Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Settling and clustering of snow particles in atmospheric turbulence
by
Li, Cheng
, Hong, Jiarong
, Heisel, Michael
, Lim, Kaeul
, Coletti, Filippo
, Abraham, Aliza
, Berk, Tim
, Guala, Michele
in
Acceleration
/ Atmospheric models
/ Atmospheric turbulence
/ Clustering
/ Computational fluid dynamics
/ Elongation
/ Fluid flow
/ Fluid mechanics
/ JFM Papers
/ Morphology
/ Multiphase flow
/ Particle size distribution
/ Phenomenology
/ Precipitation
/ Reynolds number
/ Settling
/ Size distribution
/ Snow
/ Snow accumulation
/ Spatial distribution
/ Stokes number
/ Sweeping
/ Velocity
/ Vertical distribution
/ Weather forecasting
2021
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Settling and clustering of snow particles in atmospheric turbulence
by
Li, Cheng
, Hong, Jiarong
, Heisel, Michael
, Lim, Kaeul
, Coletti, Filippo
, Abraham, Aliza
, Berk, Tim
, Guala, Michele
in
Acceleration
/ Atmospheric models
/ Atmospheric turbulence
/ Clustering
/ Computational fluid dynamics
/ Elongation
/ Fluid flow
/ Fluid mechanics
/ JFM Papers
/ Morphology
/ Multiphase flow
/ Particle size distribution
/ Phenomenology
/ Precipitation
/ Reynolds number
/ Settling
/ Size distribution
/ Snow
/ Snow accumulation
/ Spatial distribution
/ Stokes number
/ Sweeping
/ Velocity
/ Vertical distribution
/ Weather forecasting
2021
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Settling and clustering of snow particles in atmospheric turbulence
by
Li, Cheng
, Hong, Jiarong
, Heisel, Michael
, Lim, Kaeul
, Coletti, Filippo
, Abraham, Aliza
, Berk, Tim
, Guala, Michele
in
Acceleration
/ Atmospheric models
/ Atmospheric turbulence
/ Clustering
/ Computational fluid dynamics
/ Elongation
/ Fluid flow
/ Fluid mechanics
/ JFM Papers
/ Morphology
/ Multiphase flow
/ Particle size distribution
/ Phenomenology
/ Precipitation
/ Reynolds number
/ Settling
/ Size distribution
/ Snow
/ Snow accumulation
/ Spatial distribution
/ Stokes number
/ Sweeping
/ Velocity
/ Vertical distribution
/ Weather forecasting
2021
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Settling and clustering of snow particles in atmospheric turbulence
Journal Article
Settling and clustering of snow particles in atmospheric turbulence
2021
Request Book From Autostore
and Choose the Collection Method
Overview
The effect of turbulence on snow precipitation is not incorporated into present weather forecasting models. Here we show evidence that turbulence is in fact a key influence on both fall speed and spatial distribution of settling snow. We consider three snowfall events under vastly different levels of atmospheric turbulence. We characterize the size and morphology of the snow particles, and we simultaneously image their velocity, acceleration and relative concentration over vertical planes approximately $30\\ \\textrm {m}^2$ in area. We find that turbulence-driven settling enhancement explains otherwise contradictory trends between the particle size and velocity. The estimates of the Stokes number and the correlation between vertical velocity and local concentration are consistent with the view that the enhanced settling is rooted in the preferential sweeping mechanism. When the snow vertical velocity is large compared to the characteristic turbulence velocity, the crossing trajectories effect results in strong accelerations. When the conditions of preferential sweeping are met, the concentration field is highly non-uniform and clustering appears over a wide range of scales. These clusters, identified for the first time in a naturally occurring flow, display the signature features seen in canonical settings: power-law size distribution, fractal-like shape, vertical elongation and large fall speed that increases with the cluster size. These findings demonstrate that the fundamental phenomenology of particle-laden turbulence can be leveraged towards a better predictive understanding of snow precipitation and ground snow accumulation. They also demonstrate how environmental flows can be used to investigate dispersed multiphase flows at Reynolds numbers not accessible in laboratory experiments or numerical simulations.
Publisher
Cambridge University Press
Subject
This website uses cookies to ensure you get the best experience on our website.