Attribution of riming and aggregation processes by application of the vertical distribution of particle shape (VDPS) and spectral retrieval techniques to cloud radar observations

Teisseire, Audrey; Billault-Roux, Anne-Claire; Vogl, Teresa; Seifert, Patric

doi:https://doi.org/10.5194/egusphere-2024-2711

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https://doi.org/10.5194/egusphere-2024-2711

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11 Oct 2024

| 11 Oct 2024

Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Attribution of riming and aggregation processes by application of the vertical distribution of particle shape (VDPS) and spectral retrieval techniques to cloud radar observations

Audrey Teisseire, Anne-Claire Billault-Roux, Teresa Vogl, and Patric Seifert

Abstract. Advancing the understanding of mixed-phase cloud microphysical growth processes requires a thorough detection of the transition processes from pristine hydrometeor states toward aggregates, rimed particles and graupel. In this study, a versatile combination of techniques is applied to detect and characterize aggregated and strongly rimed hydrometeors even under harsh atmospheric conditions such as the presence of orographic gravity waves. The approach combines dual-frequency observations from vertical-stare Doppler cloud radars as well as measurements from a polarimetric scanning cloud radar. Core of the approach are profiles of the Vertical Distribution of Particle Shape (VDPS) method that serve as a proxy for the presence of columnar, isometric, or prolate cloud particles. At height levels within the VDPS-based shape profiles where isometric particles are identified, Doppler spectra and dual-wavelength vertical-stare cloud radar observations are used to discriminate the occurrence of aggregation or graupel formation.

The underlying dataset was acquired in the framework of the 3-year field experiment, “Dynamic Aerosols Clouds and Precipitation Observation in the Pristine Environment in the Southern Ocean” (DACAPO-PESO) at the southern hemispheric midlatitude site of Punta Arenas, Chile (53° S, 71° W). The frequent presence of layers of supercooled liquid water and the permanent occurrence of orographic gravity waves motivate a strong interest to understand the formation of precipitation and the role of aggregation and riming at this site. Therefore, two case studies of both strong riming events and aggregation processes from the DACAPO-PESO campaign are presented to demonstrate the potential of combining the new VDPS retrieval with spectral methods, which analyze particle fall velocity and the coexistence of multiple particle types. We found that the identification of layers of supercooled liquid water is essential to pin down regions of riming in the observed cloud systems. In consequence, considering the general notion of the excess of liquid water in clouds over the southern hemisphere midlatitudes, our study serves as a preliminary investigation into the occurrence of riming and aggregation processes above Punta Arenas.

Received: 01 Sep 2024 – Discussion started: 11 Oct 2024

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Audrey Teisseire, Anne-Claire Billault-Roux, Teresa Vogl, and Patric Seifert

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RC1:
'Comment on egusphere-2024-2711', Anonymous Referee #1, 15 Nov 2024 reply
This paper presents a multi-faceted approach for identifying aggregation and advanced riming leading to graupel formation using ground-based remotely-sensed observations from the DACAPO-PESO experiment in Punta Arenas, Chile. The paper is well-written and the figures are produced to a high quality. The analysis is thoroughly described and grounded in the literature. The results comprise four case studies, two indicative of riming of graupel and two of aggregation of low-density snowflakes in the absence of riming. My main comments are focused only on illustrating and highlighting the interpretation of the case studies with some changes to the figures and discussion of the results.

This is a high-quality paper, providing novel results based on recently-developed techniques, which help to better understand the remote-sensing signatures of key microphysical processes. I recommend this paper for publication in AMT, with only minor revisions.

Comments:
Each case study uses three figures illustrating: (Figure A) the time-series of zenith-pointing radar and lidar measurements, overlaid with temperature contours from the ECMWF analysis; (Figure B) RHI scans of SLDR and vertical distribution of the polarizability ratio from a short period within the case study, and (Figure C) Doppler spectra and profiles of reflectivity from a selected profile during the period relating to Figure B.

The detailed discussion of the results requires frequent references to all of these figures, including specific height and temperature ranges, across the multiple figures, which each may have different ranges in the height axis.

A few additional touches to the figures would greatly assist the reader in this cross-referencing and interpretation:
Please annotate on Figure A the time period of the RHI scans and profiles used in Figures B and C

On Figures B and C, please include information about the critical temperature ranges which help us to interpret which processes are most likely: I leave it to you to decide how to show this (e.g. duplicate y-axes, or an additional profile added to panel b) in both figures or an additional panel, or simply annotating selected height levels with the corresponding temperature)

When specific layers are frequently referred to in the text, consider annotating these layers in Figures B and C

Please also consider adding subtitles to the panels of each figure to assist the reader when jumping back-and-forth between figures.

2. Some aspects of the interpretation of the case studies could be clarified and expanded upon:
In Section 4.1.2 the lowest layer is interpreted as “large aggregates…falling into a layer of supercooled liquid droplets and… forming smaller but more dense and isometric particles such as graupel”. The possible mechanisms for a decrease in the size of the particles are not commented on. Are the low-density aggregates breaking up during the riming process? Does the turbulence near cloud-base indicate mixing with sub-saturated air leading to sublimation-driven breakup?

In Section 4.2.1 the lowest layer, apparently containing spheroidal/low-density aggregates according to VDPS method (Figure 8b), is mostly discussed in terms of ruling out the presence of supercooled liquid and therefore of riming; however, the other observations characterising this layer include a very broad Doppler spectrum (Figure 9a) and indications of turbulence in the Doppler velocity (Figure 7c), as well as a rapid reduction of radar reflectivity (Figure 9b) toward cloud-base. Please comment on the interpretation of these features.

Typos and minor comments:
L4: “harsh atmospheric conditions such as the presence of orographic gravity waves”. Not sure if “harsh conditions” is the right word here; perhaps “adverse” with respect to Doppler-velocity based methods.

L5-6: “Core of the approach…” could be either “At the core of the approach…” or “Core to the approach…”

L39: “low” instead of “small density”

L46: “...by a lack of a efficient ice nucleating particles…” may have a missing word or a typo here.

L118: “...94-GHz radar and operates at 3.2-millimeter wavelength.” This is already implicit in the 94-GHz frequency, so could read more like “...94-GHz radar (3.2-millimeter wavelength).”

L269: “...by the presence low values of…” missing “of”

L300: should be “...provide evidence of…”

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Citation: https://doi.org/10.5194/egusphere-2024-2711-RC1

Audrey Teisseire, Anne-Claire Billault-Roux, Teresa Vogl, and Patric Seifert

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Short summary

This study demonstrates the ability of the VDPS method, delivering the vertical distribution of particle shape, to highlight riming and aggregation processes, identifying graupel and aggregates, respectively, as isometric particles. The distinction between these processes can be achieved using lidar or spectral techniques, as demonstrated in the case studies. The capability of the VDPS method to identify rimed particles and aggregates without differentiating them can simplify statistical work.


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