Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

Tiira, Jussi; Moisseev, Dmitri; Lerber, Annakaisa von; Ori, Davide; Tokay, Ali; Bliven, Larry F.; Petersen, Walter A.

doi:10.5194/amt-9-4825-2016

Cited by 62 publications

(142 citation statements)

References 51 publications

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“…At Barrow, the range of N (D) is given by 0.6 ≤ ≤ 3.6 mm −1 with mean = 1.5 mm −1 . The derived exponential slope parameters yield snowflake size distributions N (D) that are in line with previously presented snowflake size distributions using different measurement methods, e.g., by Brandes et al (2007) and Tiira et al (2016), with their reported median volume diameters D 0 of the derived snowflake size distributions converted to = 3.67/D 0 for N (D) given by Eq. (1).…”

Section: Measurement Resultssupporting

confidence: 85%

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

2017

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Abstract. The snowflake microstructure determines the microwave scattering properties of individual snowflakes and has a strong impact on snowfall radar signatures. In this study, individual snowflakes are represented by collections of randomly distributed ice spheres where the size and number of the constituent ice spheres are specified by the snowflake mass and surface-area-to-volume ratio (SAV) and the bounding volume of each ice sphere collection is given by the snowflake maximum dimension. Radar backscatter cross sections for the ice sphere collections are calculated at X-, Ku-, Ka-, and W-band frequencies and then used to model triplefrequency radar signatures for exponential snowflake size distributions (SSDs). Additionally, snowflake complexity values obtained from high-resolution multi-view snowflake images are used as an indicator of snowflake SAV to derive snowfall triple-frequency radar signatures. The modeled snowfall triple-frequency radar signatures cover a wide range of triple-frequency signatures that were previously determined from radar reflectivity measurements and illustrate characteristic differences related to snow type, quantified through snowflake SAV, and snowflake size. The results show high sensitivity to snowflake SAV and SSD maximum size but are generally less affected by uncertainties in the parameterization of snowflake mass, indicating the importance of snowflake SAV for the interpretation of snowfall triplefrequency radar signatures.

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Section: Measurement Resultssupporting

confidence: 85%

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

2017

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“…In this study observations of this IOP are used. The surface-based snowfall measurements were carried out by the PIP video-disdrometer (Newman et al, 2009;Tiira et al, 2016) and a weighing precipitation gauge OTT Pluvio 2 . The multi-frequency radar observations were obtained by the X-band scanning ARM cloud 20 radar (XSACR), Ka-band ARM zenith radar (KAZR), and the Marine W-band ARM cloud radar (MWACR), which all were part of the second ARM mobile facility (AMF2) deployed at the measurement site during BAECC.…”

Section: Measurements and Datamentioning

confidence: 99%

“…Due to the large variability of snow properties 20 2016) were used to constrain these scattering computations. The PIP measures particle size distribution and fall velocities (Tiira et al, 2016). From these observations particle masses were derived (von Lerber et al, 2017) using the hydrodynamic theory (Böhm, 1989).…”

Section: Introductionmentioning

confidence: 99%

“…The v(D) relation is derived by a linear regression fit in the log-space for the observed particles during every five minutes (Tiira et al, 2016). The m(D) relation is retrieved by utilizing the general hydrodynamic 15 theory (Böhm, 1989;Mitchell and Heymsfield, 2005), where a snow particle mass is computed from the observed dimension, fall velocity and area ratio of a snow particle.…”

mentioning

confidence: 99%

“…Particle fall velocities are recorded as a function of D Deq and values are considered reliable, if there are more than two observations of the identified particle and values are higher or equal to 0.5 ms In this study 5-minute time series of the observed PSD, the fitted v(D) and the retrieved m(D) relations are utilized (Tiira et al, 2016;von Lerber et al, 2017) as a function of the diameter D. The PSD is averaged using one minute observations, after spurious particle records are filtered out. The v(D) relation is derived by a linear regression fit in the log-space for the observed particles during every five minutes (Tiira et al, 2016).…”

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confidence: 99%

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Snowfall retrieval at X, Ka and W band: consistency of backscattering and microphysical properties using BAECC ground-based measurements

Falconi¹,

Lerber²,

Ori³

et al. 2018

Preprint

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Abstract. Radar-based snowfall intensity retrieval is investigated at centimeter and millimeter wavelengths using high-quality collocated ground-based multi-frequency radar and video-disdrometer observations. Using data from four snowfall events, recorded during the Biogenic Aerosols Effects on Clouds and Climate (BAECC) campaign in Finland, measurements of liquidwater-equivalent snowfall rate S are correlated to radar equivalent reflectivity factors Z e , measured by the Atmospheric Radiation Measurement (ARM) cloud radars operating at X, Ka and W frequency bands. From these coupled observations power-5 law Z e -S relationships are derived for all considered frequencies and distinguishing fluffy from rimed snowfall. Interestingly fluffy-snow events show a spectrally distinct signature of Z e -S with respect to rimed-snow cases. In order to understand the connection between snowflake microphysical and multi-frequency backscattering properties, numerical simulations are also performed by using the particle size distribution provided by the in-situ video-disdrometer. The latter are carried out by using both the T-matrix method (TMM) for soft-spheroids with different aspect ratios and exploiting a pre-computed discrete dipole 10 approximation (DDA) database for complex-shape snowflakes. Based on the presented results, it is concluded that the softspheroid approximation can be adopted to explain the observed multi-frequency Z e -S relations if a proper spheroid aspect ratio is selected. The latter may depend on the snowfall type. A further analysis of the backscattering simulations reveals that TMM cross-sections are higher than the DDA ones for small ice particles, but lower for larger particles. These differences may explain why the soft-spheroid approximation is satisfactory for radar reflectivity simulations, the errors of computed cross-sections for 15 larger and smaller particles compensating each other.

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How dual‐polarization radar observations can be used to verify model representation of secondary ice

2016

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In this paper it is discussed how dual‐polarization radar observations can be used to verify model representations of secondary ice production. An event where enhanced specific differential phase, Kdp, signatures in snow occur at the altitudes where temperatures lie in the range between −8 and −3°C is investigated. By combining radar and surface‐based precipitation observations it is shown that these dual‐polarization radar signatures are most likely caused by ice with concentrations exceeding those expected from primary ice parameterizations. It is also shown that the newly formed ice particles readily aggregate, which may explain why Kdp values seem to be capped at 0.2–0.3°/km for a C band radar. For the event of interest, multiple high‐resolution (1 km) Weather Research and Forecasting (WRF) model simulations are conducted. When the default versions of the Morrison microphysics schemes were used, the simulated number concentration of frozen hydrometeors is much lower than observed and the simulated ice particle concentrations are comparable with values expected from primary ice parameterizations. Higher ice concentrations, which exceed values expected from primary ice parameterizations, were simulated when ad hoc thresholds for rain and cloud water mixing ratio in the Hallett‐Mossop part of the Morrison scheme were removed. These results suggest that the parameterization of secondary ice production in operational weather prediction models needs to be revisited and that dual‐polarization radar observations, in conjunction with ancillary observations, can be used to verify them.

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Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

Cited by 62 publications

References 51 publications

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

Snowfall retrieval at X, Ka and W band: consistency of backscattering and microphysical properties using BAECC ground-based measurements

How dual‐polarization radar observations can be used to verify model representation of secondary ice

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