Orientations and aspect ratios of falling snow

Garrett, Timothy J.; Yuter, Sandra E.; Fallgatter, C.; Shkurko, Konstantin; Rhodes, Spencer R.; Endries, Jason L.

doi:10.1002/2015gl064040

Cited by 93 publications

(91 citation statements)

References 47 publications

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“…Early studies (e.g., Kajikawa, ; Klett, ; Zikmunda & Vali, ) found that ice particle canting angle ranges from 5° to 25°. Video camera records show that the median orientation is between 35° and 39° (Garrett et al, ), which indicates σ θ might be up to 30°–40°. Nevertheless, polarized radar measurements seem to show smaller σ θ in some case studies.…”

Section: Methodsmentioning

confidence: 99%

How Does Riming Affect Dual‐Polarization Radar Observations and Snowflake Shape?

Moisseev

Lerber

2018

JGR Atmospheres

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As an ice particle grows by riming its shape is expected to change, resulting in a more spherical particle at the later stages of riming. This conceptual model is at the core of the current ice microphysical schemes and used for dual‐polarization radar observation based classification of hydrometeors. A quantitative relation between riming and shapes of snowflake aggregates, however, has not been established yet. This study aims to derive this relation by using surface‐based precipitation and coinciding dual‐polarization radar observations. The observations were collected during four winter seasons, 49 snowstorms, at University of Helsinki measurement station in Hyytiälä, Finland. Results show that relation between the differential reflectivity and reflectivity‐weighted rime mass fraction is not monotonic and depends on reflectivity‐weighted mean diameter. This behavior can be explained by the opposing effects of riming on dual‐polarization radar observations. Riming increases particle bulk density, which leads to more pronounced dual‐polarization radar signatures. As riming progresses the aspect ratio of snowflake increases slowly until the rime mass fraction value reaches a certainty value after which the aspect ratio increases more rapidly. Finally, coutilization of Ze, Zdr, and Kdp for inferring riming fraction is analyzed.

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Section: Methodsmentioning

confidence: 99%

How Does Riming Affect Dual‐Polarization Radar Observations and Snowflake Shape?

Moisseev

Lerber

2018

JGR Atmospheres

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“…In terms of aspect-ratio, Straka et al (2000) report values ranging between 0.6 and 0.8 for dry aggregates and between 0.6 and 0.9 for graupel while Garrett et al (2015) reports a median aspect-ratio of 0.6 for aggregates and a strong mode in graupel aspect-ratios around 0.9.…”

Section: Snow and Graupelmentioning

confidence: 99%

From model to radar variables: a new forward polarimetric radar operator for COSMO

Wolfensberger

Berne

2018

Atmos. Meas. Tech.

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Abstract. In this work, a new forward polarimetric radar operator for the COSMO numerical weather prediction (NWP) model is proposed. This operator is able to simulate measurements of radar reflectivity at horizontal polarization, differential reflectivity as well as specific differential phase shift and Doppler variables for ground based or spaceborne radar scans from atmospheric conditions simulated by COSMO. The operator includes a new Doppler scheme, which allows estimation of the full Doppler spectrum, as well a melting scheme which allows representing the very specific polarimetric signature of melting hydrometeors. In addition, the operator is adapted to both the operational onemoment microphysical scheme of COSMO and its more advanced two-moment scheme. The parameters of the relationships between the microphysical and scattering properties of the various hydrometeors are derived either from the literature or, in the case of graupel and aggregates, from observations collected in Switzerland. The operator is evaluated by comparing the simulated fields of radar observables with observations from the Swiss operational radar network, from a high resolution X-band research radar and from the dual-frequency precipitation radar of the Global Precipitation Measurement satellite (GPM-DPR). This evaluation shows that the operator is able to simulate an accurate Doppler spectrum and accurate radial velocities as well as realistic distributions of polarimetric variables in the liquid phase. In the solid phase, the simulated reflectivities agree relatively well with radar observations, but the simulated differential reflectivity and specific differential phase shift upon propagation tend to be underestimated. This radar operator makes it possible to compare directly radar observations from various sources with COSMO simulations and as such is a valuable tool to evaluate and test the microphysical parameterizations of the model.

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“…Given images from the MASC, it is possible to derive estimates of properties such as particle shape, aspect ratio, maximum dimension, complexity, and orientation (Garrett et al, 2012(Garrett et al, , 2015Garrett and Yuter, 2014). Here, specifically, we use observations of maximum dimension and habit to constrain uncertainties inherent to radar-based retrievals of snow rate.…”

Section: Multi-angle Snowflake Cameramentioning

confidence: 99%

“…We introduce MASC PSD information into the retrieval scheme through use of the a priori estimate, a, and a priori covariance matrix, S a . For the PSD slope parameter a priori guess, the MASC images were processed to quantify maximum particle dimension for each snowflake according to the techniques developed in Garrett et al (2012), Garrett and Yuter (2014), and Garrett et al (2015). We then fit a slope parameter for an assumed exponential particle size distribution to the tail of the size distribution > 1 mm in size.…”

Section: Combined Radar-masc Retrievalmentioning

confidence: 99%

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

Cooper

Wood²,

L’Ecuyer

2017

Atmos. Meas. Tech.

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Abstract. Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100-200 % for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASC measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a −18 % difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36 % for the individual events. Use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from −64 to +122 % for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. More accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground-and space-based radar estimates of snowfall.

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Orientations and aspect ratios of falling snow

Cited by 93 publications

References 47 publications

How Does Riming Affect Dual‐Polarization Radar Observations and Snowflake Shape?

How Does Riming Affect Dual‐Polarization Radar Observations and Snowflake Shape?

From model to radar variables: a new forward polarimetric radar operator for COSMO

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

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