar is currently used for this purpose, such observations are only available over well-instrumented land areas creating gaps in coverage that can negatively impact forecasts. While latent heating has been estimated from low-earth orbiting satellites, these platforms do not provide sufficient temporal resolution to fill this need. Thus the development of a GOES-based algorithm is well-motivated and there is evidence supporting the suggestion that geostationary visible and infrared radiances carry information for identifying convection from which it may be possible to derive approximate latent heating profiles.
Regrettably, while the material is appropriate, the manuscript suffers from several critical flaws that render it unsuitable for publication in Atmospheric Measurement Techniques at this time. The description of the algorithm lacks several important details and a number of important assumptions are insufficiently validated. Only one snapshot is presented as verification of algorithm performance so the conclusions lack justification. In addition, the narrative suffers from numerous grammatical errors that make the manuscript difficult to read and many arguments hard to follow. While this alone would not lead me to reject the paper, when coupled with the scientific flaws, I feel the manuscript is not currently suitable for publication. requires substantial editing before it can be submitted elsewhere for publication. For these reasons, I do not recommend the paper be accepted for publication at this time.
Major Comments
- My primary concern with the study is the fact that the results are insufficient to provide a sufficient assessment of the algorithm performance. A single snapshot from one single convective scene is presented as justification that the approach has merit. Furthermore, the limited results that are presented show some very significant differences between estimates that warrant further investigation and explanation. The overall conclusion from Section 4 appears to be that substantial differences in individual latent heating profile estimates coupled with substantial differences in the areal coverage appear to offset one another to yield area-mean latent heating profiles that are in reasonable agreement in this particular scene. However, very little deeper explanation is conducted to explain these large compensating errors and there are no guarantees that such errors will always offset each other as they do here. Given the very indirect relationship between cloud top properties, model vertical motion, radar reflectivity, and latent heating, substantially more investigation is required to convince the reader that the algorithm is providing reasonable results. In addition to case studies, some statistical analysis of a much larger volume of data needs to be presented.
- Another significant flaw concerns the description of the algorithm itself and verification of the associated assumptions. Section 3.1 notes that growing convection is identified in GOES-16 observations when ‘Tb decrease over ten minutes for two water vapor channels … is greater than the designated threshold’ but neither the channels nor the threshold is specified. Similarly, the paper states ‘For mature convection, the method looks for grid points that have continuously high reflectance, low Tb, and lumpy cloud top over ten minutes’ but no quantitative information is provided regarding the definition of the qualitative terms continuous, low, and lumpy. More importantly, given the goal of reproducing radar-based latent heating estimates, how do convective distributions identified using these definitions compare to the 28 dBZ threshold used by NEXRAD? The only direct comparison provided in the manuscript is a single snapshot in Figure 3 with no quantitative analysis. This lack of verification is amplified in the subsequent assignment of a corresponding vertical motion threshold to mimic these criteria. The only support provided for the 1.5 ms-1 threshold is a table comparing convective areas from ‘observations’ and different vertical velocity thresholds but its not clear what data are used to derive these cases or how well this vertical velocity threshold actually compares to the 28 dBZ radar-based method.
- The description of the cases used to drive the model simulations is also incomplete and no verification of model performance is provided, e.g. against NEXRAD observations.
- The description of the TRMM and GPM algorithms in Section 2.2. is difficult to follow and I’m not sure the non-expert reader would glean even a basic understanding of how these algorithms work from this discussion.
Additional Comments
- The manusrcipt requires substantial editing to improve grammar and readability.
- For completeness in the introduction, Nelson et al (2016) and Nelson and L’Ecuyer (2018) introduce an algorithm analogous to SLH for retrieving latent heating profiles from shallow convection.
- The sentence at the end of Section 1 is not really a complete thought.
- The labels on many figures, especially the panels in Figures 1 and 3 are much too small.
- It is not obvious what all of the variables listed on Line 113 represent.
- It is not clear what is meant by the sentences: ‘Tb at 11.2 which is used to construct the LUT is mostly sensitive to hydrometeors or water vapor. Accordingly the signal received by the channel will be largely from layers with high cloud water contents.’ The 11.2 micron channel is a window channel and not very sensitive to water vapor and the signal received will typically come from the highest cloud layer in the atmosphere not the layer with the highest cloud water contents.
- How well do the values in Table 3 compare to observations? Couldn’t NEXRAD and GOES be combined to examine this.
References
Nelson, E., T. S. L’Ecuyer, S. Saleeby, S van den Heever, and S. Herbener, 2016: Toward an Algorithm for Estimating the Latent Heat Released in Warm Rain, J. Atmos. Oceanic Tech. 33, 1309-1329.
Nelson, E. L. and T. S. L’Ecuyer, 2018: Global character of latent heat release in oceanic warm rain systems, J. Geophys. Res.123, 4797-4817.
Citation: https://doi.org/10.5194/amt-2021-97-RC1 -
CC1: 'Comment on amt-2021-97', Zhengzhao Johnny Luo, 29 Jun 2021
Equation (1): shouldn't it be Q1 - QR, instead of Q1-Q2?
Citation: https://doi.org/10.5194/amt-2021-97-CC1 - RC2: 'Comment on amt-2021-97', Anonymous Referee #3, 02 Jul 2021
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RC3: 'Comment on amt-2021-97', Anonymous Referee #2, 05 Jul 2021
Review for AMT of “Latent heating profiles from GOES-16 and its comparison to heating from NEXRAD and GPM” by Yoonjin Lee et al.
General Comments:
Latent heating (LH) is an important process-level cloud variable. LH retrieval, over the past couple decades, has been largely limited to TRMM, GPM and ground-based radar/NEXRAD (the latter more recently). TRMM and GPM have tropics-wide views, (or near global views for GPM) but also have infrequent revisit times, and thus the temporal resolution of LH retrievals is typically on the order of days. Conversely, ground-based radar retrievals offer increased temporal sampling, though data are not available globally. This paper recognizes the above limitations and aims to develop a retrieval of LH that allows for increased temporal resolution for LH data over a large spatial domain. The retrieval uses GOES input data (and though not stated, the algorithm developed potentially could be applied to multiple geostationary datasets.)
The topic is very appropriate for AMT, and successful LH retrievals will be of use to both the NWP and cloud physics research communities. However, the paper as written requires major revisions largely due to a need for a) more substantial statistical evaluation of the LH retrieval beyond the few cloud snapshot samples discussed; and b) improved clarity of presentation and improved grammar/sentence structure throughout.
Relevant to major revision comment a) above, the authors need to do a much more thorough analysis of the LH retrievals (either via comparison to both NEXRAD and GPM, or just comparison to either NEXRAD or GPM). For example, how do the new LH retrievals compare to GPM CSH over (flat) land vs ocean vs mountains? How about for NEXRAD locations spanning different types of convective regimes? Or, how would results compare as a function of echo top height or surface rainfall rate? There are many ways to slice-and-dice and/or to design an analysis for comparing LH retrievals. Either way, I do not think comparing a handful of cloud snapshots for one 2 deg domain at one time snapshot is sufficient for this publication. I also do not think a new intercomparison analysis would add so much new text and images so as to warrant rejection as the paper currently exists. Regarding major issue b), I strongly recommend a thorough read-through and correction of the English/grammatical structure. After noticing many issues with sentences in most paragraphs of the manuscript, I decided to not focus on grammar past the Abstract (see Technical Issues section below) and instead, I focused mostly on the science and retrieval aspects (see additional comments in “Specific comments” below).
Specific Comments:
Beginning on line 32 (Introduction) and discussion about using LH in NWP: The authors write that LH aims to increase buoyancy in the atmosphere. If one thinks of most deep convection as rooted in the lower atmosphere (or below 2 km), then buoyancy in the lower troposphere is most relevant. However, LH heats the atmosphere to a larger degree as one moves up – in other words, LH is larger at 5 km than it is at 4 km, and larger at 4 km than 3 km, larger at 3 km than 2 km, and so forth. If heating is larger aloft, then LH is doing the exact opposite: it is stabilizing the local atmosphere. And this is what we expect of convective LH – the “job” of convection is to stabilize the atmosphere. Thus, I recommend removing all science text about buoyancy being enhanced by LH as reasons for use in NWP. Instead, I would recommend using the arguments presented in the original conference pre-preprints on using LH in NWP:
https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124540.htm
https://ams.confex.com/ams/88Annual/techprogram/paper_134081.htm
Upon reading those, it is clear that LH – or a perturbation in heating above the surface – allows for increased local surface convergence and local upper-level divergence to be induced. The “forced” local surface convergence in the presence of an already conditionally unstable atmosphere – or, more realistic local vertical circulations – increase convection for regions that are already unstable or conditionally unstable. But, importantly, the LH itself is not causing buoyancy because the vertical height derivative of LH is positive (dLH/dz>0) up to above the melting level (and that differential heating weakens buoyancy).
Line 28: at a few km, convection can be approximately resolved or “permitted” – to resolve convection, one needs a few hundred-meter resolution simulations. See Andreas Prein et al. studies and anything more recent, for example.
Line 58: I believe melting level and the PR/DPR convective-stratiform flags are used for LUT indexing, too?
Line 75: There are orbital level DPR products – they do not provide rapid revisit for any one location, but when they are available, they do not represent a temporal resolution of a day. They represent the instantaneous LH for that type of convection characterized by that surface rainfall rate, ETH, etc. *on average*, so it is not clear what is meant by DPR having a temporal resolution of a day.
Line 91: In Eq. (1), on LHS, should be Q1-QR, not Q1-Q2.
Line 100: the authors have already introduced CSH and SLH acronyms previously.
Lines 108 – 114: it sounds as if only one LH or Q1 profile is provided. CSH also provides convective and stratiform profiles separately since both convection and stratiform cloud types exist simultaneously in a given grid box.
Line 113: what is a “decreasing flag”?
Line 128 and Figure 1: I would suggest improving the color gradation in Fig. 1 so that 0 K/hr is not centered on red and white is not the largest negative heating value. If anything, I would think 0 K/hr might be more suitable for white. I find it very difficult to interpret that color scale and visualization shown.
Line 133: Of course, differences in models used to populate the CSH and SLH LUTs might indeed cause differences in heating, but the different LUT inputs also play a role, and it probably should be mentioned.
Line 224: Is there a reference to be added that supports the claim the LH in stratiform clouds is not important?
Lines 228 onward (first 2 paragraphs of 3.1): it sounds like the authors are arguing that one cannot use CRTM simulated brightness temperature for defining convection (can a reference be provided for this on line 240?). Instead, the argument is made that one must use vertical velocity to define convection. But then, in sections 3.2/3.3, the authors note that they use CRTM brightness temperatures (e.g., lines 266, 275). So, these two parts seem to conflict with each other, and I am confused therefore about what the authors mean when they say they cannot use CRTM brightness temperatures.
Line 250: an altitude of maximum cloud water maybe on average will be correlated with updraft strength, but not instantaneously, and particularly since water condensate falls and is lagged with respect to updraft momentum dynamics. And, overall, I am a bit confused on what the paragraph of discussion about water condensates is trying to convey to the readers.
Line 252: I do not really follow the sentence “Since vertical velocity…”
Line 264: what does a “stable mean LH profile” mean? Is this about sample sizes?
Figure 2: Please convert WRF pressure level to actual pressures (hPa) or heights (km); also, the temperature legend font looks slightly messed up (with question marks appearing after the first Kelvin units).
Table 3 – the maximum precipitation rate for 245K and 250K bins are lower than the mean precipitation rate. Typo?
Minor / Technical Issues:
Below are some small/grammatical corrections I noticed (in the Abstract only). However, as I noted in my general comments, there are many grammar-type issues needing to be addressed throughout the manuscript, and for this review, there were too many to keep track of, so I stopped correcting after the Abstract.
Line 11: suggest adding “mission satellites” after “…Measurement (GPM)”.
Line 12: suggest remove “its”
Line 14: suggest rewriting “using it is that its” as “using these sources is that”
Line 21: suggest adding “the” after “of”, and “the” before “Goddard”.
Citation: https://doi.org/10.5194/amt-2021-97-RC3 -
EC1: 'Editor Comment on amt-2021-97', Brian Kahn, 02 Aug 2021
August 2, 2021
Dear Dr. Lee,
We have reached the end of the discussions phase of your manuscript ‘Latent heating profiles from GOES-16 and its comparison from NEXRAD and GPM”. There are three reviews, one “reject” and two “reconsider after major revisions”. I have also performed my own evaluation of the manuscript.
I recommend against submitting a revised manuscript for consideration of final publication in AMT. The scope of the work required is likely to well exceed the 4-8 week time frame for a revision. While the reviewers (unanimously) make the point that your work is scientifically well motivated and potentially has promise, the investigation is not sufficiently mature enough to be considered ready for publication.
Several of the scientific and algorithmic concerns are mentioned by either two of three, or all three of the reviewers. The algorithm is lacking in quantitative detail, nor has been thoroughly validated with a sufficient set of diverse cases. The authors have not quantified the added value of LH from ABI for the limited set of convective clouds that may contain sufficient information at cloud top. Nor have the authors shown, or even discussed, how the ABI-derived LH retrieval products impact initialization of convective-permitting numerical models.
My best advice is that you and your coauthors carefully consider the feedback from the three reviews as you continue to mature your potentially promising research and submit a future manuscript elsewhere. If you and your coauthors decide to proceed against this advice with a revised manuscript, you are bound to the journal timeline for revisions and will undergo a second round of reviews, running the risk of rejection if the revised version does not satisfactorily and thoroughly address every reviewer concern (which are many and substantial).
Best regards,
Brian Kahn
Associate Editor
Atmospheric Measurement Techniques
Citation: https://doi.org/10.5194/amt-2021-97-EC1 -
AC1: 'Reply on EC1', Yoonjin Lee, 04 Aug 2021
Dear Dr. Kahn,Thank you for your suggestions.We’ve been working on the revision based on reviewers’ comments while the interactive discussion was going on, and we’d like to continue revision if it can be done within 4~8weeks frame.Would it be ok if we continue working for another week to see whether it's achievable or not and decide to continue the revision or withdraw?Best regards,YoonjinCitation: https://doi.org/
10.5194/amt-2021-97-AC1
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AC1: 'Reply on EC1', Yoonjin Lee, 04 Aug 2021
Interactive discussion
Status: closed
-
RC1: 'Comment on amt-2021-97', Anonymous Referee #1, 21 Jun 2021
This manuscript describes an approach for estimating latent heating profiles from high-temporal resolution geostationary satellite observations to fill the need for more frequent observations for assimilation into NWP models. While ground-based radar is currently used for this purpose, such observations are only available over well-instrumented land areas creating gaps in coverage that can negatively impact forecasts. While latent heating has been estimated from low-earth orbiting satellites, these platforms do not provide sufficient temporal resolution to fill this need. Thus the development of a GOES-based algorithm is well-motivated and there is evidence supporting the suggestion that geostationary visible and infrared radiances carry information for identifying convection from which it may be possible to derive approximate latent heating profiles.
Regrettably, while the material is appropriate, the manuscript suffers from several critical flaws that render it unsuitable for publication in Atmospheric Measurement Techniques at this time. The description of the algorithm lacks several important details and a number of important assumptions are insufficiently validated. Only one snapshot is presented as verification of algorithm performance so the conclusions lack justification. In addition, the narrative suffers from numerous grammatical errors that make the manuscript difficult to read and many arguments hard to follow. While this alone would not lead me to reject the paper, when coupled with the scientific flaws, I feel the manuscript is not currently suitable for publication. requires substantial editing before it can be submitted elsewhere for publication. For these reasons, I do not recommend the paper be accepted for publication at this time.
Major Comments
- My primary concern with the study is the fact that the results are insufficient to provide a sufficient assessment of the algorithm performance. A single snapshot from one single convective scene is presented as justification that the approach has merit. Furthermore, the limited results that are presented show some very significant differences between estimates that warrant further investigation and explanation. The overall conclusion from Section 4 appears to be that substantial differences in individual latent heating profile estimates coupled with substantial differences in the areal coverage appear to offset one another to yield area-mean latent heating profiles that are in reasonable agreement in this particular scene. However, very little deeper explanation is conducted to explain these large compensating errors and there are no guarantees that such errors will always offset each other as they do here. Given the very indirect relationship between cloud top properties, model vertical motion, radar reflectivity, and latent heating, substantially more investigation is required to convince the reader that the algorithm is providing reasonable results. In addition to case studies, some statistical analysis of a much larger volume of data needs to be presented.
- Another significant flaw concerns the description of the algorithm itself and verification of the associated assumptions. Section 3.1 notes that growing convection is identified in GOES-16 observations when ‘Tb decrease over ten minutes for two water vapor channels … is greater than the designated threshold’ but neither the channels nor the threshold is specified. Similarly, the paper states ‘For mature convection, the method looks for grid points that have continuously high reflectance, low Tb, and lumpy cloud top over ten minutes’ but no quantitative information is provided regarding the definition of the qualitative terms continuous, low, and lumpy. More importantly, given the goal of reproducing radar-based latent heating estimates, how do convective distributions identified using these definitions compare to the 28 dBZ threshold used by NEXRAD? The only direct comparison provided in the manuscript is a single snapshot in Figure 3 with no quantitative analysis. This lack of verification is amplified in the subsequent assignment of a corresponding vertical motion threshold to mimic these criteria. The only support provided for the 1.5 ms-1 threshold is a table comparing convective areas from ‘observations’ and different vertical velocity thresholds but its not clear what data are used to derive these cases or how well this vertical velocity threshold actually compares to the 28 dBZ radar-based method.
- The description of the cases used to drive the model simulations is also incomplete and no verification of model performance is provided, e.g. against NEXRAD observations.
- The description of the TRMM and GPM algorithms in Section 2.2. is difficult to follow and I’m not sure the non-expert reader would glean even a basic understanding of how these algorithms work from this discussion.
Additional Comments
- The manusrcipt requires substantial editing to improve grammar and readability.
- For completeness in the introduction, Nelson et al (2016) and Nelson and L’Ecuyer (2018) introduce an algorithm analogous to SLH for retrieving latent heating profiles from shallow convection.
- The sentence at the end of Section 1 is not really a complete thought.
- The labels on many figures, especially the panels in Figures 1 and 3 are much too small.
- It is not obvious what all of the variables listed on Line 113 represent.
- It is not clear what is meant by the sentences: ‘Tb at 11.2 which is used to construct the LUT is mostly sensitive to hydrometeors or water vapor. Accordingly the signal received by the channel will be largely from layers with high cloud water contents.’ The 11.2 micron channel is a window channel and not very sensitive to water vapor and the signal received will typically come from the highest cloud layer in the atmosphere not the layer with the highest cloud water contents.
- How well do the values in Table 3 compare to observations? Couldn’t NEXRAD and GOES be combined to examine this.
References
Nelson, E., T. S. L’Ecuyer, S. Saleeby, S van den Heever, and S. Herbener, 2016: Toward an Algorithm for Estimating the Latent Heat Released in Warm Rain, J. Atmos. Oceanic Tech. 33, 1309-1329.
Nelson, E. L. and T. S. L’Ecuyer, 2018: Global character of latent heat release in oceanic warm rain systems, J. Geophys. Res.123, 4797-4817.
Citation: https://doi.org/10.5194/amt-2021-97-RC1 -
CC1: 'Comment on amt-2021-97', Zhengzhao Johnny Luo, 29 Jun 2021
Equation (1): shouldn't it be Q1 - QR, instead of Q1-Q2?
Citation: https://doi.org/10.5194/amt-2021-97-CC1 - RC2: 'Comment on amt-2021-97', Anonymous Referee #3, 02 Jul 2021
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RC3: 'Comment on amt-2021-97', Anonymous Referee #2, 05 Jul 2021
Review for AMT of “Latent heating profiles from GOES-16 and its comparison to heating from NEXRAD and GPM” by Yoonjin Lee et al.
General Comments:
Latent heating (LH) is an important process-level cloud variable. LH retrieval, over the past couple decades, has been largely limited to TRMM, GPM and ground-based radar/NEXRAD (the latter more recently). TRMM and GPM have tropics-wide views, (or near global views for GPM) but also have infrequent revisit times, and thus the temporal resolution of LH retrievals is typically on the order of days. Conversely, ground-based radar retrievals offer increased temporal sampling, though data are not available globally. This paper recognizes the above limitations and aims to develop a retrieval of LH that allows for increased temporal resolution for LH data over a large spatial domain. The retrieval uses GOES input data (and though not stated, the algorithm developed potentially could be applied to multiple geostationary datasets.)
The topic is very appropriate for AMT, and successful LH retrievals will be of use to both the NWP and cloud physics research communities. However, the paper as written requires major revisions largely due to a need for a) more substantial statistical evaluation of the LH retrieval beyond the few cloud snapshot samples discussed; and b) improved clarity of presentation and improved grammar/sentence structure throughout.
Relevant to major revision comment a) above, the authors need to do a much more thorough analysis of the LH retrievals (either via comparison to both NEXRAD and GPM, or just comparison to either NEXRAD or GPM). For example, how do the new LH retrievals compare to GPM CSH over (flat) land vs ocean vs mountains? How about for NEXRAD locations spanning different types of convective regimes? Or, how would results compare as a function of echo top height or surface rainfall rate? There are many ways to slice-and-dice and/or to design an analysis for comparing LH retrievals. Either way, I do not think comparing a handful of cloud snapshots for one 2 deg domain at one time snapshot is sufficient for this publication. I also do not think a new intercomparison analysis would add so much new text and images so as to warrant rejection as the paper currently exists. Regarding major issue b), I strongly recommend a thorough read-through and correction of the English/grammatical structure. After noticing many issues with sentences in most paragraphs of the manuscript, I decided to not focus on grammar past the Abstract (see Technical Issues section below) and instead, I focused mostly on the science and retrieval aspects (see additional comments in “Specific comments” below).
Specific Comments:
Beginning on line 32 (Introduction) and discussion about using LH in NWP: The authors write that LH aims to increase buoyancy in the atmosphere. If one thinks of most deep convection as rooted in the lower atmosphere (or below 2 km), then buoyancy in the lower troposphere is most relevant. However, LH heats the atmosphere to a larger degree as one moves up – in other words, LH is larger at 5 km than it is at 4 km, and larger at 4 km than 3 km, larger at 3 km than 2 km, and so forth. If heating is larger aloft, then LH is doing the exact opposite: it is stabilizing the local atmosphere. And this is what we expect of convective LH – the “job” of convection is to stabilize the atmosphere. Thus, I recommend removing all science text about buoyancy being enhanced by LH as reasons for use in NWP. Instead, I would recommend using the arguments presented in the original conference pre-preprints on using LH in NWP:
https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124540.htm
https://ams.confex.com/ams/88Annual/techprogram/paper_134081.htm
Upon reading those, it is clear that LH – or a perturbation in heating above the surface – allows for increased local surface convergence and local upper-level divergence to be induced. The “forced” local surface convergence in the presence of an already conditionally unstable atmosphere – or, more realistic local vertical circulations – increase convection for regions that are already unstable or conditionally unstable. But, importantly, the LH itself is not causing buoyancy because the vertical height derivative of LH is positive (dLH/dz>0) up to above the melting level (and that differential heating weakens buoyancy).
Line 28: at a few km, convection can be approximately resolved or “permitted” – to resolve convection, one needs a few hundred-meter resolution simulations. See Andreas Prein et al. studies and anything more recent, for example.
Line 58: I believe melting level and the PR/DPR convective-stratiform flags are used for LUT indexing, too?
Line 75: There are orbital level DPR products – they do not provide rapid revisit for any one location, but when they are available, they do not represent a temporal resolution of a day. They represent the instantaneous LH for that type of convection characterized by that surface rainfall rate, ETH, etc. *on average*, so it is not clear what is meant by DPR having a temporal resolution of a day.
Line 91: In Eq. (1), on LHS, should be Q1-QR, not Q1-Q2.
Line 100: the authors have already introduced CSH and SLH acronyms previously.
Lines 108 – 114: it sounds as if only one LH or Q1 profile is provided. CSH also provides convective and stratiform profiles separately since both convection and stratiform cloud types exist simultaneously in a given grid box.
Line 113: what is a “decreasing flag”?
Line 128 and Figure 1: I would suggest improving the color gradation in Fig. 1 so that 0 K/hr is not centered on red and white is not the largest negative heating value. If anything, I would think 0 K/hr might be more suitable for white. I find it very difficult to interpret that color scale and visualization shown.
Line 133: Of course, differences in models used to populate the CSH and SLH LUTs might indeed cause differences in heating, but the different LUT inputs also play a role, and it probably should be mentioned.
Line 224: Is there a reference to be added that supports the claim the LH in stratiform clouds is not important?
Lines 228 onward (first 2 paragraphs of 3.1): it sounds like the authors are arguing that one cannot use CRTM simulated brightness temperature for defining convection (can a reference be provided for this on line 240?). Instead, the argument is made that one must use vertical velocity to define convection. But then, in sections 3.2/3.3, the authors note that they use CRTM brightness temperatures (e.g., lines 266, 275). So, these two parts seem to conflict with each other, and I am confused therefore about what the authors mean when they say they cannot use CRTM brightness temperatures.
Line 250: an altitude of maximum cloud water maybe on average will be correlated with updraft strength, but not instantaneously, and particularly since water condensate falls and is lagged with respect to updraft momentum dynamics. And, overall, I am a bit confused on what the paragraph of discussion about water condensates is trying to convey to the readers.
Line 252: I do not really follow the sentence “Since vertical velocity…”
Line 264: what does a “stable mean LH profile” mean? Is this about sample sizes?
Figure 2: Please convert WRF pressure level to actual pressures (hPa) or heights (km); also, the temperature legend font looks slightly messed up (with question marks appearing after the first Kelvin units).
Table 3 – the maximum precipitation rate for 245K and 250K bins are lower than the mean precipitation rate. Typo?
Minor / Technical Issues:
Below are some small/grammatical corrections I noticed (in the Abstract only). However, as I noted in my general comments, there are many grammar-type issues needing to be addressed throughout the manuscript, and for this review, there were too many to keep track of, so I stopped correcting after the Abstract.
Line 11: suggest adding “mission satellites” after “…Measurement (GPM)”.
Line 12: suggest remove “its”
Line 14: suggest rewriting “using it is that its” as “using these sources is that”
Line 21: suggest adding “the” after “of”, and “the” before “Goddard”.
Citation: https://doi.org/10.5194/amt-2021-97-RC3 -
EC1: 'Editor Comment on amt-2021-97', Brian Kahn, 02 Aug 2021
August 2, 2021
Dear Dr. Lee,
We have reached the end of the discussions phase of your manuscript ‘Latent heating profiles from GOES-16 and its comparison from NEXRAD and GPM”. There are three reviews, one “reject” and two “reconsider after major revisions”. I have also performed my own evaluation of the manuscript.
I recommend against submitting a revised manuscript for consideration of final publication in AMT. The scope of the work required is likely to well exceed the 4-8 week time frame for a revision. While the reviewers (unanimously) make the point that your work is scientifically well motivated and potentially has promise, the investigation is not sufficiently mature enough to be considered ready for publication.
Several of the scientific and algorithmic concerns are mentioned by either two of three, or all three of the reviewers. The algorithm is lacking in quantitative detail, nor has been thoroughly validated with a sufficient set of diverse cases. The authors have not quantified the added value of LH from ABI for the limited set of convective clouds that may contain sufficient information at cloud top. Nor have the authors shown, or even discussed, how the ABI-derived LH retrieval products impact initialization of convective-permitting numerical models.
My best advice is that you and your coauthors carefully consider the feedback from the three reviews as you continue to mature your potentially promising research and submit a future manuscript elsewhere. If you and your coauthors decide to proceed against this advice with a revised manuscript, you are bound to the journal timeline for revisions and will undergo a second round of reviews, running the risk of rejection if the revised version does not satisfactorily and thoroughly address every reviewer concern (which are many and substantial).
Best regards,
Brian Kahn
Associate Editor
Atmospheric Measurement Techniques
Citation: https://doi.org/10.5194/amt-2021-97-EC1 -
AC1: 'Reply on EC1', Yoonjin Lee, 04 Aug 2021
Dear Dr. Kahn,Thank you for your suggestions.We’ve been working on the revision based on reviewers’ comments while the interactive discussion was going on, and we’d like to continue revision if it can be done within 4~8weeks frame.Would it be ok if we continue working for another week to see whether it's achievable or not and decide to continue the revision or withdraw?Best regards,YoonjinCitation: https://doi.org/
10.5194/amt-2021-97-AC1
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AC1: 'Reply on EC1', Yoonjin Lee, 04 Aug 2021
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