Towards the connection between snow microphysics and melting layer: insights from multifrequency and dual-polarization radar observations during BAECC

Li, Haoran; Tiira, Jussi; Lerber, Annakaisa von; Moisseev, Dmitri

doi:10.5194/acp-20-9547-2020

Cited by 28 publications

(29 citation statements)

References 79 publications

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“…When thermodynamic information is available, radio-wave propagation models can be used to estimate radar signal attenuation by atmospheric gases. Here we use the MPM93 model, an updated version of the millimeter-wave propagation model described by Liebe (1985) and Liebe et al (1993) to compute two-way gas attenuation of X-, Ka-, Wand G-band signals for the conditions that occurred at 12:00 and 15:44 UTC on 25 February 2020 when two radiosondes were launched. Figure 2a and b show the profiles of temperature, dew-point temperature and humidity recorded at the US NWS site 22 km east of SBRO at 12:00 UTC and at the SBRO at 15:44 UTC.…”

Section: Gaseous Attenuation Correctionmentioning

confidence: 99%

Multifrequency radar observations of clouds and precipitation including the G-band

et al. 2021

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Abstract. Observations collected during the 25 February 2020 deployment of the Vapor In-Cloud Profiling Radar at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research, something that until now was only discussed in theory. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the Ka–G pairing can generate differential reflectivity signal several decibels larger than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes. The observations also showed that G-band signals experience non-Rayleigh scattering in regions where Ka- and W-band signal do not, thus demonstrating the potential of G-band radars for sizing sub-millimeter ice crystals and droplets. Observed peculiar radar reflectivity patterns also suggest that G-band radars could be used to gain insight into the melting behavior of small ice crystals. G-band signal interpretation is challenging, because attenuation and non-Rayleigh effects are typically intertwined. An ideal liquid-free period allowed us to use triple-frequency Ka–W–G observations to test existing ice scattering libraries, and the results raise questions on their comprehensiveness. Overall, this work reinforces the importance of deploying radars (1) with sensitivity sufficient enough to detect small Rayleigh scatters at cloud top in order to derive estimates of path-integrated hydrometeor attenuation, a key constraint for microphysical retrievals; (2) with sensitivity sufficient enough to overcome liquid attenuation, to reveal the larger differential signals generated from using the G-band as part of a multifrequency deployment; and (3) capable of monitoring atmospheric gases to reduce related uncertainty.

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Section: Gaseous Attenuation Correctionmentioning

confidence: 99%

Multifrequency radar observations of clouds and precipitation including the G-band

et al. 2021

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“…The measurements used in this study were collected at Station for Measuring Ecosystem -Atmosphere Relations II (Hari and Kulmala, 2005;Petäjä et al, 2016) (Küchler et al, 2017) (HYytiälä Doppler RAdar, HYDRA-W) has been operating at the station. The radar is pointing vertically and measures radar signal spectral moments, LDR and dual-polarization Doppler spectra; see Li and Moisseev (2020) for the example of the data. The LDR decoupling is about 30 dB, so the minimum observable LDR is about −30 dB.…”

Section: Cloud Radar Observationsmentioning

confidence: 99%

“…For vertically pointing Ka-and W-band radars, ice columns usually produce LDR values as high as −15 dB, which is distinctively higher than that of most other ice particle types (Aydin and Walsh, 1999;Tyynelä et al, 2011). Oue et al (2015), Li and Moisseev (2020), Luke et al (2021), andLi et al (2021) have shown that this strong LDR signal at the slow falling part of the radar Doppler spectrum can be used to identify columnar-ice crystals. In this study, this method is applied to long-term radar Doppler spectra observations for characterizing the production of columnar-ice particles in stratiform clouds.…”

Section: Introductionmentioning

confidence: 99%

Two-year statistics of columnar-ice production in stratiform clouds over Hyytiälä, Finland: environmental conditions and the relevance to secondary ice production

Möhler

Petäj̈ä

et al. 2021

Atmos. Chem. Phys.

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Abstract. Formation of ice particles in clouds at temperatures of −10 ∘C or warmer was documented by using ground-based radar observations. At these temperatures, the number concentration of ice-nucleating particles (INPs) is not only expected to be small, but this number is also highly uncertain. In addition, there are a number of studies reporting that the observed number concentration of ice particles exceeds expected INP concentrations, indicating that other ice generation mechanisms, such as secondary ice production (SIP), may play an important role in such clouds. To identify formation of ice crystals and report conditions in which they are generated, W-band cloud radar Doppler spectra observations collected at the Hyytiälä station for more than 2 years were used. Given that at these temperatures ice crystals grow mainly as columns, which have distinct linear depolarization ratio (LDR) values, the spectral LDR was utilized to identify newly formed ice particles. It is found that in 5 %–13 % of clouds, where cloud top temperatures are −12 ∘C or warmer, production of columnar ice is detected. For colder clouds, this percentage can be as high as 33 %; 40 %–50 % of columnar-ice-producing events last less than 1 h, while 5 %–15 % can persist for more than 6 h. By comparing clouds where columnar crystals are produced and to the ones where these crystals are absent, the columnar-ice-producing clouds tend to have larger values of liquid water path and precipitation intensity. The columnar-ice-producing clouds were subdivided into three categories, using the temperature difference, ΔT, between the altitudes where columns are first detected and cloud top. The cases where ΔT is less than 2 K are typically single-layer shallow clouds where needles are produced at the cloud top. In multilayered clouds where 2 K < ΔT, columns are produced in a layer that is seeded by ice particles falling from above. This classification allows us to study potential impacts of various SIP mechanisms, such as the Hallet–Mossop process or freezing breakup, on columnar-ice production. To answer the question whether the observed ice particles are generated by SIP in the observed single-layer shallow clouds, ice particle number concentrations were retrieved and compared to several INP parameterizations. It was found that the ice number concentrations tend to be 1–3 orders of magnitude higher than the expected INP concentrations.

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“…Using these retrieved masses m and the maximum dimensions of the observed particles, D max , we first estimated the rime fraction. The mass of unrimed snow, m us is assumed as in Moisseev et al (2017); Li et al (2020):…”

Section: Sampling Of Training Datamentioning

confidence: 99%

“…Mason et al (2018) developed an optimal estimation retrieval to obtain a density factor parameter, which is sensitive to riming, using observations of mean Doppler velocity (MDV) and DWR Ka,W . Li et al (2020) developed a snow observation classification with a rimed and an unrimed category, using MDV and DWR X,Ka . Oue et al (2021) combined MDV, DWR Ka,W with polarimetric observations and were able to observe even different stages of the riming process.…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural networks to predict riming from Doppler cloud radar observations

Vogl

Maahn

Kneifel

et al. 2021

Preprint

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Abstract. Riming, i.e. the accretion and freezing of SLW on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying riming using ground-based cloud radar observations is of great interest, however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka-band cloud radar and in-situ observations of snowfall. We focus on two configurations of input variables: ANN #1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width; SEW), and the skewness as input features. ANN #2 only uses Ze, SEW and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (riming index = 1) and yield low values (riming index ≤ 0.4) for unrimed snow. In general, the predictions of ANN #1 and ANN #2 are very similar, advocating the capability to predict riming without the use of MDV. It is demonstrated that both ANN setups are able to generalize to W-band radar data. The predictions of both ANNs for a wintertime convective cloud fit coinciding in-situ observations extremely well, suggesting the possibility to predict riming even within convective systems. Application of ANN #2 to an orographic case yields high riming index values coinciding with observations of solid graupel particles at the ground.

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Towards the connection between snow microphysics and melting layer: insights from multifrequency and dual-polarization radar observations during BAECC

Cited by 28 publications

References 79 publications

Multifrequency radar observations of clouds and precipitation including the G-band

Multifrequency radar observations of clouds and precipitation including the G-band

Two-year statistics of columnar-ice production in stratiform clouds over Hyytiälä, Finland: environmental conditions and the relevance to secondary ice production

Using artificial neural networks to predict riming from Doppler cloud radar observations

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