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Question

1. What is the Vertical Distribution of Particle Shape (VDPS) method and how does it contribute to the understanding of cloud microphysical processes?

2. What are the technical characteristics and modifications of the SLDR-mode 35-GHz cloud radar Mira-35 used in the CyCARE campaign in Limassol, Cyprus?

3. How does the VDPS method combine simulated and measured values?

4. How do polarimetric gradients influence particle shape visibility?

Accepted Answer

The Vertical Distribution of Particle Shape (VDPS) method is a simplified and versatile approach developed for the retrieval of the polarizability ratio using data from a SLDR-mode 35-GHz scanning cloud radar. This method concentrates on the polarizability ratio, which is considered more relevant for investigating cloud microphysical processes compared to the degree of orientation. By using observations of SLDR, the VDPS method enables a broader field of application. The method was introduced in the Cyprus Clouds Aerosols and pRecipitation Experiment (CyCARE) field campaign, where a newly configured SLDR-mode 35-GHz cloud radar was deployed in Limassol, Cyprus. The VDPS method allows for the derivation of particle shape, providing valuable insights into the evolution of ice particles from pristine state to aggregates and rimed particles. This contributes to the understanding of cloud microphysical processes, such as the formation of ice crystals, precipitation transition, and the impact of different processes on cloud systems. The method has been demonstrated to successfully track the evolution of particle shape, including isometric particles, columnar crystals, and dendrites, through case studies. Overall, the VDPS method offers a valuable tool for monitoring cloud systems, expanding the understanding of microphysical properties, and supporting the improvement of representation in numerical models.

Accepted Answer

The central instrument for the present study is a modified version of the 35-GHz cloud radar Mira-35, which is operated in SLDR-mode. The SLDR-mode cloud radar was implemented based on the conventional Linear Depolarization Ratio (LDR)-mode by 45 * rotation of the antenna system around the emission direction. The properties of the standard LDR mode Mira-35 are elaborated in detail in Gorsdorf et al. (2015). The technical characteristics of MIRA-35 used in the CyCARE campaign in Limassol, Cyprus, are detailed in Table 1. The standard vertical-stare LDR-mode allows only to discriminate between hydrometeors with an isometric intersection and with a columnar intersection. In order to optimize the Mira-35 cloud radar for improved measurements of hydrometeor shape and orientation, two modifications were applied. First, the cloud radar was mounted onto a positioner platform which allows for a freely definable position of the radar within a half sphere given by 360 * of azimuth and 180 * of elevation. Second, a 45 * rotation of the receiver antenna around the emission direction was applied. This operation mode, in general defined as SLDR-mode, has specific advantages in studies of the intrinsic relationship between the polarimetric signature of the particle shape and radar elevation angle. In contrast to the standard LDR mode, variations in the orientation of hydrometeors only have small effects on the measured SLDR (d s) even at low elevation angles. The radar was steered toward geographic south direction and performed range-height-indicator (RHI) scans from 90 * (zenith pointing) to 150 * , corresponding to 30 * elevation over the horizon toward north direction. The primary measurement parameters are the complex Doppler spectra received by the detectors in the co-and cross-channel with respect to the emitted polarization plane Sco (o k ) and Scx (o k ), respectively. The raw spectra of d s and r s are subject to noise artifacts, and a noise filtering is performed to remove values which are below a given threshold value. The leakage of a fraction of signal from the co channel into the cross channel was found to be as low as -31 dB, which is not possible to detect and is entirely caused by the depolarization channel decoupling.

Accepted Answer

The VDPS method combines simulated and measured values of d s at only the two elevation angles of 90deg and 150deg. This approach allows for shorter scan cycles compared to full RHI scans used in earlier studies. By utilizing polarimetric measurements at different elevation angles, the VDPS method ensures horizontal homogeneity of observed clouds. The combination of experienced-eye evaluation and a minimum number of data points in each layer further enhances the accuracy of the method. Overall, the VDPS method provides a tailored retrieval of the vertical distribution of particle shape, making it a valuable tool for researchers in the field.

Accepted Answer

Polarimetric gradients, specifically d s and r s parameters, are most visible when the elevation angle difference of scans is large. By using values at 90 * and 150 * elevation angles, different particle shapes can be distinguished. The VDPS algorithm automatically searches for data points with minimum and maximum elevation angles to calculate fit values of d s. A 3rd degree polynomial fit is used for a more accurate representation. These fit values, d s (th min) and d s (th max), are then utilized for evaluating against the spheroidal scattering model. Additionally, mean gradients ds th and rs th are calculated using linear regression to define the primary particle shape class.

Accepted Answer

Modeled ds are compared to measured ds by calculating d s (th min ) and d s (th max ) from direct measurements at th min and th max. The 95% prediction interval of the polynomial fit is used to determine the potential range of intersection. Isolines c. [dB] [dB] of d s (th min ) +- the 95% interval prediction, and d s (th max ) +- the 95% interval prediction are identified in the modeled fields of ds at th min and th max. Intersections between ds (th min ) and ds (th max ) are found in the model output, and the associated values of x and k are identified. If no intersection is found, the algorithm searches for the point where the difference between ds (th min ) and ds (th max ) is the lowest. The x-axis positions (polarizability ratio x) are characterized by deriving the mean and standard deviation of all overlapping data points included in each intersection. The values of x for each intersection are saved at each height iteration, and the appropriate particle shape class is identified based on the remaining, corresponding value of polarizability ratio.

Accepted Answer

Primary particle shape classes are identified and quantified by comparing the modeled and observed data from case studies representing the three particle shape classes. Thresholds of ds th and rs th are determined to allow for an unambiguous separation of the prolate, oblate, and isometric hydrometeor shape classes. The actual shape class selection criteria are summarized in Table 2. If the slopes of ds th and rs th exceed lim ds and lim rs, respectively, particles are assigned to the oblate primary particle shape class. If the slope of only rs th exceeds lim rs and d s is relatively high, particles are assigned to the prolate primary particle shape class. If both gradients are below lim ds and lim rs for both polarimetric parameters, particles are associated to the isometric primary particle shape class. The error bars are calculated based on the intersections of the standard deviation obtained for ds (th min ) and ds (th max ) for the oblate and prolate primary particle shape classes. For the isometric primary particle shape class, x values of the two intersections are used as error bars. The variability of the particle shapes in the selected primary particle shape class can be derived and applied to further data analysis steps. The distribution of x is stored over the entire height of the cloud's profile, providing information about the stratification of ice particle shapes in a cloud, which is a relevant indicator to understand in-cloud processes.

Accepted Answer

Temperature and supersaturation play a crucial role in determining particle shape. Laboratory studies have shown a clear relationship between particle shape, temperature, and supersaturation with respect to ice (Bailey and Hallett, 2009). Given conditions of liquid water saturation, near instrumentation (Illingworth et al., 2007), and two RHI-scans of d s and r s from th min to th max, the two polarimetric parameters d s and r s measured at th min and th max are combined with the spheroid model introduced in Section 3.2. The focus is on the selected layer to illustrate the case studies, even though all layers are processed to obtain the vertical distribution of particle shape. The last step aims to deliver insights in the quantification of the primary particle shape classes, as explained in Section 3.3, with the vertical distribution of x in the cloud. However, caution should be exercised when applying the method to rain and melting layer, as the derived x may not be valid due to the presence of spherical particles or irregular shapes, water coating, and large size in the melting layer.

Accepted Answer

The primary particle shape class observed in the rain event on 13 February 2017 at Limassol is the isometric particle shape class. It is identified using the VDPS method by analyzing the scattering patterns of the rain droplets. The VDPS method calculates the vertical distribution of x, which represents the particle shape transition. In this case, the isometric particle shape class is derived from the measurements of d s and r s, which are consistent with the scattering patterns of isometric particles. The melting layer is also identified using the VDPS method, and the temperature and Cloudnet classification further support the identification of isometric particles in the observed rain event.

Accepted Answer

The VDPS method, introduced in the article, offers several strengths. It provides a versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of particle shape. The method allows for the identification of oblate, isometric, and other particle shape classes based on the vertical distribution of polarizability ratio x. It also provides valuable constraints for microphysical fingerprinting studies and helps characterize mixed-phase cloud processes. The VDPS method can be implemented through an automatized framework, enabling long-term measurements and covering multiple field campaigns. However, there are limitations to the method. The radar antenna quality affects the calibration of d s and r s, which are intrinsically dependent on the calibration of the radar system. The method currently only investigates the shape of the main peak of the Doppler spectrum, limiting its ability to evaluate coexisting particle populations. Future development steps aim to improve calibration, peak separation techniques, and measurement time efficiency.

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Most frequently asked questions

1. What is the Vertical Distribution of Particle Shape (VDPS) method and how does it contribute to the understanding of cloud microphysical processes?

2. What are the technical characteristics and modifications of the SLDR-mode 35-GHz cloud radar Mira-35 used in the CyCARE campaign in Limassol, Cyprus?

3. How does the VDPS method combine simulated and measured values?

4. How do polarimetric gradients influence particle shape visibility?

5. How are modeled ds compared to measured ds?

6. How are primary particle shape classes identified and quantified?

7. How do temperature and supersaturation affect particle shape?

8. What is the primary particle shape class observed in the rain event on 13 February 2017 at Limassol, and how is it identified using the VDPS method?

9. What are the strengths and limitations of the Vertical-Distribution-of-Particle-Shape (VDPS) method?

References

Polarimetric Doppler Weather Radar

Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations

A Comprehensive Habit Diagram for Atmospheric Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field Studies

Mixed-Phase Clouds: Progress and Challenges

The Growth of Atmospheric Ice Crystals: A Summary of Findings in Vertical Supercooled Cloud Tunnel Studies

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