Presenting the Snowflake Video Imager (SVI)

Newman, Andrew J.; Kucera, Paul A.; Bliven, Larry F.

doi:10.1175/2008jtecha1148.1

Cited by 124 publications

(160 citation statements)

References 12 publications

(9 reference statements)

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“…where X fov and Y fov are the maximum width and height of the frame, f is an empirical factor relating particle size to depth of field, and D ij k is the particle size (Newman et al, 2009). The expression Eq.…”

Section: Discussionmentioning

confidence: 99%

“…A number of instruments installed at CARE provided observations of snow particles, including the NASA Snowflake Video Imager (SVI) (Newman et al, 2009) and Colorado State University's 2-D Video Disdrometer (2DVD) (Thurai and Bringi, 2005). The SVI uses a video camera to capture 2-D images of particles.…”

Section: Snow Particle Observationsmentioning

confidence: 99%

“…The SVI uses a video camera to capture 2-D images of particles. In each image frame, the SVI directly observes a 3-D volume defined by the camera's 2-D field of view and the depth of field (Newman et al, 2009). For a single image frame, the discrete size distribution is…”

Section: Snow Particle Observationsmentioning

confidence: 99%

“…Because the SVI is not dependent on particle matching, the SVI observations are taken as the primary measure of the snow PSDs for this work. The SVI size distributions are reported in discrete size bins of width 0.25 mm for sizes from 0 to 26 mm at 1 min resolution, but observations of particles smaller than 0.3 mm are discarded during the SVI image processing (Newman et al, 2009). While incomplete matching interferes with accurate determination of a PSD from the 2DVD data, it does not interfere with the measurement of fallspeeds, so the 2DVD observations are used primarily for particle fallspeed data.…”

Section: Snow Particle Observationsmentioning

confidence: 99%

“…Analytic uncertainties include uncertainties that arise in the analysis of SVI images to determine particle sizes, uncertainties in the counted number of particles, and uncertainties in the calculated depth of field and field of view (Newman et al, 2009). In contrast, sampling uncertainties arise due to statistical fluctuations in the number of particles counted by the instrument.…”

Section: N B Wood Et Al: Characterization Of Disdrometer Uncertainmentioning

confidence: 99%

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Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

et al. 2013

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Abstract. Estimates of snow microphysical properties obtained by analyzing collections of individual particles are often limited to short timescales and coarse time resolution. Retrievals using disdrometer observations coincident with bulk measurements such as radar reflectivity and snowfall amounts may overcome these limitations; however, retrieval techniques using such observations require uncertainty estimates not only for the bulk measurements themselves, but also for the simulated measurements modeled from the disdrometer observations. Disdrometer uncertainties arise due to sampling and analytic errors and to the discrete, potentially truncated form of the reported size distributions. Imaging disdrometers such as the Snowflake Video Imager and 2-D Video Disdrometer provide remarkably detailed representations of snow particles, but view limited projections of their three-dimensional shapes. Particle sizes determined by such instruments underestimate the true dimensions of the particles in a way that depends, in the mean, on particle shape, also contributing to uncertainties. An uncertainty model that accounts for these uncertainties is developed and used to establish their contributions to simulated radar reflectivity and snowfall rate. Viewing geometry effects are characterized by a parameter, φ, that relates disdrometer-observed particle size to the true maximum dimension of the particle. Values and uncertainties for φ are estimated using idealized ellipsoidal snow particles. The model is applied to observations from seven snow events from the Canadian CloudSat/CALIPSO Validation Project (C3VP), a mid-latitude cold-season cloud and precipitation field experiment. Typical total uncertainties are 4 dB for reflectivity and 40-60 % for snowfall rate, are highly correlated, and are substantial compared to expected uncertainties for radar and precipitation gauge observations. The dominant sources of errors are viewing geometry effects and the discrete, truncated form of the size distributions. While modeled Ze-S relationships are strongly affected by assumptions about snow particle mass properties, such relationships are only modestly sensitive to φ owing to partially compensating effects on both the reflectivity and snowfall rate.

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Section: Discussionmentioning

confidence: 99%

Section: Snow Particle Observationsmentioning

confidence: 99%

Section: Snow Particle Observationsmentioning

confidence: 99%

Section: Snow Particle Observationsmentioning

confidence: 99%

Section: N B Wood Et Al: Characterization Of Disdrometer Uncertainmentioning

confidence: 99%

See 3 more Smart Citations

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

et al. 2013

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Estimating snow microphysical properties using collocated multisensor observations

et al. 2014

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The ability of ground-based in situ and remote sensing observations to constrain microphysical properties for dry snow is examined using a Bayesian optimal estimation retrieval method. Power functions describing the variation of mass and horizontally projected area with particle size and a parameter related to particle shape are retrieved from near-Rayleigh radar reflectivity, particle size distribution, snowfall rate, and size-resolved particle fall speeds. Algorithm performance is explored in the context of instruments deployed during the Canadian CloudSat CALIPSO Validation Project, but the algorithm is adaptable to other similar combinations of sensors. Critical estimates of observational and forward model uncertainties are developed and used to quantify the performance of the method using synthetic cases developed from actual observations of snow events. In addition to illustrating the technique, the results demonstrate that this combination of sensors provides useful constraints on the mass parameters and on the coefficient of the area power function but only weakly constrains the exponent of the area power function and the shape parameter. Information content metrics show that about two independent quantities are measured by the suite of observations and that the method is able to resolve about eight distinct realizations of the state vector containing the mass and area power function parameters. Alternate assumptions about observational and forward model uncertainties reveal that improved modeling of particle fall speeds could contribute substantial improvements to the performance of the method.

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Observed influence of riming, temperature, and turbulence on the fallspeed of solid precipitation

Garrett

Yuter

2014

Geophys. Res. Lett.

121

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Forecasts of the amount and geographic distribution of snow are highly sensitive to a model's parameterization of hydrometeor fallspeed. Riming is generally thought to lead to particles with a higher mass and terminal velocity. Yet models commonly assume that heavily rimed particles such as graupel have a fixed density and that their settling speed is unaffected by turbulence in storms. Here we show automated measurements of photographed hydrometeor shape and fallspeed using a new instrument placed in Utah's Wasatch Mountain Range. The data show that graupel in low‐turbulence conditions has a size‐dependent fallspeed distribution with a mode near 1 m s−1, a result that is generally consistent with prior observations. However, the distributions are broadened by turbulence and there is a correspondence between particle density and air temperature. In high turbulence and at low temperatures, any sensitivity of fallspeed to particle size disappears.

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Presenting the Snowflake Video Imager (SVI)

Cited by 124 publications

References 12 publications

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

Estimating snow microphysical properties using collocated multisensor observations

Observed influence of riming, temperature, and turbulence on the fallspeed of solid precipitation

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