Quantitative Parametric Approach to Estimating Snowflake Size Distributions Using an Optical Sensing Disdrometer

Ogawa, Mariko; Oishi, Satoru; Yamaguchi, Kosei; Nakakita, Eiichi

doi:10.2151/sola.2015-031

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…However, there are some experimental works that can describe them. Although some researchers used gamma function to describe the particle size distribution of snowfall particles (Ogawa et al, ; Ulbrich, ), more researchers referred to exponential function (Brandes et al, ; Gunn & Marshall, ; Heymsfield et al, ):

P ((), d_{p}) = N_{0} exp ((), - normalΛ d_{p})

where N 0 (mm −1 m −3 ) is the interception parameter and Λ (mm ‐1 ) is the slope, and according to the study of Brandes et al (), we have

({, \begin{matrix} N_{0} = 7.0 \times 10^{3} {(T_{0} - T)}^{0.6} \\ normalΛ = 2.27 {(T_{0} - T)}^{0.18} \end{matrix})

where T 0 = 273.15 K.…”

Section: Model and Methodsmentioning

confidence: 99%

“…However, there are some experimental works that can describe them. Although some researchers used gamma function to describe the particle size distribution of snowfall particles (Ogawa et al, 2015;Ulbrich, 1983), more researchers referred to exponential function (Brandes et al, 2007;Gunn & Marshall, 1958;Heymsfield et al, 2008):…”

Section: Snowfall Particle Motionmentioning

confidence: 99%

See 1 more Smart Citation

A Snow Distribution Model Based on Snowfall and Snow Drifting Simulations in Mountain Area

Wang

Huang

2018

JGR Atmospheres

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Snow distribution modeling is important to hydrological research of alpine catchments, for the temporal and spatial evolution of snow cover has significant influence on snowmelt runoff. However, few snow distribution models have considered the slope effect on drifting snow transport. In this work, a drifting snow parameterization scheme considering the slope effect is proposed, and a snow distribution model based on snowfall and snow drifting simulations is established for alpine terrain. The validation wind‐tunnel experiments and field observations show high accuracy of our model in snow depth evaluation. Then we have analyzed the snow deposition patterns of complex terrain in detail. The results show different deposition patterns for snowfall and drifting snow, for deposition patterns for snowfall are controlled by both terrain and wind, while deposition patterns for drifting snow are mainly dominantly controlled by terrain, and the erosion or deposition rate is sensitive to the local wind speed. This work has considerable value in improving the accuracy of snow distribution prediction in alpine area, which we believe is essential for hydrological research of alpine catchments.

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P ((), d_{p}) = N_{0} exp ((), - normalΛ d_{p})

where N 0 (mm −1 m −3 ) is the interception parameter and Λ (mm ‐1 ) is the slope, and according to the study of Brandes et al (), we have

({, \begin{matrix} N_{0} = 7.0 \times 10^{3} {(T_{0} - T)}^{0.6} \\ normalΛ = 2.27 {(T_{0} - T)}^{0.18} \end{matrix})

where T 0 = 273.15 K.…”

Section: Model and Methodsmentioning

confidence: 99%

“…However, there are some experimental works that can describe them. Although some researchers used gamma function to describe the particle size distribution of snowfall particles (Ogawa et al, 2015;Ulbrich, 1983), more researchers referred to exponential function (Brandes et al, 2007;Gunn & Marshall, 1958;Heymsfield et al, 2008):…”

Section: Snowfall Particle Motionmentioning

confidence: 99%

A Snow Distribution Model Based on Snowfall and Snow Drifting Simulations in Mountain Area

Wang

Huang

2018

JGR Atmospheres

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Particle polarization lidar for precipitation particle classification

Shibata

2022

Appl. Opt.

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This study proposes a particle polarization lidar that distinguishes raindrops and snowflakes based on individual precipitation particles’ polarization information. As precipitation particles are several millimeters in size, the lidar signal from individual precipitation particles can be detected with a single laser pulse. Therefore, particle polarization lidar observation can obtain the range distribution of raindrops and snowflakes from the polarization information of individual precipitation particles. This paper reports the principle of the particle polarization lidar and vertical distributions of raindrops and snowflakes obtained by the lidar observations.

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Ground Observation of Tephra Particles: On the Use of Weather Radar for Estimating Volcanic Ash Distribution

Hapsari

Iida

Muranishi

et al. 2019

JDR

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This paper reports a preliminary attempt to determine volcanic ash particle size distribution using the video drop size detector (VDSD) for estimating volcanic ash amount with X-band radar. The VDSD records an image showing the size and number of particles falling into the aperture by a charge coupled device camera. Size distribution spectra of a range of particles from fine ash to small lapilli were derived in discrete form from the VDSD observation. The parameterization of the particle size distribution following Gamma function was done using volcanic ash of eruptions at the Sakurajima Volcano between December 13–21, 2014. Three Gamma distribution parameters were determined analytically. The analytical results revealed a continuous distribution of particles characterized by shape, intercept, and slope. The distribution was used to determine volcanic mass concentration, ground deposit weight, and reflectivity. Verification of these results with X-band radar observations showed that the reflectivity obtained from analytical results is similar to that from radar observation. However, the ground deposit weight from analysis was overestimated, compared with the real weight of ash deposit on the ground. The algorithm proposed in this study is expected to provide a practical method for estimating ash distribution in the aftermath of a volcanic eruption using radar-reflectivity for cases where direct measurement at the location is not possible. An overview of the algorithm for volcanic ash retrieval from X-band radar observations is also presented.

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Quantitative Parametric Approach to Estimating Snowflake Size Distributions Using an Optical Sensing Disdrometer

Cited by 3 publications

References 16 publications

A Snow Distribution Model Based on Snowfall and Snow Drifting Simulations in Mountain Area

A Snow Distribution Model Based on Snowfall and Snow Drifting Simulations in Mountain Area

Particle polarization lidar for precipitation particle classification

Ground Observation of Tephra Particles: On the Use of Weather Radar for Estimating Volcanic Ash Distribution

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