scholarly journals Radar measurements of blowing snow off a mountain ridge

2020 ◽  
Vol 14 (6) ◽  
pp. 1779-1794
Author(s):  
Benjamin Walter ◽  
Hendrik Huwald ◽  
Josué Gehring ◽  
Yves Bühler ◽  
Michael Lehning
Keyword(s):  
Wind Velocity ◽  
Order Approximation ◽  
Sonic Anemometer ◽  
Snow Accumulation ◽  
Travel Distance ◽  
Storm Event ◽  
Height Distribution ◽  
Blowing Snow ◽  
Mountain Ridge ◽  
Mountainous Regions

Abstract. Modelling and forecasting wind-driven redistribution of snow in mountainous regions with its implications on avalanche danger, mountain hydrology or flood hazard is still a challenging task often lacking in essential details. Measurements of drifting and blowing snow for improving process understanding and model validation are typically limited to point measurements at meteorological stations, providing no information on the spatial variability of horizontal mass fluxes or even the vertically integrated mass flux. We present a promising application of a compact and low-cost radar system for measuring and characterizing larger-scale (hundreds of metres) snow redistribution processes, specifically blowing snow off a mountain ridge. These measurements provide valuable information of blowing snow velocities, frequency of occurrence, travel distances and turbulence characteristics. Three blowing snow events are investigated, two in the absence of precipitation and one with concurrent precipitation. Blowing snow velocities measured with the radar are validated by comparison against wind velocities measured with a 3D ultra-sonic anemometer. A minimal blowing snow travel distance of 60–120 m is reached 10–20 % of the time during a snow storm, depending on the strength of the storm event. The relative frequency of transport distances decreases exponentially above the minimal travel distance, with a maximum measured distance of 280 m. In a first-order approximation, the travel distance increases linearly with the wind velocity, allowing for an estimate of a threshold wind velocity for snow particle entrainment and transport of 7.5–8.8 m s−1, most likely depending on the prevailing snow cover properties. Turbulence statistics did not allow a conclusion to be drawn on whether low-level, low-turbulence jets or highly turbulent gusts are more effective in transporting blowing snow over longer distances, but highly turbulent flows are more likely to bring particles to greater heights and thus influence cloud processes. Drone-based photogrammetry measurements of the spatial snow height distribution revealed that increased snow accumulation in the lee of the ridge is the result of the measured local blowing snow conditions.

scholarly journals Radar measurements of blowing snow off a mountain ridge

2019 ◽  
Author(s):  
Benjamin Walter ◽  
Hendrik Huwald ◽  
Josué Gehring ◽  
Yves Bühler ◽  
Michael Lehning
Keyword(s):  
Flood Hazard ◽  
Low Cost ◽  
Snow Accumulation ◽  
Travel Distance ◽  
Storm Event ◽  
Height Distribution ◽  
Blowing Snow ◽  
Mountain Ridge ◽  
Mountainous Regions ◽  
Mass Fluxes

Abstract. Modelling and forecasting wind-driven redistribution of snow in mountainous regions with its implications on avalanche danger, mountain hydrology or flood hazard is still a challenging task often lacking in essential details. Measurements of drifting and blowing snow for improving process understanding and model validation are typically limited to point measurements at meteorological stations, providing no information on the spatial variability of horizontal mass fluxes or even the vertically integrated mass flux. We present a promising application of a compact and low-cost radar system for measuring and characterizing larger scale (hundreds of meters) snow redistribution processes, specifically blowing snow off a mountain ridge. These measurements provide valuable information of blowing snow velocities, frequency of occurrence, travel distances and turbulence characteristics. Blowing snow velocities measured with the radar are validated by comparison against wind velocities measured with a 3D ultrasonic anemometer. A minimal blowing snow travel distance of 60–120 m is reached in 10–20 % of the time during a snow storm, depending on the strength of the storm event. The relative frequency of transport distances decreases exponentially above the minimal travel distance, with a maximum measured distance of 280 m. The travel distance is linearly correlated with the wind velocity, revealing a threshold for snow particle entrainment and transport of 6.75 m s−1. Turbulence statistics did not allow to draw a conclusion on whether low-level low-turbulence jets or highly turbulent gusts are more effective in transporting blowing snow over longer distances. Drone-based photogrammetry measurements of the spatial snow height distribution revealed increased snow accumulation in the lee of the ridge being the result of the measured local blowing snow conditions.

scholarly journals The importance of wind-blown snow redistribution to snow accumulation on Bellingshausen Sea ice

2011 ◽  
Vol 52 (57) ◽  
pp. 271-278 ◽  
Author(s):  
Katherine C. Leonard ◽  
Ted Maksym
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Wind Speeds ◽  
Antarctic Sea Ice ◽  
Drifting Snow ◽  
Bellingshausen Sea ◽  
Ice Mass Balance ◽  
High Winds

AbstractSnow distribution is a dominating factor in sea-ice mass balance in the Bellingshausen Sea, Antarctica, through its roles in insulating the ice and contributing to snow-ice production. the wind has long been qualitatively recognized to influence the distribution of snow accumulation on sea ice, but the relative importance of drifting and blowing snow has not been quantified over Antarctic sea ice prior to this study. the presence and magnitude of drifting snow were monitored continuously along with wind speeds at two sites on an ice floe in the Bellingshausen Sea during the October 2007 Sea Ice Mass Balance in the Antarctic (SIMBA) experiment. Contemporaneous precipitation measurements collected on board the RVIB Nathaniel B. Palmer and accumulation measurements by automated ice mass-balance buoys (IMBs) allow us to document the proportion of snowfall that accumulated on level ice surfaces in the presence of high winds and blowing-snow conditions. Accumulation on the sea ice during the experiment averaged <0.01 m w.e. at both IMB sites, during a period when European Centre for Medium-Range Weather Forecasts analyses predicted >0.03 m w.e. of precipitation on the ice floe. Accumulation changes on the ice floe were clearly associated with drifting snow and high winds. Drifting-snow transport during the SIMBA experiment was supply-limited. Using these results to inform a preliminary study using a blowing-snow model, we show that over the entire Southern Ocean approximately half of the precipitation over sea ice could be lost to leads.

scholarly journals Geomorphological analysis of wetland distribution on various spatial scales

2019 ◽  
Vol 2 ◽  
pp. 1-5
Author(s):  
Natsuki Sasaki ◽  
Toshihiko Sugai
Keyword(s):  
Spatial Scales ◽  
Snow Accumulation ◽  
Gis Analysis ◽  
Aerial Photo Interpretation ◽  
Mountainous Regions ◽  
Multiple Spatial Scales ◽  
Wetland Distribution ◽  
Using Data ◽  
Geomorphological Analysis ◽  
Northern Japan

<p><strong>Abstract.</strong> This study introduces some case analyses of wetland distribution on various spatial scales, from nationwide to the area of a wetland group, with a focus on geomorphological feature. Then described the usefulness of GIS analysis in wetland research. The nationwide wetland distribution in Japan showed that wetland density was high at less than 200&amp;thinsp;m and around 1600&amp;ndash;2000&amp;thinsp;m. Wetlands in mountainous regions were concentrated in snowy Quaternary volcanic regions from the center to the northern part of Japan. This implied snow accumulation and topography of volcanic mountains are important for wetland formation. Secondly, we clarified that wetlands were mainly distributed on the gentle slope of original volcanic surfaces and in landslides in the Hachimantai volcanic groups, in the northern Japan, using 10-m grid DEM and aerial photo interpretation. With the higher-resolution data, it was clear that wetlands were arranged depending on the microtopography of landslides and volcanic surfaces and groundwater. Using data with resolution suitable for the target topographical size and combining the results of multiple spatial scales/resolutions, we can understand the origin of wetlands in more detail.</p>

scholarly journals Variation of diurnal Turbulence Kinetic Energy using sonic anemometer observation: a case study Ledeng, Bandung

2018 ◽  
Author(s):  
Sandy Hardian Susanto Herho ◽  
Dasapta Erwin Irawan
Keyword(s):  
Kinetic Energy ◽  
Diurnal Variation ◽  
Wind Velocity ◽  
Sonic Anemometer ◽  
Diurnal Variations ◽  
Turbulence Kinetic Energy ◽  
Atmospheric Conditions

Sonic anemometer observation was performed on 29 - 30 September 2014 in Ledeng, Bandung to see diurnal variations of Turbulence Kinetic Energy (TKE) that occurred in this area. The measured sonic anemometer was the wind velocity components u, v, and w. From the observation result, it can be seen that the diurnal variation that happened was quite significant. The maximum TKE occurs during the daytime when atmospheric conditions tend to be unstable. TKE values were small at night when atmospheric conditions are more stable than during the daytime.

scholarly journals The interaction between drifting snow and atmospheric turbulence

1998 ◽  
Vol 26 ◽  
pp. 167-173 ◽  
Author(s):  
Richard Bintanja
Keyword(s):  
Surface Layer ◽  
Wind Speed ◽  
Atmospheric Surface Layer ◽  
Order Approximation ◽  
Atmospheric Stability ◽  
Blowing Snow ◽  
Speed Profile ◽  
Stability Parameters ◽  
Wind Speed Profile ◽  
First Order

This paper presents a modelling study of the influence of suspended snow on turbulence in the atmospheric surface layer. Turbulence is diminished in drifting and blowing snow, since part of the turbulent energy is used to keep the particles in suspension. This decrease in turbulence directly affects the vertical turbulent fluxes of momentum and snow particles (and other scalars), and can effectively be simulated by introducing an appropriate Richardson number to account for the stability effects of the stably stratified air-snow mixture. We use a one-dimensional model of the atmospheric surface layer in which the Reynolds stress and turbulent suspended snow flux are parameterized in terms of their mean vertical gradients (first-order closure). The model calculates steady-state vertical profiles of mean wind speed, suspended snow mass in 16 size classes and stability parameters. Using the model, the influence of snowdrifting on the wind-speed profile is quantified for various values of the initial friction Velocity (which determines the steepness of the initial wind-speed profile). It will be demonstrated why the roughness length appears to increase when snowdrifting occurs. Finally, we present a parameterization of the effects of snowdrifting on atmospheric stability which can be used in data analyses as a first-order approximation.

scholarly journals TOPOFIRE: A Topographically Resolved Wildfire Danger and Drought Monitoring System for the Conterminous United States

2019 ◽  
Vol 100 (9) ◽  
pp. 1607-1613 ◽  
Author(s):  
Zachary A. Holden ◽  
W. Matt Jolly ◽  
Alan Swanson ◽  
Dyer A. Warren ◽  
Kelsey Jencso ◽  
...  
Keyword(s):  
United States ◽  
Snow Water Equivalent ◽  
Wildland Fire ◽  
Early Warning Systems ◽  
The United States ◽  
Snow Accumulation ◽  
Assessment System ◽  
Shortwave Radiation ◽  
Mountainous Regions ◽  
Proactive Management

AbstractPatterns of energy and available moisture can vary over small (<1 km) distances in mountainous terrain. Information on fuel and soil moisture conditions that resolves this variation could help to inform fire and drought management decisions. Here, we describe the development of TOPOFIRE, a web-based mapping system designed to provide finely resolved information on soil water balance, drought, and wildfire danger information for the contiguous United States. We developed 8-arc-second-resolution (~250 meter) daily historical, near real-time, and 4-day forecast radiation, temperature, humidity, and snow water equivalent data and used these grids to calculate a suite of drought and wildfire danger indices. Large differences in shortwave radiation and surface air temperature with aspect contribute to greater snow accumulation and delays in melt timing on north-facing slopes, delaying fuel conditioning on shaded slopes. These datasets will help advance our understanding of the role of topography in wildland fire spread and ecological effects. Integration with national programs like the Wildland Fire Assessment System, the Wildland Fire Decision Support System, and drought early warning systems could support more proactive management of wildland fires and refine the characterization of drought in mountainous regions of the United States.

scholarly journals Blowing snow in coastal Adélie Land, Antarctica: three atmospheric-moisture issues

2014 ◽  
Vol 8 (5) ◽  
pp. 1905-1919 ◽  
Author(s):  
H. Barral ◽  
C. Genthon ◽  
A. Trouvilliez ◽  
C. Brun ◽  
C. Amory
Keyword(s):  
Moisture Content ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Atmospheric Moisture ◽  
Atmospheric Models ◽  
Moisture Fluxes ◽  
Measurement Uncertainties ◽  
Strong Winds ◽  
The Impact ◽  
Atmospheric Moisture Content

Abstract. A total of 3 years of blowing-snow observations and associated meteorology along a 7 m mast at site D17 in coastal Adélie Land are presented. The observations are used to address three atmospheric-moisture issues related to the occurrence of blowing snow, a feature which largely affects many regions of Antarctica: (1) blowing-snow sublimation raises the moisture content of the surface atmosphere close to saturation, and atmospheric models and meteorological analyses that do not carry blowing-snow parameterizations are affected by a systematic dry bias; (2) while snowpack modelling with a parameterization of surface-snow erosion by wind can reproduce the variability of snow accumulation and ablation, ignoring the high levels of atmospheric-moisture content associated with blowing snow results in overestimating surface sublimation, affecting the energy budget of the snowpack; (3) the well-known profile method of calculating turbulent moisture fluxes is not applicable when blowing snow occurs, because moisture gradients are weak due to blowing-snow sublimation, and the impact of measurement uncertainties are strongly amplified in the case of strong winds.

scholarly journals Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas*

2015 ◽  
Vol 16 (5) ◽  
pp. 2169-2186 ◽  
Author(s):  
Stefanie Jörg-Hess ◽  
Nena Griessinger ◽  
Massimiliano Zappa
Keyword(s):  
Snow Water Equivalent ◽  
Similarity Score ◽  
Snow Accumulation ◽  
Ensemble Prediction ◽  
Regional Patterns ◽  
Ensemble Prediction System ◽  
Mountainous Regions ◽  
Runoff Volume ◽  
Probabilistic Forecasts ◽  
Snow Water

Abstract Good initial states can improve the skill of hydrological ensemble predictions. In mountainous regions such as Switzerland, snow is an important component of the hydrological system. Including estimates of snow cover in hydrological models is of great significance for the prediction of both flood and streamflow drought events. In this study, gridded snow water equivalent (SWE) maps, derived from daily snow depth measurements, are used within the gridded version of the conceptual hydrological model Precipitation Runoff Evapotranspiration Hydrotope (PREVAH) to replace the model SWE at initialization. The ECMWF Ensemble Prediction System (ENS) reforecast is used as meteorological input for 32-day forecasts of streamflow and SWE. Experiments were performed in several parts of the Alpine Rhine and the Thur River. Predictions where modeled SWE estimates were replaced with SWE maps could successfully enhance the predictability of SWE up to a lead time of 25 days, especially at the beginning and the end of the snow season. Additionally, the prediction of the runoff volume was improved, particularly in catchments where the snow accumulation, and thus the runoff volume, had been greatly overestimated. These improvements in predictions have been made without affecting the ability of the forecast system to discriminate between the different runoff volumes observed. A spatial similarity score was first used in the context of SWE forecast verification. This confirmed the findings of the time series analysis and yielded additional insight on regional patterns of extended range SWE predictability.

A Distributed Snow-Evolution Modeling System (SnowModel)

2006 ◽  
Vol 7 (6) ◽  
pp. 1259-1276 ◽  
Author(s):  
Glen E. Liston ◽  
Kelly Elder
Keyword(s):  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Vegetation Type ◽  
Atmospheric Model ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Canopy Interception ◽  
Meteorological Forcing ◽  
Spatially Distributed ◽  
Evolution Modeling

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.

Sign in / Sign up

Export Citation Format

Share Document