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.
Introduction
Seasonal and permanent snow covers in mountainous regions are of economic
and environmental importance worldwide and may affect communities in a wide
range of aspects: for example, flood hazard, avalanche danger, drinking water
supply, hydropower production, lowland irrigation, ecosystem function or
winter tourism (e.g. Grünewald et al., 2018; Beniston et al., 2018). The
spatial variability of a mountain snow cover is therefore of great interest
for various disciplines like natural hazard assessment, hydrology,
meteorology or climatology. Orographic precipitation in mountainous regions
affects the snow cover variability on larger scales (mountain range scale;
e.g. Mott et al., 2014), whereas preferential deposition (ridge scale; e.g.
Lehning et al., 2008; Gerber et al., 2019; Comola et al., 2019) and blowing and
drifting snow (slope scale; e.g. Shook and Gray, 1996; Schön et al., 2015;
Gerber et al., 2018; Sharma et al., 2019) are typically responsible for local
snow redistribution. The first two processes are categorized as
pre-depositional, and the third one as post-depositional accumulation
processes. For blowing snow, the snow particles are in suspension, whereas
they follow parabolic ballistic paths near the surface (saltation) for
drifting snow (e.g. Bagnold, 1941; Walter et al., 2014). The local mass change
rate dM/dt (M being equivalent to the snow water equivalent, SWE) of the snowpack
(Armstrong and Brun, 2008),
dMdt=P-∇Dbs-Ebs±E-R,
depends on the precipitation rate P, the horizontal redistribution rate
Dbs of surface snow by wind (drifting and blowing snow), the sublimation
rate of blowing snow Ebs, sublimation/evaporation (loss of mass) or
condensation/deposition (gain of mass) rates E at the surface, and the
runoff rate R of liquid water at the bottom of the snowpack. The objective of
this study is to gain a better understanding of the horizontal
redistribution of surface snow (Dbs, mass per unit length per unit time)
in mountainous terrain, especially of blowing snow off mountain ridges. To
date, horizontal redistribution of snow is rather poorly investigated,
difficult to measure and consequently insufficiently quantified. Because
sublimation rates Ebs of blowing snow (e.g. Groot Zwaaftink et al., 2011;
Sharma et al., 2018) directly depend on the mass flux and the time snow
particles are in suspension, our investigations are also relevant for better
estimates of Ebs.
Despite substantial advances being made in understanding and modelling
blowing snow and the resulting snow cover variability in mountainous regions
(e.g. Guyomarc'h and Mérindol, 1998; Naaim-Bouvet et al., 2010; Gerber et al., 2018; Mott et al., 2018), there is still a significant lack of in-situ
measurements to better understand and characterize pre- and
post-depositional accumulation processes. Point measurements of drifting and
blowing snow with snow particle counters (SPCs, Niigata Co.; e.g. Nishimura et al., 2014; Guyomarc'h et al., 2019), for example at meteorological stations in
mountainous terrain, do not allow for general conclusions on the spatial
characteristics of snow redistribution, not even in rather close vicinity of
the station (e.g. Naaim-Bouvet et al., 2010; Nishimura et al., 2014; Aksamit
and Pomeroy, 2016). Naaim-Bouvet et al. (2010) used point measurements of the
wind velocity and snow particle flux at a mountain pass to parameterize and
validate a numerical model of drifting snow. Nishimura et al. (2014)
measured snow particle velocities and mass fluxes using an SPC and found
snow particles to be about 1–2 m s-1 slower than the wind speed below a
height of 1 m. Aksamit and Pomeroy (2016) introduced an outdoor application
of particle-tracking velocimetry (PTV) of near-surface blowing snow
investigating the complex surface flow dynamics. Despite providing valuable
knowledge on process understanding, none of those studies provides spatially
resolved measurements on larger scales (>10 m).
Spatially continuous measurements using remote sensing techniques like
radar, for observing blowing snow, in combination with lidar (light
detection and ranging) or photogrammetry measurements (e.g. Schirmer et al., 2011; Picard et al., 2019), to capture the spatio-temporal snow depth
variability, may thus provide valuable information for improving our
understanding and modelling of drifting and blowing snow and its
spatio-temporal variability. First attempts at measuring blowing snow across
a mountain ridge to estimate additional snow deposition on steep lee slopes
for the local avalanche warning in Davos were presented by Föhn (1980).
Space-born images of a huge, about 15 to 20 km long snow plume at Mount
Everest have been related to local wind and weather conditions by Moore (2004). Geerts et al. (2015) used airborne radar and lidar data to show that
small fractured blowing snow ice crystals may enhance snow growth in clouds.
Nishimura et al. (2019) recently applied 15 SPCs and ultra-sonic
anemometers on a flat field to reveal the spatio-temporal structures of
blowing snow near the surface and explore the interaction with the turbulent
flow structures. Several studies have simulated wind-affected snow redistribution
and accumulation by relating atmospheric wind fields to resulting snow
deposition patterns in mountainous terrain (Dadic et al., 2010; Winstral et al., 2013; Mott et al., 2014; Vionnet et al., 2017; Gerber et al., 2017; Wang
and Huang, 2017). Flow structures around a utility-scale 2.5 MW wind turbine
have previously been measured by Hong et al. (2014) using a field particle-imaging velocimetry (PIV) set-up with snow precipitation as the tracer
particles. Their results provide significant insights into the Reynolds
number similarity issues presented in wind energy applications.
Radar is often used for snow avalanche detection (e.g. Vriend et al., 2013)
and to capture avalanche flow structures and velocities. Kneifel et al. (2011) analysed the potential of a low-power FM-CW K-band radar (Micro Rain Radar, MRR) for snowfall observation, a method that was further improved by
Maahn and Kollias (2012).
This study makes use of ground radar measurements
of blowing snow particle clouds off a mountain ridge using an MRR instrument
to evaluate the potential of remote sensing techniques in characterizing
pre- and post-depositional accumulation processes. The goal is to relate
measured particle cloud characteristics like velocity distribution,
transport distance and direction, and turbulence intensities to the
prevailing wind conditions and the subsequent snow accumulation in the
vicinity. Our analysis provides a first insight into the potential of radar
measurements for determining blowing snow characteristics, improves our
understanding of mountain ridge blowing snow events and provides a valuable
data basis for validating coupled numerical weather and snowpack
simulations.
The instrumentation and methods used in this study are introduced in Sect. 2. In Sect. 3, the measured blowing snow particle cloud characteristics,
meteorological conditions and snow distributions are presented, discussed
and related to each other. A summary of the results and the conclusions from
this research can be found in Sect. 4.
Methods
An MRR was set up as a part of a meteorological snow drift
station (SDS) on top of the Gotschnagrat mountain ridge at 46∘51.5116′ N, 9∘50.9207′ E (Davos-Klosters, Switzerland) at an altitude
of 2281 m a.s.l. to investigate drifting and blowing snow. The station was
part of the Role of Aerosols and Clouds Enhanced by Topography on Snow
(RACLETS) campaign, which took place in February and March 2019 in the area
of Davos-Klosters. The data collected during the campaign, including those
used in this study, have been made publicly available (Walter and Huwald, 2019; Walter et al., 2019). The MRR
is a radar measuring the full Doppler spectrum and operating at a frequency
of 24 GHz. It is manufactured by Meteorologische Messtechnik GmbH (METEK,
Germany). The MRR is originally designed as a vertically pointing radar for
measuring clouds and precipitation (Peters et al., 2002, 2005). In this
study, the MRR was tilted 90∘, pointing horizontally to measure
the particle velocity relative to the antenna direction (Doppler velocity)
and the distance of blowing snow off the Gotschnagrat mountain ridge (Fig. 1). The Doppler spectrum provides for each Doppler velocity bin the power
backscattered from particles within the specific velocity range. From this,
one can determine the mean Doppler velocity v¯ and the spectrum width
σv, which are defined as
2v¯=1P∫-vnyvnyv⋅S(v)dv,3σv2=1P∫-vnyvnyv-v¯2⋅S(v)dv,
where P=∫-vnyvnyS(v)dv is the mean power of the
spectrum and S(v) is the spectral power. Note that v is weighted by S(v) at each
Doppler velocity bin. Since the backscattered power is more sensitive to the
size of the particles than their concentration, v represents the Doppler
velocity weighted by the size of the particles. The Doppler spectrum
represents the distribution of particle velocities relative to the radar. In
a given radar volume, particles typically move with different velocities due
to wind turbulence, so v is a measure of the mean displacement of the
particles relative to the radar and σv is the standard
deviation of the Doppler spectrum. In the case of a horizontally pointing
antenna, v¯ and σv (hereinafter referred to as
vMRR and σv,MRR) can be interpreted as a measure of the
mean horizontal wind velocity and turbulence. The MRR turbulence intensity
IMRR in the direction of the MRR's field of view is defined as
IMRR=σv,MRRvMRR,
where the standard deviation σv,MRR of the MRR radial
velocity within each range gate is determined from the spectral width of the
Doppler spectrum for each averaging period Ti. The definition of
IMRR includes the assumption that, within each range gate of length
δr and for each time interval Ti, the MRR velocity is normally
distributed around the mean velocity vMRR. This assumption is supported
by the good agreement between the MRR turbulence intensity IMRR and the
turbulence intensity ISonic determined from a 3D ultra-sonic anemometer
(hereinafter referred to as Sonic) as will be shown in Sect. 3.2.
Three MRR evaluation periods (EPs) are in the focus of this study: (1) 04:00–10:00 UTC+1 on 4 March 2019 (EP1); (2) 18:00 UTC+1 on 6 March 2019 to 02:00 UTC+1 on 7 March 2019 (EP2); and (3) 11:00–19:00 UTC+1 on 14 March 2019
(EP3). EP1 and EP2 are the only ones during the RACLETS campaign with strong
blowing snow events in the absence of precipitation. Because the radar
signal is backscattered by all snow particles in the air, the distance of
pure blowing snow events can only be obtained without precipitation. Because
both events occurred not in between two drone flights (discussed below), EP3
was included in the analysis, although it was a precipitation event. On
21 March 2019, the MRR and the instruments of the SDS were dismantled.
Different MRR parameter settings were tested during the RACLETS campaign to
find the best setting for detecting blowing snow off mountain ridges. The
most important parameters were those defining the distance and velocity
resolution. Table 1 provides a brief overview of the MRR instrument
configuration used in this study (more information in Maahn and Kollias, 2012
and MRR Pro Manual, 2016). It is possible to set the following five MRR
configuration parameters: (i) the number of range gates N=32, 64, 128 or
256, where a range gate defines a measurement volume of a certain length in
the MRR pointing direction; (ii) the range gate length δr
(>10 m) (the maximum measurement distance dmax is thus
defined by N×δr); (iii) the number of lines in spectrum m=32, 64, 128 or 256, which controls the velocity resolution; (iv) the height above
sea level H of the MRR installation site (this parameter is used for
assumptions to compute rain rate from spectral power; since it is not
relevant for this study, it was set to 0); and (v) the averaging time Ti>1 s of the power spectra defining the temporal resolution of
the MRR products (MRR Pro Manual, 2016).
(a) Picture of the study site: the Micro Rain Radar (MRR) is
looking horizontally from the ridge, measuring the radial velocity and
distance of blowing snow clouds across the valley. (b) Transect of the
topography in the viewing direction of the MRR (aspect ratio is 1:1).
The first range gate was removed for the analysis, since it is affected by
near-field effects. The first useable range gate covers the range 20 to 40 m,
and the maximum measurement distance was dmax=1280 m for EP1 on
4 March 2019 (Table 1). The half-power beam width of the MRR is 1.5∘, resulting in a beam expansion of about 1.3 m at 100 m. The Nyquist velocity
range is inverse proportional to the number of range gates N (MRR Pro Manual,
2019) and was at the minimum for EP1 with vny=24 m s-1. The velocity resolution δv of the MRR radial velocity
vMRR is given by vny/m. Because the wind direction was expected to
vary depending on the general weather situation, with snow potentially being
blown either away or towards the MRR, the available velocity range
vny was set symmetrically to 0, resulting in an actual velocity range
vact=±vny/2 (Table 1). Velocities of |vMRR|>|vact| result in aliasing
(Tridon et al., 2011) but can be corrected for by applying a dealiasing
procedure based on vdealiased=vMRR+n⋅vny, where
n is the dealiasing number (integer of -1 if the lower limit of the Nyquist
interval is exceeded and +1 if the upper limit is exceeded). However,
particle velocities |vMRR|>|vact| were rare. Another possible source of uncertainty for the
Doppler velocity is the effect of ground clutter at small range gates, where
the beam is not properly formed. However, since the MRR was installed at the
edge of a steep slope (30∘, Fig. 1b), the effects of ground
echoes on the measured Doppler velocity can be neglected. Furthermore, it is
difficult to quantify an uncertainty on the mean Doppler velocity
vMRR that is a moment of a distribution, the Doppler spectrum. The
measure of the Doppler velocity itself is relatively precise; i.e. it depends
on the precision of the clock in the radar. It is more uncertain to what
extent the mean Doppler velocity is representative of the movement of the
particles within a range gate. However, the main wind direction was
typically well aligned with the MRR view direction, and the velocity
fluctuations induced by turbulence is assumed to be normally distributed
around the mean so that the mean Doppler velocity vMRR well represents
the mean wind or particle velocity within a range gate. The averaging time
was set to Ti=5 s for EP1 and Ti=10 s for EP2 and EP3.
Providing a recommendation for an ideal MRR parameter combination is
difficult, as it depends on the transport distance and velocity of the
blowing snow events. Based on the results of this study, we recommend
starting with a number of (N=32) short (δr=10 m) range gates
resulting in a high distance resolution, a typically sufficient maximum
measurement distance of 320 m and a high Nyquist frequency of vny=48 m s-1 (vact=±24 m s-1). A maximum possible
value of m=256 for the number of lines in spectrum results in a high
velocity resolution of δv=0.19 m s-1. An averaging time of
Ti=5 s seems to result in a sufficient temporal resolution without
producing too much data while still capturing the major flow variability.
MRR parameter settings (parameters 1–5) for the three different
evaluation periods investigated and the resulting MRR limits (parameters 6–9):
PARAMETER:EP1:EP2:EP3:4 March 20196–7 March 201914 March 20191. Number of range gates:N6432162. Range gate length:δr [m]2040403. Number of lines in spectrum:m641281284. Height above sea level:H [m]0005. Averaging time:Ti [s]510106. Maximum distance:dmax [m]128012806407. Nyquist velocity range:vny [ms-1]2448968. Actual velocity range:vact [ms-1]±12±24±489. Velocity resolution:δv [ms-1]0.380.380.75
Among the standard products of the METEK processed data, the mean MRR radial
velocity vMRR and the spectrum width σv,MRR obtained for
each averaging period Ti are of primary interest in the subsequent
analysis, providing information on the blowing snow particle cloud
velocities and turbulence intensities. Furthermore, the last range gate
reflecting the MRR signal defines the blowing snow travel distance d in the
MRR pointing direction for each averaging period Ti. Finally, the radar
reflectivity Z, which mainly depends on the particle size, provides an
indication of blowing snow particle sizes. The determination of blowing snow
particle cloud concentrations and a mass flux is not possible, since there
is no quantitative relationship between the spectral power and the particle
size distribution for snow. Nevertheless, the MRR measurements provide other
interesting characteristics of blowing snow events as discussed in the
following sections.
The MRR was mounted at the edge of a few-hundred-metre-wide flat mountain
ridge transitioning into a 30∘ slope defining the accumulation
zone. A transect of the topography of the test site in the direction of the
MRR's field of view (Fig. 2a) is shown in Fig. 1b. The MRR was oriented at
an azimuth angle of 22∘ (clockwise with respect to north; see Fig. 2a). Note that the MRR radial velocity and turbulence characteristics
determined from the MRR Doppler spectra are meant exclusively in the
direction of the field of view of the MRR. However, the wind direction
α was typically along the MRR pointing direction; thus the MRR
radial velocity is typically close to the blowing snow absolute velocity.
At about 5 m from the MRR, sensors of the SDS were mounted on a
mast. The present study uses measurements of the three wind components (u,
v, w) and the wind direction (α) measured with a 3D ultra-sonic
anemometer (R. M. Young 81000) at a height of 1.5 m above ground at a
sampling frequency of 20 Hz.
Two drone flights were performed on 12 and 29 March 2019 with
the SenseFly eBee+ RTK fixed-wing Unmanned Aerial System (UAS) to
photogrammetrically map the local snow height changes due to pre- and
post-depositional snow redistribution processes in between these
measurements. Photogrammetric snow depth mapping with UAS has proven to be
an accurate and reliable method if capturing the spatial variability in high
alpine terrain with accuracies in the range of 5 to 30 cm (Bühler et al., 2016, 2017; Harder et al., 2016; Redpath et al., 2018). As a
meaningful distribution of ground control points in the steep and dangerous
slope was not possible, we applied integrated sensor orientation using
the UAS GNSS measurements (mean positioning accuracy: 2.5 cm). This approach
proved to be valid for accurate georeferencing (Benassi et al., 2017). This
is also supported by several studies we performed for snow depth mapping
applying ground control points (Bühler et al., 2018; Noetzli et al., 2019). For both flights we had a mean flight height above ground of 190 m
resulting in a ground sampling distance (GSD) of about 4 cm. However, on
12 March 2019, wind gusts with high velocities up to 18 m s-1 occurred,
which led to deviations of the plane along the flight lines, resulting in a
reduced overlap of the imagery. Therefore, some photogrammetric noise is
present in the resulting digital surface model (DSM), reducing its accuracy
(Fig. 2a). No such noise is present in the data acquired on 29 March 2019, a
day with calm wind conditions. We produced two 10 cm resolution DSMs and
calculated the elevation difference by subtracting them (Fig. 2a).
MRR reflectivity for (a) part of EP2 (6–7 March 2019) for pure
blowing snow events and (b) EP3 (14 March 2019) for blowing snow with concurrent
snow precipitation.
Results and discussionThe radar reflectivity
The radar reflectivity Z is proportional to the fourth power of the diameter
for snow particles (Ryzhkov and Zrnic, 2019) and is thus mainly affected by the snow
particle size and less so by the concentration as discussed before. The low
reflectivity values of the measured pure blowing snow clouds (Fig. 3a),
compared to the higher reflectivity of precipitation snowflakes (Fig. 3b),
imply that the measured blowing snow clouds were composed of rather small
particles. This is consistent with other findings of drifting and blowing
snow investigations where small particle sizes of typically
50–500 µm were detected (Nishimura and Nemoto, 2005; Gromke et al., 2014)
compared to precipitation snowflakes that can have diameters of several
millimetres (e.g. Gergely et al., 2017). The lower reflectivities closer to
the ridge (d=0–200 m) compared to further away (d>300 m)
for the precipitation event (Fig. 3b) indicate smaller blowing snow
particles due to higher wind speeds near the mountain ridge, whereas further
away larger precipitation particles potentially dominate the backscatter of
the radar signal.
(a) MRR radial velocity in the azimuth direction 22∘ for
a 2 min period containing four different blowing snow events on
4 March 2019. (b) Corresponding turbulence intensity I, Sonic (c) wind velocity
(absolute and in the direction 202∘) and (d) turbulence intensity
for 5 s intervals.
Radial velocity and turbulence intensity: exemplary cases
The MRR radial velocity vMRR (Eq. 2) within a range gate is computed as
the average of the MRR Doppler spectrum (MRR Pro Manual, 2016) and is
directly related to the blowing snow particle cloud velocity in the viewing
direction of the MRR. In this section we introduce the basic MRR data by
means of four exemplary blowing snow events (Fig. 4), including a brief
discussion and interpretation of the results, as these data form the basis
for the analyses presented in the following sections. Figure 4a shows the MRR
radial velocity vMRR of the four blowing snow events of different
characteristics within a 2 min time frame during EP1. The first event
(no. 1) lasted for 25 s with a constant transport distance of 60 m. For the
subsequent range gates (>60 m), no snow particles were in the
field of view of the MRR anymore (Fig. 1b). The assumption is that the snow
was blown off the ridge horizontally by up to about 60 m before it started
settling, either resulting in local accumulation or being further advected
closer to the ground, and thus leaving the field of view of the MRR. Event
no. 1 started with relatively high MRR radial velocities of about
vMRR=10–11 m s-1, while the velocities gradually decreased to
about vMRR=7–8 m s-1 towards the end of this event. The Sonic
wind velocities (Fig. 4c) are in good agreement, also decreasing to about
vSonic=8 m s-1 towards the end of event no. 1. The turbulence
intensity IMRR=0.06–0.12 of this first event (Fig. 4b) shows low-velocity fluctuations of the particle cloud, indicating a rather stable
low-level, low-turbulence jet, which is supported by the Sonic turbulence
intensities (Fig. 4d). The velocity drop at the end of event no. 1 is likely
the reason for the break in snow being blown off the ridge between event nos. 1 and 2.
(a) Temporal evolution of the horizontal transport distance of all
blowing snow events of EP1 (4 March, 04:00–10:00 UTC+1). (b) Wind velocity parallel to the MRR direction (202∘) measured with the Sonic compared to the close range (20–40 m) blowing snow radial
velocities measured with the MRR (see Fig. 4a). (c) Wind direction (mainly
180–220∘) and (d) momentum flux -u′w′ calculated
using the Sonic data.
Blowing snow event no. 2 is different, starting with lower radial velocities
of about vMRR=9 m s-1, likely initiated by higher
wind velocities starting around 04:16:00 (Fig. 4c), and then suddenly dropping
to about vMRR=6–7 m s-1 during the following 10 s because of
another wind velocity vSonic decrease around 04:16:10 (Fig. 4c). Strong
velocity changes are an indication of turbulent gusts, which is supported by
higher MRR turbulence intensities of up to IMRR=0.27 (Fig. 4b). The
maximum turbulence intensity at the SDS measured with the Sonic in the
direction of the MRR was ISonic=0.25 (Fig. 4d) and
thus in good agreement with the MRR result. However, the temporal agreement
of the peak turbulence intensity is rather poor, as the peak in
ISonic lags the peak in IMRR although it should be vice versa.
Nevertheless, an overall good agreement between the turbulence intensities
measured with the Sonic and that of the first range gate of the MRR is
found, with a mean difference of ΔI= mean (IMRR-ISonic)=0.01 with standard deviation of σΔI=0.09 for the
entire EP1 and EP2. The lower-velocity particle cloud of event no. 2 is
transported further within the field of view of the MRR compared to event
no. 1, resulting in a gradually increasing transport distance starting from
60 and increasing to 80, 120 and finally 140 m after 20 s.
Interestingly, vMRR increases with distance for event no. 2, which
is counter-intuitive, as one would rather expect a decrease of the wind
velocity behind the ridge. However, the highly turbulent flow with changes
in the wind direction and potentially large eddies of up to 100 m is likely
causing this effect of higher velocities at longer distances. Event nos. 3
and 4 both show rather high radial velocities similarly to event no. 1,
which are in good agreement with the Sonic wind velocities (Fig. 4c), but
with slightly higher turbulence intensities, indicating a more turbulent flow
unlike for event no. 1. The transport distances are about 80–100 m for
event nos. 3 and 4.
Based on the above discussion of the four blowing snow events, it seems that
stronger turbulent fluctuations with higher turbulence intensities result in
longer transport distances. This leads us to the hypothesis that not
necessarily low-turbulence jets with high wind velocities but turbulent
gusts with lower wind velocities may be more effective in transporting
blowing snow over longer distances on the lee side of a mountain ridge.
Another explanation could be that the blowing snow cloud is vertically more
extended for turbulent gusts, which increases the likelihood of snow
particles being in the field of view of the MRR (Fig. 1b), whereas for
low-level, low-turbulence jets the particles may rather quickly settle after
a certain distance, leaving the field of view of the MRR. These
considerations are further discussed in Sect. 3.4.
Blowing snow distances
The MRR blowing snow distances d for EP1 are shown in Fig. 5a. Typically, a
minimum distance of about 60 m is reached, whereas longer distances
>100 m appear rather seldom. The distances d and particle cloud
radial velocities vMRR (Fig. 5b) may be smaller than the real absolute
distances and velocities, as blowing snow was detected from various angles (Fig. 5c), not
only straight in the view direction of the MRR as mentioned
earlier. Nevertheless, the main wind direction was typically in overall good
agreement with the view direction (202∘) of the MRR (Fig. 5c),
and the main interest of this
study is in snow being blown off perpendicular to the Gotschnagrat mountain
ridge. A comparison between the MRR radial velocities vMRR of the first
useable range gate (d=40 m) and the horizontal wind velocity
vSonic measured with the Sonic, both for the direction of 202∘, is provided in Fig. 5b. A qualitatively good agreement is found despite
some outliers. Very low MRR velocities around vMRR=2.5 m s-1
are either an instrument artefact because of very low blowing snow particle
concentrations or else caused by wind directions temporarily deviating significantly from
the MRR field-of-view direction. Discrepancies between the MRR and the Sonic
velocities may be the result of the spatial average distance of about 30 m
between the first usable range gate d=40 m (with a measurement volume
extending from 20 to 40 m) and the location of the Sonic in combination with
the slightly varying wind direction. To assess a potential dependency of the
velocity difference on the wind direction, Fig. 6 shows the relative
difference between the MRR and the Sonic velocity as a function of the wind
direction α for EP1–EP3. A positive trend is found with a bias of
vMRR>vSonic for wind directions α>180∘. Nevertheless, an overall good agreement between the MRR
radial and Sonic velocity is found, with a mean difference of
mean ((vMRR-vSonic)/vSonic)=10 % and a standard deviation of
±20 %. The intersection of the linear fit with the vMRR-vSonic=0
line for α=170∘ (Fig. 6) suggests a stable wind
direction in the vicinity of the MRR and the SDS for winds coming from that
direction. This result is most likely strongly related to the local
topography (Fig. 2b) influencing the nearby wind field and direction, where
the mountain station is located west and another SW–NE-oriented mountain
ridge east of the MRR and the SDS, resulting in a rather undisturbed flow
for southerly winds.
Relative difference between MRR and Sonic wind velocity in the
direction 202∘ as a function of wind direction for EP1–EP3.
Figure 5d shows the momentum flux -u′w′ calculated from the Sonic wind
velocities, which is generally positive for EP1, indicating a downward
momentum flux and an increase in wind velocity with height above the
location of the Sonic. However, between 06:15 and 07:00 UTC+1, the
momentum flux was negative, indicating a decreasing wind velocity with
height above the Sonic and the presence of a low-level jet close to the
ground constantly blowing from a direction of 180∘ (south). During
this time period, the wind velocity was highest, at up to 12–13 m s-1, and long blowing snow distances were reached of typically
>80 m (Fig. 5a). Furthermore, the best agreement between the
Sonic wind velocity and the MRR radial velocity was found for this period of
stable wind conditions.
Very similar results were found for EP2 (Fig. 7). Longer transport distances
(Fig. 7a) were typically obtained as a result of the higher wind velocities
(Fig. 7b). The wind direction (Fig. 7c) was typically quite stable, although
there were two periods (21:00–22:00 and 23:00–23:30 UTC+1) where
the wind direction varied significantly. The momentum flux (Fig. 7d) was
negative about 50 % of the time, indicating a higher presence of
low-level jets close to the ground compared to EP1.
(a) Temporal evolution of the horizontal transport distance of all
blowing snow events of EP2 (18:00 UTC+1 on 6 March 2019 to 02:00 UTC+1 on 7 March 2019). (b) Wind velocity parallel to the MRR direction (202∘)
measured with a Sonic compared to the close range (40–80 m) blowing snow
radial velocities measured with the MRR. (c) Wind direction (mainly
180–220∘) and (d) momentum flux -u′w′ calculated
using the Sonic data.
Blowing snow statistics
The relative frequency of occurrence of blowing snow transport distances
from Fig. 5a is shown in Fig. 8a for EP1. Eighty percent of the time, no blowing
snow was present or detected by the MRR (transport distance d=0 m). No
events were detected for a distance d=20 m since this range gate cannot
be used as discussed earlier. Only few events were detected for a transport
distance d=40 m, although this range gate delivered continuous
information on radial velocities for higher transport distances d>40 m (Fig. 4a). Therefore, we expect that also for d=20 m
only very few or no events would have been detected by the MRR, resulting in
a gap in the frequency distribution for 0<d>60 m in Fig. 8a. We hypothesize that, if the wind is strong enough and above a threshold
wind speed to entrain and transport snow in suspension, a minimum transport
distance of d=60 m is reached, which occurred for about 10 % of the
total time of observation for EP1 (including the “no blowing
snow” time). For distances d>60 m, the relative
frequency decreases exponentially, with the maximum
distance of d=200 m only observed once. The mean Sonic wind velocity was 7.3 m s-1
during EP1, which is only 6 h long but sampled at a temporal resolution of 5 s, resulting in 4320 samples and thus providing a good data basis for statistics.
Histogram of the transport distance of all blowing snow events for
(a) EP1 (Fig. 5a) and (b) EP2 (Fig. 7a), including exponential fits for
distances larger than the minimal transport distance.
The relative frequency of occurrence of blowing snow distances for EP2
(18:00 UTC+1 on 6 March 2019 to 02:00 UTC+1 on 7 March 2019) is shown in Fig. 8b.
The mean wind velocity of 9.1 m s-1 during the 8 h (10 s sampling)
measured with the Sonic was significantly higher compared to EP1 (7.3 m s-1), resulting in a larger gap before the minimal transport distance
and higher overall transport distances of up to maximum d=280 m. The
higher minimal transport distance of d=120 m compared to EP1 might be the
result of stronger gusts during the more powerful storm of EP2 and the conditions and erodibility of the snow
surface. Despite some differences between the
two distributions in Fig. 8, both show very similar characteristics, with a
gap before a minimal distance is reached and an exponential decay
afterwards. Therefore, those distributions seem to be generally valid,
providing a good representation of the frequency of blowing snow distances
for mountain ridges. A dependency of the minimal transport distance and the
frequency distribution on the strength of the storm event and snow cover
conditions could be investigated in future more detailed studies.
To estimate a threshold wind velocity (e.g. Li and Pomeroy, 1997) and thus
the erodibility of the surrounding snow surface, box plots of the Sonic wind
velocity as a function of the transport distance are provided in Fig. 9. The
median wind velocity increases by about 2 m s-1 for transport
distances increasing from d=40 to 200 m for EP1 and about 5 m s-1
(d=80–280 m) for EP2. An extrapolation of the wind velocity to d=0 m provides an estimate of a threshold velocity of 7.5 m s-1 for EP1and
8.8 m s-1 for EP2, a result that is in overall good agreement with
other studies (e.g. Li and Pomeroy, 1997). Note that the wind velocity threshold
definition for particle transport used in this study, defined for a height
of 1.5 m (Sonic), is similar to that used in Li and Pomeroy (1997), who
defined a threshold wind speed at 10 m above ground. These definitions are
different to the traditional definition of a threshold friction velocity for
particle entrainment and saltation (e.g. Schmidt, 1980; Guyomarc'h and
Mérindol, 1998; Clifton et al., 2006; Walter et al., 2012). The fact that
the estimated threshold for EP2 (Fig. 9b) is 1.3 m s-1 higher than for
EP1 (Fig. 9a) supports our previous hypothesis of different snow surface
conditions with a reduced erodibility for EP2.
Sonic wind velocity as a function of the transport distance of the
blowing snow events for (a) EP1and (b) EP2.
Turbulent gusts at rather low velocities were found to be potentially
responsible for longer transport distances as discussed in Sect. 3.2 (Fig. 4a). To investigate whether these events or low-level, low-turbulence jets
with high wind velocities are more effective in transporting snow over long
distances across a mountain ridge, the turbulence intensities of the last
range gate defining the blowing snow transport distance (Fig. 4b) are
plotted as a function of the transport distance (box plot) in Fig. 10. For
EP1 (Fig. 10a) and distances d≥80 m, the median, the upper and lower
quartiles, the whiskers and the outliers all show a decreasing trend with
increasing distance, indicating that low-level, low-turbulence jets with high
wind velocities are more effective than highly turbulent gusts in
transporting blowing snow over long distances across a mountain ridge for
EP1. Nevertheless, as mentioned earlier, highly turbulent motions still may
result in a higher vertical extension of blowing snow clouds and thus in an
increased likelihood of being within the field of view of the MRR (Fig. 1b)
for long distances. For the stronger storm event of EP2, the turbulence
level was significantly higher, with median intensities of 0.1–0.2
(<0.5 for EP1) (Fig. 10b), supporting the latter assumption.
Strong low-turbulence jets may also result in a slight downward air flow
right after the ridge, and the blowing snow may quickly settle, thereby getting out of
the field of view of the MRR. The turbulence statistics shown in Fig. 10 do
thus not allow a conclusion to be drawn on whether low-level, low-turbulence jets
or turbulent gusts are more effective in transporting blowing snow over
longer distances. However, highly turbulent flows are more likely to bring
particles to greater heights and thus influence cloud processes.
Measurements with a two-MRR system oriented parallel at different heights
could provide a conclusion on which of the two events is more effective in
transporting snow over longer distances across mountain ridges.
Turbulence intensity determined from the MRR spectral width of
the Doppler spectrum of the range gate defining the blowing snow transport
distance (Fig. 4a) as a function of transport distance for (a) EP1 and (b) EP2.
Snow height distribution
To provide a first connection between mountain ridge blowing snow events and
a subsequent snow height distribution in the vicinity, the measured snow
height distribution (Fig. 2a) is discussed in the context of prevailing
precipitation and wind conditions and related to the analysed blowing snow
events in this section. The spatial variation in snow height difference
between 29 and 12 March 2019 of the investigated area around the MRR
(Fig. 2a) shows distinct patterns as a result of pre- and post-depositional
accumulation and erosion processes. Deep blue and deep red spotted
areas of maximum snow depth differences are an artefact from wind gusts
affecting the drone flights on 12 March 2019, resulting in erroneous
photogrammetry measurements (see Sect. 2). Nevertheless, the
smooth areas of the snow depth map show that significant snow deposition
occurred north of the SDS in between the two drone flights, while other
regions were eroded.
Precipitation event (EP3) on 14 March 2019 with strong wind from the
south resulting in blowing snow and preferential deposition north of the
snow drift station as shown in Fig. 2a. (a) Sonic wind velocity and MRR
radial velocity, (b) wind direction and (c) momentum flux -u′w′ calculated
using the Sonic data (similar to Figs. 5 and 7).
The increased snow accumulation north of the MRR shown in Fig. 2a is the
result of a combination of preferential deposition and blowing snow, i.e.
pre- and post-depositional accumulation processes. Although the pure blowing
snow events analysed in the previous sub-sections took place about a week
prior to this long-term observational period between the two drone flights,
two major snow storm events were found to be responsible for the
accumulation during the 17 d between the two flights on 12 and
29 March 2019. Figure 11a shows a comparison of the Sonic wind velocity and the
MRR radial velocity (similar to Figs. 5b and 7b) for the first
precipitation event on 14 March 2019 (EP3). For this precipitation event, the
MRR particle velocities are also in good agreement with the Sonic wind
velocity at levels of up to 8 m s-1, similar to those of the pure blowing
snow events of EP1 and EP2. The wind direction was also well aligned with the
MRR view axis and quite stable from S to SW (approx. 200∘) for
the entire storm (Fig. 11b). We assume that the wind resulted not only in
preferential deposition during the precipitation event but also in snow on
the ground being entrained and transported during strong gusts from the
ridge to the accumulation zone (Figs. 1b, 2a). This simultaneous appearance
of pre- and post-depositional accumulation processes also occurred during
the second snow storm on 15 March 2019, which was very similar but is not
presented here. The wind rose shown in Fig. 2a summarizes the wind
directions for wind velocities >6 m s-1, thus potentially
blowing snow effectively, for the 9 d period 12 to 21 March 2019. On
the last day, the MRR and the instruments of the SDS were dismantled.
However, although the wind rose does not cover the entire period between the
two drone flights, it clearly shows that the blowing snow effective wind
direction was stable from S to SW at least for the first half of the time
between the two drone flights. Similar transport distances for the blowing
snow events with concurrent precipitation (EP3) to those without (EP1
and EP2) are assumed, based on the similarity of the wind direction and wind
velocity. Therefore, the increased accumulation north of the ridge up to
distances of 200 m (Fig. 2a) are very likely the result of the two blowing
snow events with concurrent precipitation between the two drone flights.
Although the wind velocities for EP3 (Fig. 11a) are slightly smaller than
for EP1 and EP2, probably resulting in smaller transport distances than shown
in Figs. 5a and 7a, the snow likely gets transported closer to
the ground outside the field of view of the MRR before it is finally
deposited, which might explain increased accumulation for distances of up to
d=220 m (Fig. 2a). Although the local topography and the near-ground wind
velocities north of the ridge also influenced the small-scale (metres) snow
height distribution on the ground, the main conclusion is that an overall
good agreement is found between the blowing snow direction, wind velocities,
blowing snow distances and larger-scale (several tens of metres) snow
accumulation pattern.
Summary and conclusions
Our results show that radar measurements of blowing snow may deliver
valuable information to improve our understanding of pre- and
post-depositional snow accumulation or redistribution processes on larger
scales. The Micro Rain Radar (MRR) instrument provides characteristics of
and statistics on blowing snow distances, its frequency of occurrence,
particle cloud velocities and turbulence intensities. We found good
agreement between the MRR blowing snow velocity and the Sonic wind velocity,
and that a minimal horizontal blowing snow transport distance of 60–120 m
is reached in the lee of a mountain ridge, depending on the strength of the
storm event. The relative frequency of occurrence decreases exponentially
for distances longer than the minimal transport distance, with a measured
maximum distance of 280 m in our case. It was not possible to draw a
conclusion on whether low-level, low-turbulence jets or turbulent gusts are
more effective in transporting blowing snow over longer distances in the lee
of a mountain ridge. The increased snow height distribution north of the
measurement location (Fig. 2a) was found to be the result of a combination
of preferential deposition and blowing snow accumulation during at least two
measured and analysed snowstorm events. The presented snow height
distributions together with the characterization of the blowing snow events
provide a valuable data basis for validating coupled numerical weather and
snowpack simulations.
Further investigations are required for more clarification and may
incorporate measurements with a second MRR system oriented parallel at a
slightly different elevation to better resolve the local wind field and
blowing snow events – particularly to capture the process of settling snow
disappearing from the field of view of the upper MRR. The MRR instrument was
also recently tested by the Cryos group at EPFL, Lausanne, Switzerland, for
measuring vertical velocity profiles and temporal
variability of blowing snow in East Antarctica at the site S17 near the Japanese research
station Syowa (unpublished work in progress), where blowing snow layers can
reach a vertical extent of up to 200 m (Palm et al., 2017). The next
challenge for radar specialists will be finding a way to extract particle
concentrations from the radar measurements to estimate particle mass fluxes
or at least order of magnitude. Exploring the potential of horizontally
pointing cloud physics lidar (e.g. Mona et al., 2012) in detecting the
spatio-temporal variability of blowing snow would be worthwhile for the
community interested in characterizing and better understanding pre- and
post-depositional snow accumulation processes in various cold regions
worldwide.
Data availability
The MRR and SDS data for the entire RACLETS campaign are available on the
ENVIDAT data repository (Walter and Huwald, 2019; Walter et al., 2019).
Author contributions
BW, HH and ML designed the experiments, and BW carried them out. JG provided
MRR support, and YB conducted the drone flights. BW prepared the manuscript
with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank the SLF workshop for supporting us with the design
and construction of the snow drift station. Furthermore, we would like to
thank the Environmental Remote Sensing Laboratory (LTE) at EPFL, especially
Alexis Berne and Alfonso Ferrone for lending the MRR and for the technical
support with the instrument. Thanks also to Andreas Stoffel, Elisabeth
Hafner and Lucie Eberhard for performing the photogrammetric drone flights;
David Wagner, Felix von Rütte and Beat Nett for their support with the
installation of the snow drift station; and Rebecca Mott for the GIS
support.
Financial support
This research has been supported by the Swiss National Science
Foundation (grant no. 200020-179130 and 200020-175700/1).
Review statement
This paper was edited by Guillaume Chambon and reviewed by Michele Guala and one anonymous referee.
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