the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Discriminating between "Drizzle or rain" and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Abstract. The highly sensitive Ka-band cloud radar at the Barbados Cloud Observatory (BCO) frequently reveals radar reflectivities below -50 dBZ within updrafts and below the cloud base of cumulus clouds. These so-called haze echoes are signals from hygroscopically grown sea salt aerosols. Within the Cloudnet target classification scheme, haze echoes are generally misclassified as precipitation ("Drizzle or rain"). We present a technique to discriminate between "Drizzle or rain" and sea salt aerosols in Cloudnet that is applicable to marine Cloudnet sites. The method is based on deriving heuristic probability functions utilizing a combination of cloud radar reflectivity factor, radar mean Doppler velocity and ceilometer attenuated backscatter coefficient. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for the measurements over the BCO. The results are validated against the amount of precipitation detected by the Virga-Sniffer tool. We analyze data for the measurements in the vicinity of the BCO covering two years (July 2021–July 2023) as well as during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field experiment that took place in Jan–Feb 2020. A first-ever statistical analysis of the Cloudnet target classification product including the new "haze echo" target over two years at the BCO is presented. In the atmospheric column above the BCO, “Drizzle or rain" is on average more frequent during the dry season compared to the wet season, due to the higher occurrence of warm clouds contributing to the amount of precipitation. Haze echoes are identified about four times more often during the dry season compared to the wet season. The frequency of occurrence of "Drizzle or rain" in Cloudnet caused by misclassified haze echoes is overestimated by up to 16 %. Supported by the Cloudnet statistics and the results obtained from the Virga-Sniffer tool, 48 % of detected warm clouds in the dry and wet season precipitate. The proportion of precipitation evaporating fully before reaching the ground (virga) is higher during the dry season. During EUREC4A, precipitation from warm clouds was found to reach the ground more frequently over the RV Meteor compared to the BCO.
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RC1: 'Comment on egusphere-2024-894', Anonymous Referee #1, 02 Jul 2024
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The authors present a new method to detect hygroscopically grown sea salt aerosol below Trade wind cumuli bases based on the Cloudnet suite of remote sensing instruments. The method improves the Cloudnet target classification scheme which currently mis-labels grown aerosols (haze) as „drizzle or rain“. After summarizing state-of-the-art and presenting the instrumentation at Barbados Cloud Observatory (BCO) as well as the Cloudnet algorithm, the authors introduce their new classification methodology as well as a cloud type classification algorithm.
Applying their method to 2 years of BCO measurements and the EUREC4A period, they perform a statistical analysis of Cloudnet target classification occurrence, analyze controlling environmental factors, and evaluate the performance of their new classification class with labels obtained from the Virga-Sniffer method available in Kalesse-Los et al, 2023. The authors also present statistics of precipitation and virga at BCO, and discuss limitations of their new method.
The paper is well written and I acknowledge the early career status of the first author. I am making some general and more specific comments below to help clarifying the main message of the paper, and strengthening the presented argumentation.
General comments:
- GC1: The introduction summarizes that radar reflectivity thresholds are often used to exclude haze echoes from drizzle/rain occurrence analyses (L 71-73). The manuscript in its current state, however, does not clarify in what way the new haze category in Cloudnet improves or changes occurrence statistics compared to the Ze-threshold method (eg Klingebiel et al, 2019). I would propose to add a comparison to the analysis Section (also see comment below).
- GC2: The authors need to clarify where to find the data sets that they used for their analysis. Many datasets, especially those obtained during the EUREC4A period, are publically available with dois; were these data sets used?
- GC3: Sec 3.1, L247 - 259: I am missing the reasons for the choice of µ, sigma, beta, and a justification for how they were optimized and set. The authors should clarify how sensitive the analyses are to these settings including the probability threshold of 60% (L261). How do users of the method would need to adjust these settings for different maritime Cloudnet sites (or is it “plug and play“?)?
- GC4: I find the argumentation line of the analysis at times hard to follow. In order to be more convincing of the new method, I would propose to adapt the structure as follows:
- 4 i) apply method to BCO statistics and evaluate new approach by comparing to Virga-Sniffer and traditional -50dBZ threshold in order to highlight benefits of new classification scheme for haze detectio; include an analysis of limitations using spectra/skewness (also see Specific comments below) and sensitivity to parameters (see comment above)
- 4 ii) analyze driving factors of haze occurrences in water vapor or subsidence space (see comment below)
- (optional:) 4iii) make use of Cloudnet and Virga-Sniffer to analyze virga and precip statistics at BCO given the improved detection scheme excluding mis-labeled haze)- GC5: the main message of the paper should be clarified throughout the manuscript. Is the main scope of the manuscript to introduce a new Cloudnet classification scheme? Or, rather, to analyze rain and virga characteristics at BCO given an optimized detection method? Abstract and introduction rather focus on the novel Cloudnet method, while the main scope of the analysis Section seems to focus on BCO statistics.
Specific comments:
- L 21-26: The importance of evaporation and moistening processes in the sub-cloud layer for cloud and precipitation evolution should be highlighted here.
- L24: A reference should be added.
- L177: To my knowledge, operation of the CORAL Radar and ceilometer continued at BCO after the EUREC4A campaign (http://bcoweb.mpimet.mpg.de/systems/data_availability/DeviceAvailability.html, last access July 2, 24)
-L178: the authors should clarify why this data is not usable and why timestamps cannot be corrected in post-processing.
- Sec 2.2.1 and Sec. 2.2.2; L 569: it remains unclear to me throughout the manuscript how MWR and MRR data impact the classification algorithm. Are they mandatory for the new classification class? The HATPRO in operation at the time of analysis is the BCOHAT instrument as specified in Schnitt et al, 2024, ESSD (doi.org/10.5194/essd-16-681-2024) (reference missing)
- Fig 3: I would suggest to add boxes in (a) which illustrate the zoomed areas in (b) and (c); and to maybe re-configure the plot such that (a) is largest on the left side, and (b) and (c) are smaller and connected to boxes in (a)
- Fig 4 : in order to highlight the added value of the new classification class compared to the conventional Ze threshold method (L70), I would propose to add a panel on the top to illustrate the measured radar reflectivity.
- L205: arguments for why m and c were chosen this way should be added here. How sensitive is the clutter mask and resulting analysis and evaluation to these values? I suspect that the presented evaluation of the Cloudnet class with the Virga-Sniffer strongly depends on the values chosen here.
- L205: do the authors refer to the sensitivity limit of the CORAL radar? If so, this limit should scale with range, and should be negative. If not, a clarification is needed here.
- L232: a sentence should be added on how the insect detection scheme works and why it would be suitable for also detecting haze; this information should also be added to Sec 2.3. Why not including the Virga-Sniffer method to Cloudnet instead or in addition as it uses similar instruments? The advantages of the chosen method compared to the Virga-Sniffer should be highlighted.
- L290: doubles L261.
- L 311: more explanation is needed for why it would be important and interesting to split the analysis in the two cloud classes; this should be stated already in the introduction as well.
- Sec 4.1: Rather than splitting the analysis into dry and wet season statistics for each year, an occurrence analysis could be performed in subsidence or water vapor space for both years to exclude for example skewing wet intrusions in the dry season from the statistics. The EUREC4A period could be used to analyze driving factors in more detail, such as cloud organization type, cloud type, wind direction, wind speed, and to include the impact of Saharan dust events on haze occurrence (which is mentioned in L403 but not shown).
- Sec 4.3: The authors should clarify why they are comparing BCO and the Meteor observations; I am confused - did the authors also run Cloudnet based on the Meteor? If so, additional input is needed in Sec 2 and the introduction. Maybe the authors rather use the comparison to optimize the application of the Virga-Sniffer to BCO measurements in which case the text needs to be clarified to underline this.
- L425 and Fig 9: The text should comment on the large occurrence of ‘Unclassified‘ aboard the Meteor compared to the BCO; and should summarize why the difference between object- and profile-based statistics is particularly profound for the trade wind cumulus class in panel (b) compared to the warm clouds class in panel (a) (which is hinted at in L434, and extensively analysed in Appendix C, but should be summarized here)
- Sec 4.3.2 As I understand the analysis presented here, the Virga-Sniffer is applied to BCO measurements and occurrence statistics of virga, precipitation and clouds are analysed. I am not sure how this Section relates to the title of the manuscript, as the Cloudnet haze method is not included in this Section (also see GC 4 and 5)
- L476: it should be clarified if Virga-Sniffer results are shown, or Cloudnet classification results; also see comment above
- L502: Spectra and higher moments are available at MPI for the analysed period and should be used in the analysis to strengthen the proposed method, or, at least, to quantify the limitations more thoroughly. Could the classification scheme be adapted to include skewness as an additional proxy for detecting haze?
Technical Corrections:
- Fig 2: All colors seem to be related to 24h data coverage; the colorbar should be adjusted to enhance the Figure‘s message.
- Fig 8: Legend should be adjusted to distinguish solid and dashed line without reading the caption.
- Fig 9 caption: last sentence should be moved to main body text.
- Figs 7-10: description of colors shown in legends need to be added to the captions.
Citation: https://doi.org/10.5194/egusphere-2024-894-RC1
Model code and software
Cloudnet haze echoes Johanna Roschke https://doi.org/10.5281/zenodo.10469906
Cloud classification Johanna Roschke https://doi.org/10.5281/zenodo.10471932
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