the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Feb 2025
Detection of Multi-Modal Doppler Spectra. Part 1: Establishing Characteristic Signals in Radar Moment Data
Abstract. Vertically pointing millimeter-wavelength radars provide a wealth of information about cloud and precipitation particle properties. Doppler spectral data can inform on how particles of varying vertical velocities contribute to total backscattered power observed. It is more computationally cost effective to process moment data instead of spectra data, but doing so leaves valuable information on the cutting room floor. To confidently identify a multi-modal spectra event, in which two or more modes are present within a layer, Doppler spectral data are essential. This means long-term identification of layers featuring multi-modal spectra can be cost prohibitive.
To address this, we explore three multi-modal spectra cases from winter precipitation events to determine characteristic signatures of these layers in the moment data averaged over short time periods (~145 s) and explore how these layers differ from the rest of the vertical profiles. We find that the mean spectrum width and the standard deviation of mean Doppler velocity can be used to determine whether or not a layer is multi-modal. In particular, multi-modal layers in mixed-phase and ice clouds feature larger mean spectrum width (exceeding 0.19 m s-1) and smaller standard deviation of the mean Doppler velocity (below 0.1 m s-1). In Part 1 of this study, the identification criteria and methods are described. In Part 2, we perform a verification of the method for three years of vertically pointing radar data, and explore the meteorological conditions associated with identified multi-modal spectral events.
Competing interests: One of the authors is a members of the editorial board of the journal "Atmospheric Measurement Techniques".
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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RC1: 'Comment on egusphere-2025-671', Anonymous Referee #1, 17 Mar 2025
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Review for Detection of Multi-Modal Doppler Spectra. Part 1: Establishing
Characteristic Signals in Radar Moment Data by Wugofski et al.
This study develops a method for using radar moment data to determine periods with multi-modal Doppler spectra signatures. This issue of detecting multi-modal spectra is difficult because the signature can be rare and is often buried in the large amount of spectra data available throughout various cases. In their analysis of three cases from three separate sites, the authors find that multimodality appears to be associated with large average values of spectrum width compared to control layers and small standard deviations of mean Doppler velocity compared to turbulent layers. Using these moment metrics and various other filters, the authors then create a criteria-based methodology for flagging periods of potential multi-modal spectra. Part 2 of this two-part paper will apparently use this method with a large number of cases.
This study seems like an important step in curating and understanding datasets with multi-modal spectra. I’m a little unsure whether three short time periods from three cases is enough to create appropriate criteria for detecting multi-modality. However, I suppose that the merits of this methodology will be more apparent in part 2 when the authors apply their methodology to a larger dataset where they can independently verify their results. However, I do have one major concern that I think the authors should address in part 1. Throughout the study, there is no mention of quantifying the degree of multi-modality. It seems like the authors use a relatively subjective reflectivity threshold of 5 dB between local peaks (with local peaks determined computationally?) to determine if there is or is not multi-modality in the Doppler spectra at various heights. I think that the authors really should perform a more objective and quantitative analysis here. If the moment statistics that the authors use actually are representative of multi-modal Doppler spectra in general, then I would expect that these statistics should correlate directly with the amount and degree of multi-modality present in the spectra data according to: MEAN(SW)/STD(MDV), perhaps normalized in some way. This quantity could potentially be used as a proxy for degree or strength of multi-modality or the probability of multi-modality. To me, the lack of objectivity of determining multi-modality from the data is concerning because the authors could be potentially missing periods of developing or subtle multi-modality that their methodology would not flag. If the algorithm misses a particular multi-modal event, then it would be good for the authors to illustrate why the event was missed. Also, because the authors only consider three cases here, it seems to me that it would be quite simple and fast to objectively determine the degree of multi-modality as a metric itself. For part 2, the authors will need to be very careful to identify periods/heights that are correctly labeled as either multi-modal or unimodal. In order for the authors to address these concerns, I recommend major revisions.
Major Concerns:
- The authors should come up with a better way of quantifying multi-modal spectra. The authors could easily develop quantitative metrics for degree of multi-modality using a variety of techniques such as fitting the spectra at each height with a Gaussian Mixture model and by using the Gaussian mixture components to derive relevant statistical measures of multi-modality. My concern is that the authors are being a bit too selective about what they consider to be multi-modal and unimodal. An objective metric that is more independent of researcher biases here would be more informative and would provide a fairer representation of multi-modal/unimodality inherent in the spectra data.
- What is the expected miss rate and false positive rate of the detection algorithm? From Figure 7 it seems like there is a missed multi-modal spectra as pointed out in the main text. However, because the authors only look at three cases, it’s hard to actually evaluate the performance of the algorithm. The authors will need to be very careful in part 2 to objectively evaluate the algorithm’s performance and to properly quantify the hits, misses, false alarms, and nulls using a larger dataset. In my opinion, this really necessitates the ability for the authors to provide a much more objective set of criteria for determining multi-modality or uni-modality.
Minor Concerns:
- The authors state in the conclusions that: “The design of the criteria and methodology is targeted at reducing false positives; it is likely that the use of these criteria may miss the detection of some multi-modal spectra (cf. Fig. 7).” This seems backwards to me. If anything, wouldn’t you want to reduce missed events? The reasoning is that you can always revisit the spectra data for flagged periods to verify multi-modality after the fact. Therefore, false positives, unless there are many, aren’t that big of a deal whereas missed multi-modal spectrums represent a serious deficiency in your algorithm as possibly suggested by Figure 7.
- I was disappointed to see a lack of complimentary data from other instruments present at these three sites. The SBU site in particular has a wealth of potentially useful ground instruments like Pluvio, Parsivel, and the MASC imager. These instruments could be helpful in part 1 of this paper to provide some hints to readers as to the actual mechanisms behind periods of multi-modal spectra. I don’t necessarily think that the authors have to include such data in their analysis (perhaps that will be in part 2?). However, I do think that the authors should at least consider including one or two figures that use these additional instruments to help provide context into the potential sources of the multi-modality for each of the cases. That is, do MASC images of the particles at the ground support hypotheses of rime splintering, ice-ice collisions, or freezing drop fragmentation near regimes of multi-modal Doppler spectra?
Suggestions/Typos/etc.:
- I didn’t find any typographical errors. The authors did a good job writing the preprint draft.
Citation: https://doi.org/10.5194/egusphere-2025-671-RC1
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