the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing Arctic low-level clouds and precipitation from above – a radar perspective
Imke Schirmacher
Pavlos Kollias
Katia Lamer
Mario Mech
Lukas Pfitzenmaier
Manfred Wendisch
Susanne Crewell
Abstract. Most Arctic clouds occur below 2 km altitude as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low- level cloud fraction, which is the amount of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from downlooking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC has been operated during four aircraft campaigns taking place in the vicinity of Svalbard during different times of the year and covering more than 25,000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions.
A forward simulator converts MiRAC measurements to synthetic CloudSat radar reflectivities. These forward simulations are compared with the original CloudSat observations for four satellite underflights to prove the suitability of our forward- simulation approach. Above CloudSat’s blind zone of 1 km and below 2.5 km, the forward simulations reveal that CloudSat would overestimate the MiRAC cloud fraction over all campaigns by about 6 percent points (pp) due to its horizontal resolution, by 12 pp due to its range resolution, and underestimate it by 10 pp due to its sensitivity. Especially during cold air outbreaks over open water, high reflectivity clouds appear below 1.5 km, which are stretched by CloudSat’s pulse length causing the forward-simulated cloud fraction to be 16 pp higher than that observed by MiRAC. The pulse length merges multilayer clouds, whereas thin low-reflectivity clouds remain undetected. Consequently, 48 % of clouds observed by MiRAC belong to multilayer clouds, which reduces by a factor of 4 for the forward-simulated CloudSat counterpart. Despite the overestimation between 1 and 2.5 km, the overall low-level cloud fraction is strongly reduced due to CloudSat’s blind zone that misses a cloud fraction of 32 % and half of the total (mainly light) precipitation amount.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(6183 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6183 KB) - Metadata XML
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- Final revised paper
Journal article(s) based on this preprint
Imke Schirmacher et al.
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-636', Anonymous Referee #1, 05 Jun 2023
I commend the authors on submitting such a nicely organized and written manuscript that presents impactful scientific results. The manuscript was economical, easy to read, conveyed results in a logical order, and included well crafted figures that were easy to digest. I quite enjoyed consuming this manuscript.
On a purely scientific level, this manuscript presents important findings that quantify CloudSat spaceborne radar deficiencies in the lowest atmospheric layers above ground level when compared to high resolution airborne radar observations that effectively sample the boundary layer and CloudSat blind zone. Cold air outbreak (CAO) conditions are particularly highlighted in this study since they produce shallow convective cloud features that might not be sampled well by CloudSat observations. CloudSat observations and products are instrumental model evaluation datasets, but contain known deficiencies. This study should instigate further research that will enable CloudSat products to be improved by accounting for blind zone features that are systematically undersampled (e.g., cloud and precipitation occurrence and blind zone evolution).
I recommend that the manuscript be published after some very minor issues are addressed. I would be happy to review the manuscript again if needed, but also defer to the editor’s judgement on whether the following minor comments warrant another review.
1. Line 93: Are the SIC thresholds chosen arbitrarily, or based on previous studies? Relatedly, how are these percentages calculated (I assume daily AMSR2 SIC products)?
2. Lines 414-416: While I think it is totally acceptable to apply the Maahn et al. (2014) Z-S relation to the MiRAC observations for back-of-the-envelope calcuations, it is likely that the rosette habit assumption used to derive Z-S does not translate very well to the microphysical composition of oceanic snow-producing clouds generated under CAO conditions, especially when comparing snow event categories differentiated by snowrate intensity. Acknowledging this methodological shortcoming is advised, but its overall effect does not detract from the larger message conveyed in the manuscript.
3. Line 432: ClaudSat → CloudSat
4. Line 470: Kulie et al. (2016) and Kulie and Milani (2018) partition CloudSat-observed snow events by “shallow” and “deep” categories, with special emphasis on high latitude regions prone to CAO’s. They highlight the light nature of shallow snow in CAO regions with appropriate (but unresolved) blind zone related caveats. This study clearly indicates that CloudSat estimated snowfall occurrence and rate/amount are significantly impacted by blind zone limitations that hamper efforts to quantify snowfall with the best available spaceborne instruments.
5. General comment: It might be worth mentioning that a combined CloudSat/CALIPSO product exists that will more successfully identify low-level cloud structures compared to the CloudSat 2B-Geoprof product.
6. General comment: Mateling et al. (2023; JGR) was just published. It focuses on CAO snowfall production in the North Atlantic Ocean using CloudSat products - another highly relevant manuscript that would benefit from the information gained from the current study.
Citation: https://doi.org/10.5194/egusphere-2023-636-RC1 - AC1: 'Reply on RC1', Imke Schirmacher, 30 Jun 2023
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RC2: 'Comment on egusphere-2023-636', Anonymous Referee #2, 13 Jun 2023
Review on the manuscript:
Assessing Arctic low-level clouds and precipitation from above – a radar perspective
by I. Schirmacher et al.
This paper presents a study on the evaluation of CloudSat measurements in the very low altitude levels based on collocated airborne radar measurements in arctic clouds.
The method consists in applying a forward-simulation methodology to high resolution airborne radar measurements (MIRAC instrument) to convert these observations in a “CloudSat mode” and then assess the CloudSat measurements.
The authors used first 4 collocated flights with CloudSat track to evaluate and validate their methodology. Then, they used airborne radar observations from 4 airborne campaigns in the Arctic region to evaluate CloudSat limitations.
Surface conditions (open water or ice) and meteorological conditions (cold air outbreak index and circulation weather type) are taking into account in the analysis of the results.
It is a very well-known issue that CloudSat measurements are limited near the ground, which implies very large uncertainties when deriving snowfall amount at ground level on a global scale. This study propose a nice methodology to assess this issue.
The results of this study are very important and should be take into account for the future studies using CloudSat products (reflectivity, snowfall rate or amount) near the surface.
The paper is very well organized, well written and easy to read.
In particular, the methodology is clearly and concisely described in the section 2.3, even if the figure 4 in the further section 4.1 helps to visualize the effect of the four steps.
I recommend this paper for publication, after answering the following minor comments and corrections:
- Line 142-143: Please rephrase the end of the sentence
- Line 432 : replace “ClaudSat” by “CloudSat”
- Figure 3: You could add the histogram with all data (separated by CWT)?
- Figure 7 and lines 288-289: I suppose the 2 dB difference you mention between Zsim and ZC comes from a linear fitting ? I suggest you add it on the figure.
- Line 410: what is the assumption of crystal habit for Z-S law in the 2C-Snow-Profile product? Please add a few details on this product to facilitate de further discussion on the differences.
- Figure 12: I suggest adding the profile of SM along that of AM,norm.
Citation: https://doi.org/10.5194/egusphere-2023-636-RC2 - AC2: 'Reply on RC2', Imke Schirmacher, 30 Jun 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-636', Anonymous Referee #1, 05 Jun 2023
I commend the authors on submitting such a nicely organized and written manuscript that presents impactful scientific results. The manuscript was economical, easy to read, conveyed results in a logical order, and included well crafted figures that were easy to digest. I quite enjoyed consuming this manuscript.
On a purely scientific level, this manuscript presents important findings that quantify CloudSat spaceborne radar deficiencies in the lowest atmospheric layers above ground level when compared to high resolution airborne radar observations that effectively sample the boundary layer and CloudSat blind zone. Cold air outbreak (CAO) conditions are particularly highlighted in this study since they produce shallow convective cloud features that might not be sampled well by CloudSat observations. CloudSat observations and products are instrumental model evaluation datasets, but contain known deficiencies. This study should instigate further research that will enable CloudSat products to be improved by accounting for blind zone features that are systematically undersampled (e.g., cloud and precipitation occurrence and blind zone evolution).
I recommend that the manuscript be published after some very minor issues are addressed. I would be happy to review the manuscript again if needed, but also defer to the editor’s judgement on whether the following minor comments warrant another review.
1. Line 93: Are the SIC thresholds chosen arbitrarily, or based on previous studies? Relatedly, how are these percentages calculated (I assume daily AMSR2 SIC products)?
2. Lines 414-416: While I think it is totally acceptable to apply the Maahn et al. (2014) Z-S relation to the MiRAC observations for back-of-the-envelope calcuations, it is likely that the rosette habit assumption used to derive Z-S does not translate very well to the microphysical composition of oceanic snow-producing clouds generated under CAO conditions, especially when comparing snow event categories differentiated by snowrate intensity. Acknowledging this methodological shortcoming is advised, but its overall effect does not detract from the larger message conveyed in the manuscript.
3. Line 432: ClaudSat → CloudSat
4. Line 470: Kulie et al. (2016) and Kulie and Milani (2018) partition CloudSat-observed snow events by “shallow” and “deep” categories, with special emphasis on high latitude regions prone to CAO’s. They highlight the light nature of shallow snow in CAO regions with appropriate (but unresolved) blind zone related caveats. This study clearly indicates that CloudSat estimated snowfall occurrence and rate/amount are significantly impacted by blind zone limitations that hamper efforts to quantify snowfall with the best available spaceborne instruments.
5. General comment: It might be worth mentioning that a combined CloudSat/CALIPSO product exists that will more successfully identify low-level cloud structures compared to the CloudSat 2B-Geoprof product.
6. General comment: Mateling et al. (2023; JGR) was just published. It focuses on CAO snowfall production in the North Atlantic Ocean using CloudSat products - another highly relevant manuscript that would benefit from the information gained from the current study.
Citation: https://doi.org/10.5194/egusphere-2023-636-RC1 - AC1: 'Reply on RC1', Imke Schirmacher, 30 Jun 2023
-
RC2: 'Comment on egusphere-2023-636', Anonymous Referee #2, 13 Jun 2023
Review on the manuscript:
Assessing Arctic low-level clouds and precipitation from above – a radar perspective
by I. Schirmacher et al.
This paper presents a study on the evaluation of CloudSat measurements in the very low altitude levels based on collocated airborne radar measurements in arctic clouds.
The method consists in applying a forward-simulation methodology to high resolution airborne radar measurements (MIRAC instrument) to convert these observations in a “CloudSat mode” and then assess the CloudSat measurements.
The authors used first 4 collocated flights with CloudSat track to evaluate and validate their methodology. Then, they used airborne radar observations from 4 airborne campaigns in the Arctic region to evaluate CloudSat limitations.
Surface conditions (open water or ice) and meteorological conditions (cold air outbreak index and circulation weather type) are taking into account in the analysis of the results.
It is a very well-known issue that CloudSat measurements are limited near the ground, which implies very large uncertainties when deriving snowfall amount at ground level on a global scale. This study propose a nice methodology to assess this issue.
The results of this study are very important and should be take into account for the future studies using CloudSat products (reflectivity, snowfall rate or amount) near the surface.
The paper is very well organized, well written and easy to read.
In particular, the methodology is clearly and concisely described in the section 2.3, even if the figure 4 in the further section 4.1 helps to visualize the effect of the four steps.
I recommend this paper for publication, after answering the following minor comments and corrections:
- Line 142-143: Please rephrase the end of the sentence
- Line 432 : replace “ClaudSat” by “CloudSat”
- Figure 3: You could add the histogram with all data (separated by CWT)?
- Figure 7 and lines 288-289: I suppose the 2 dB difference you mention between Zsim and ZC comes from a linear fitting ? I suggest you add it on the figure.
- Line 410: what is the assumption of crystal habit for Z-S law in the 2C-Snow-Profile product? Please add a few details on this product to facilitate de further discussion on the differences.
- Figure 12: I suggest adding the profile of SM along that of AM,norm.
Citation: https://doi.org/10.5194/egusphere-2023-636-RC2 - AC2: 'Reply on RC2', Imke Schirmacher, 30 Jun 2023
Peer review completion
Journal article(s) based on this preprint
Imke Schirmacher et al.
Imke Schirmacher et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6183 KB) - Metadata XML