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In operational conditions wind is the main environmental source of measurement biases for catching-type precipitation gauges. The gauge geometry induces a deformation of the surrounding airflow pattern, which is generally characterized by relevant updraft zones in front of the collector and above it. This effect deviates the trajectories of the lighter hydrometeors away from the collector, thus is responsible for a significant reduction of the collection performance. Previous approaches to this problem, using Computational Fluid Dynamic simulations and wind tunnel tests, mostly assumed steady and uniform free-stream conditions. Wind is rather turbulent in nature, though. The role of the natural free-stream turbulence on the collection performance is investigated in this work for the case study of a calix shape precipitation gauge and wind velocity between 10 and 18 ms−1. The Unsteady Reynolds Averaged Navier-Stokes model was adopted. Turbulent conditions were simulated by imposing constant free-stream velocity and introducing a fixed solid fence upstream of the gauge in order to generate the desired turbulence intensity. Wind tunnel measurements allowed validating numerical results by comparing measured and simulated velocity profiles in representative portions of the investigated domain. Results revealed that in the case of turbulent free-stream conditions both the normalized magnitude of the flow velocity and the updraft above the collector are reduced by about 20 % and 12 % respectively. The dissipative effect of the turbulent fluctuations in the free stream has a damping role on the acceleration of the flow and on the updraft. This would result in a reduced undercatch with respect to literature simulations employing the traditional uniform free-stream conditions.
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The Role of Free-Stream Turbulence in Attenuating the Wind Updraft above the
Collector of Precipitation Gauges
ARIANNA CAUTERUCCIO
Department of Civil, Chemical and Environmental Engineering, University of Genova, and WMO/CIMO
Lead Centre ‘‘B. Castelli’’ on Precipitation Intensity, Genoa, Italy
MATTEO COLLI
Artys SRL, Genoa, Italy
ANDREA FREDA
Department of Civil, Chemical and Environmental Engineering, University of Genova, Genoa, Italy
MATTIA STAGNARO AND LUCA G. LANZA
Department of Civil, Chemical and Environmental Engineering, University of Genova, and WMO/CIMO
Lead Centre ‘‘B. Castelli’’ on Precipitation Intensity, Genoa, Italy
(Manuscript received 5 June 2019, in final form 1 October 2019)
ABSTRACT
In operational conditions, wind is the main environmental source of measurement biases for catching-type
precipitation gauges. The gauge geometry induces a deformation of the surrounding airflow pattern, which is
generally characterized by relevant updraft zones in front of the collector and above it. This effect deviates the
trajectories of the lighter hydrometeors away from the collector and thus is responsible for a significant reduction of
the collection performance. Previous approaches to this problem, using computational fluid dynamics simulations
and wind-tunnel tests, mostly assumed steady and uniform free-stream conditions. Wind is turbulent in nature,
though. The role of natural free-stream turbulence on collection performance is investigated in this work for the
case study of a calyx-shaped precipitation gauge and wind velocity between 10 and 18 m s
21
. The unsteady
Reynolds-averaged Navier–Stokes model was adopted. Turbulent conditions were simulated by imposing constant
free-stream velocity and introducing a fixed solid fence upstream of the gauge to generate the desired turbulence
intensity. Wind-tunnel measurements allowed validating numerical results by comparing measured and simulated
velocity profiles in representative portions of the investigated domain. Results revealed that in the case of turbulent
free-stream conditions both the normalized magnitude of the flow velocity and the updraft above the collector are
reduced by approximately 20% and 12%, respectively. The dissipative effect of the turbulent fluctuations in the
free stream has a damping role on the acceleration of the flow and on the updraft. This would result in a reduced
undercatch with respect to literature simulations that employed the traditional uniform free-stream conditions.
1. Introduction
National weather services commonly adopt catching
type gauges for operational in situ precipitation measure-
ments. These instruments are equipped with a collector
(funnel) to convey precipitation into a container, where
the collected amount of water is measured by means of
different technologies. Modern recording instruments
mainly use tipping-bucket, floating or weighing tech-
niques (see, e.g., WMO 2014, chapter 6).
Both instrumental and environmental factors act as
sources of systematic errors in precipitation measure-
ments, and can be adjusted by means of correction
Denotes content that is immediately available upon publica-
tion as open access.
Corresponding author: Luca G. Lanza, luca.lanza@unige.it
This article is licensed under a Creative Commons
Attribution 4.0 license (http://creativecommons.org/
licenses/by/4.0/).
JANUARY 2020 C A U T E R U C C I O E T A L . 103
DOI: 10.1175/JTECH-D-19-0089.1
Ó2020 American Meteorological Society
curves. Instrumental factors such as the systematic me-
chanical error of tipping-bucket rain gauges and the
dynamic response of weighing gauges can be corrected
after dynamic calibration in the laboratory (Lanza and
Stagi 2008;Colli et al. 2013). Among the environmental
factors, wind is the main influencing variable for pre-
cipitation measurements. Any precipitation gauge, in-
deed, presents an obstruction to the prevailing wind
and the incoming airflow is deformed when wind over-
takes the precipitation gauge. Wind generally accelerates
above the collector of the instrument, while vertical up-
ward velocity components arise upwind of the collector
(Warnick 1953). This aerodynamic effect induced by
the gauge body deflects the hydrometeors (liquid/solid
particles) away from the collector (Folland 1988;Ne
spor
and Sevruk 1999). The main factors of influence are the
gauge geometry, the wind speed and the characteristics
of precipitation, including the particle size distribution
and precipitation intensity (Thériault et al. 2012;Colli
et al. 2015).
Wind-induced errors were studied in the literature
using different approaches—field measurement cam-
paigns, numerical simulation, and wind-tunnel (WT)
experiments—with the aim of formulating correction
curves to calculate the actual precipitation falling to the
ground. Nevertheless, the implementation of correction
curves in operational conditions is still rare. Sevruk
(1982) reported that the typical magnitude of the
wind-related losses (undercatch) for the precipitation
amount is 2%–10% in case of liquid precipitation and
10%–50% in case of solid precipitation. Pollock et al.
(2018) reported an observed undercatch of about 10%
to 23% for liquid precipitation at a lowland and upland
sites, respectively. Further studies focusing on solid
precipitation (Rasmussen et al. 2012;Colli et al. 2015)
showed collection losses up to 70%–80%.
In field measurement campaigns, precipitation
collected by a gauge installed in operational condi-
tions is compared with a suitable reference. The so-
called pit gauge provides the reference measurement
for liquid precipitation (Lanza and Vuerich 2009),
while the double-fence intercomparison reference is
usually adopted for solid precipitation (Nitu et al.
2018). The numerical approach, based on computa-
tional fluid dynamics (CFD) simulations, reduces the
time and resources needed to investigate different config-
urations by varying the wind speed, type of precipitation
and gauge geometry. The validation of numerical models
can be obtained by comparison with WT measurements,
obtained in controlled laboratory conditions. After valida-
tion, numerical simulation of precipitation particles trajec-
tories leads to estimate the collection efficiency and to
quantify the wind-induced errors.
Ne
spor and Sevruk (1999), conducted numerical
simulations on three cylindrical gauges of different size
while varying the shape of the collector rim and the wind
speed. Assuming uniform free-stream conditions, they
obtained the airflow velocity field (magnitude and di-
rectional components), using a time average approach,
and then computed the liquid particles trajectories. The
flow velocity and turbulent kinetic energy fields obtained
from the simulations were validated by comparison with
WT measurements. The raindrop trajectories were com-
puted using a simplified model, which neglects the in-
teraction between particles and the effect of the particles
on the air (one-way coupled model). This simulation
scheme was adopted also by Thériault et al. (2012) and
Colli et al. (2015,2016a,b) for solid precipitation, by in-
creasing the details of the computational mesh to better
capture the airflow features. Shielded and unshielded
gauge configurations were studied in both time-averaged
(Reynolds-averaged Navier–Stokes) and time-dependent
[large-eddy simulation (LES)] approaches.
A very large dataset of field measurements of solid
precipitation was provided by the Solid Precipitation In-
tercomparison Experiment (SPICE) (Nitu et al. 2018)
organized by the World Meteorological Organization
(WMO). This project involved about 20 field sites for
three years (2011–13) in an experimental campaign to
assess the impact of automation on the measurement of
snowfall, snow depth and solid precipitation in cold cli-
mates. The wind effect was also considered and correction
curves were formulated (Wolff et al. 2015;Kochendorfer
et al. 2017;Buisán et al. 2017). The analysis of real-world
data allows to account for the intrinsic turbulence of the
airflow, which is generally neglected when using a CFD
approach and in WT tests. Natural wind fields are indeed
characterized by turbulent fluctuations, especially near to
the ground where precipitation gauges are located.
The numerical and WT studies cited above neglected
the free-stream turbulence and assumed that turbulence
is only generated by the interaction of the airflow with
the gauge. In all previous works presented in the liter-
ature, CFD simulations assumed a steady and uniform
incoming flow at fixed horizontal wind speeds, whereas
WT experiments were conducted in low free-stream
turbulence conditions. The present work aims to in-
vestigate the role of free-stream turbulence on the air-
flow above the collector of a precipitation gauge. This
was quantified based on the comparison between the
aerodynamic response of the gauge under uniform and
turbulent free-stream conditions, assessed by means of
both CFD simulations and WT tests.
Although traditional catching type precipitation gauges
usually have cylindrical or ‘‘chimney’’ shapes, with the in-
creasing awareness of the wind effect on collection
104 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 37
performance new precipitation gauges characterized by
aerodynamic shapes have been recently developed. The
airflow patterns above gauges of various geometries and
the benefits of the aerodynamic shape, were shown
using results of CFD simulations (Colli et al. 2018)
and supported by field investigations (Pollock et al.
2018). Specifically, Colli et al. (2018) showed that the
turbulent kinetic energy induced by the flow-gauge
interaction above the collector for the calyx-shape
gauge is about one-third of that generated by gauges
with cylindrical or ‘‘chimney’’ shape. For this reason,
in order to better single out the role of free-stream
turbulence on the airflow features above the gauge
collector we focused, in the present work, on the
calyx-shape precipitation gauge.
2. Method
The airflow pattern above the collector of an aero-
dynamic precipitation gauge was analyzed in two
airflow configurations by means of CFD simulations
and WT experiments. The test gauge was the Kalyx-RG
tipping-bucket aerodynamic gauge, manufactured by
Environmental Measurements Limited (EML). In a first
configuration, the incoming flow was imposed steady
and uniform, with no significant turbulence intensity of
the incoming airflow upstream of the gauge. In a second
configuration, the free-stream turbulence was simulated
by using a fixed solid fence with a regular square mesh
located upstream of the gauge. In this second con-
figuration the obtained turbulence intensity upstream
of the gauge was about 0.10. Two wind regimes were
investigated per each configuration, with incoming
horizontal mean wind velocity equal to 18 and 10 ms
21
,
respectively. CFD simulations were performed us-
ing the open-source OpenFOAM numerical solver,
adopting the unsteady Reynolds-averaged Navier–
Stokes (URANS) model and the shear stress tensor
(SST) kvclosure model. Simulation results were
processed to compute the velocity profiles (magni-
tude and components) in representative portions
of the domain. Validation was provided by reproducing
the two airflow configurations in WT tests and mea-
suring the horizontal and vertical velocity profiles
(magnitude and components) at fixed positions using
velocity probes.
a. The numerical model
As compared with the most common tipping-bucket
rain gauges (having a cylindrical shape) the Kalyx-RG
(Fig. 1a) is an aerodynamic inverted conical shaped
gauge with a smaller size. The instrument has an orifice
diameter Dequal to 0.13 m and height equal to 0.192m.
The model of the gauge geometry was prepared in the
standard triangulation language format (Fig. 1b) using a
3D CAD software.
By means of spatial discretization, the computa-
tional mesh and domain were defined. The three
Cartesian coordinates were set with the xaxis orien-
tated along the streamwise direction, the yaxis along
the crosswise direction and the zaxis along the ver-
tical direction. For the uniform airflow configuration,
the spatial computational domain consists of a 5 3
232m
3
box, and is decentralized downward to op-
timize the computational cost and ensure full devel-
opment of the airflow wake downstream of the gauge.
FIG. 1. (a) The EML Kalyx aerodynamic rain gauge, and (b) the numerical model of the gauge.
JANUARY 2020 C A U T E R U C C I O E T A L . 105
For the turbulent case, the computational domain
was stretched upstream by 1 m in order to introduce a
turbulence-generating fence composed by a regular
square mesh of thickness equal to 0.02m and spac-
ing equal to 0.15 m. The three-dimensional spatial
domain was discretized using an unstructured hybrid
hexahedral/prismatic finite volume mesh. The quality of
the mesh was checked by using the geometry parame-
ters of orthogonality, skewness and aspect ratio. The
computational domain was subdivided in refinement
boxes stretched along the downwind direction, with
increasing refinement of the mesh when approaching
the gauge body (Fig. 2a) and the turbulence-generating
fence (Fig. 2b). The number of cells in the configuration
with the turbulence-generating fence was more than
2 times the steady-uniform case, that is, 2.7 310
6
and
1.2 310
6
cells, respectively. The refinement boxes al-
lowed to better solve the equation of motion in the re-
gion affected by large gradients of velocity and pressure.
For this reason, three refinement layers were intro-
duced close to the gauge surface and the minimum
dimension of the nearest cells to the geometry is about
1/3 of the collector rim thickness. To check that the
grid size near the gauge surface was consistent with
the use of a wall function (see, e.g., Menter and Esch
2001), so that the computational burden is reduced,
the wall y
1
was calculated. The wall y
1
is a nondimensional
distance defined as
y15(utyn)/ya, (1)
where y
n
is the height from the wall to the midpoint of
the wall-adjacent cells, y
a
is the kinematic viscosity of
the air, and u
t
is the friction velocity.
In the simulations, the average value of y
1
is about
15, the median is 12, and most values (90%) are below
30, with the larger values occurring in the downwind
part of the gauge; therefore, a scalable wall function
was adopted. This allowed to model the flow velocity
with linear and logarithmic profiles below and above a
threshold value defined at y
1
511.067 by Menter and
Esch (2001).
The finite-volume CFD simulations were performed
based on the solution of the URANS equations. The
URANS model provides the Eulerian description of the
air velocity components U
i
over the three-dimensional
spatial domain in time-average terms. In the URANS
model, the Reynolds stress tensor is used to represent
the transfer of momentum due to the turbulent fluctu-
ations u0
i, where the subscript indicates the spatial di-
rection while the apex denotes the turbulent component
of the flow velocity. The general expression for the ve-
locity field is assumed as
Ui5Ui1u0
i, (2)
where the bar indicates the mean operator.
The solution requires the introduction of a closure
model based on the (specific) turbulence kinetic en-
ergy k, the dissipation per unit mass «, and the tur-
bulent specific dissipation rate vreported below and
defined as in Wilcox (2006). In this approach, kis
expressed by means of turbulent fluctuation u0
iof the
flow as
k50:5(u0
iu0
i). (3)
Also, «is the rate at which kis converted into thermal
internal energy per unit mass and is given, for in-
compressible flow, by
«5yu0
i
xk
u0
j
xk
. (4)
where yis the kinematic viscosity of the air. Then vis
related to kby means of the kinematic eddy viscosity y
t
:
v;k/yt. (5)
The SST kvclosure model was adopted here so as to
switch to a k«behavior in the free stream far from the
FIG. 2. (a) Mesh refinements around the precipitation gauge, and (b) the CFD mesh and domain for the configuration with a solid
turbulence-generating fence positioned upstream of the gauge.
106 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 37
object and to the kvmodel near the walls. Constantinescu
et al. (2007), while investigating the shielding problem
between two contiguous precipitation gauges, tested
different numerical methods and concluded that the
SST kvis more consistent with the LES results on
the upstream gauge, in conditions that are similar to
the present work.
The Navier–Stokes equation were solved numerically
by employing the ‘‘pimpleFoam’’ solver of the open-
source ‘‘OpenFOAM’’ software. The closure turbulence
model was set using the ‘‘kOmegaSST’’ function. The
fluid air was modeled as a Newtonian incompressible
fluid with kinematic viscosity n
a
51.5 310
25
m
2
s
21
and
density r
a
51.25 kg m
23
at a reference environmental
temperature T
a
5208C.
At the inlet of the computational domain (yzplane)
the undisturbed wind speed U
ref
was imposed parallel
to the xaxis and was maintained uniform and constant
in time while a null gradient condition was set for
pressure. Atmospheric pressure and null gradient
conditions for the velocity were imposed at the outlet
(yzplane opposite to the inlet). The lateral surfaces
of the domain were set as symmetry planes. The
ground and the gauge surface were assumed imper-
meable with a no-slip condition. In all computational
cells of the spatial domain, initial conditions were
imposed equal to U
ref
for the velocity and equal to
zero for the relative pressure.
b. Wind-tunnel tests
The experimental tests were performed in the WT of
the Department of Civil, Chemical and Environmental
Engineering (DICCA) of the Polytechnic School of the
University of Genoa. It is a closed-loop subsonic circuit
for aerodynamic and civil engineering experiments. The
WT has a working section with a total length of 8.8 m
and two different test sections, with a cross area of 1.7 3
1.35 m
2
(width 3height). The tests have been carried
out in the downstream section, which is placed at the end
of the working section in order to install the turbulence-
generating fence in the upstream section (Fig. 3a).
In such a test section, the naturally developed wall
boundary layer is 0.13 m. To carry out tests in uniform
flow, the rain gauge was placed on an end plate (Fig. 3a).
Two conditions for the incoming flow have been con-
sidered: 1) smooth flow characterized by a turbulence
intensity I
turb
0.5% and 2) turbulent flow, produced
through a grid realized by square bars (Fig. 3a), char-
acterized by I
turb
10%, calculated as
Iturb 5[(2/3)k]1/2/Umag . (6)
A static Pitot tube placed at the top of the test section
was used to measure the reference wind speed U
ref
.
Local measurements of the wind speed were acquired
using a fast-response multihole probe (a four-hole
‘‘Cobra’’ probe; Fig. 3b). The Cobra probe was
mounted on a traversing system with three degrees
of freedom, which allowed measurement at any co-
ordinates of interest. Measurements were sampled
at 2 kHz.
3. Results and validation
The main area of interest for our study is just above
the gauge collector, where the modified airflow pat-
terns may influence particle trajectories and, there-
fore, the precipitation collection. In this region, results
were visualized in terms of normalized maps and
profiles on the vertical along-wind symmetry plane of
the gauge collector (y50). The magnitude U
mag
and
the vertical component U
z
of the airflow velocity were
reported and compared for the two free-stream tur-
bulence configurations. Both were normalized using
the undisturbed wind speed, U
ref
, while the spatial
coordinates were normalized with the gauge collector
FIG. 3. (a) The EML Kalyx rain gauge and the turbulence-generating fence in the DICCA WT, the flow direction being from right to left,
and (b) a detail of the Cobra pressure probe used to measure the local flow velocity near the gauge collector.
JANUARY 2020 C A U T E R U C C I O E T A L . 107
diameter D(the origin of the axes is located at the
center of the collector). The turbulence intensity
profile and contour map along the streamwise direction
were also reported. Validation of the numerical setup
is provided below by comparing WT local measure-
ments and numerical results, with the simulated pro-
files depicted with lines and markers denoting WT
measurements.
To ensure the comparability of results for the uniform
and turbulent free-stream conditions, that were obtained
at different undisturbed wind speed (U
ref
518 m s
21
and
U
ref
510 m s
21
), the scalability of the results was pre-
liminarily checked by performing CFD simulations under
uniform free-stream conditions for both velocities.
Figure 4 shows that the resulting normalized vertical
velocity profiles along the streamwise direction are
totally overlapped above the gauge collector. The gauge
collector is painted in gray and black dashed lines in-
dicate the edge projections.
Wind-tunnel measurements of the longitudinal pro-
files (at y50 and z50.038D) of the normalized vertical
component of flow velocity and the turbulence intensity
are depicted in Fig. 5 for the uniform (black points)
and turbulent (gray triangles) free-stream conditions.
In both cases, the turbulence intensity increases above
the collector due to the obstruction caused by the
gauge body. As already observed by Warnick (1953),
a significant updraft is expected to arise in front and
above of the gauge collector, which is evident in
Fig. 5.
As a result of the aerodynamic shape of the Kalyx
gauge, the recirculating zone is confined above the
gauge collector and the airflow pattern is charac-
terized by upward components in the upwind part,
upstream the center of the collector, and downward
components in the downwind part. This had been
shown by Colli et al. (2018) when comparing the nu-
merically simulated aerodynamic response of other
inverted conical shapes similar to the Kalyx gauge.
Contrary to the turbulence intensity, the normalized
vertical velocity components are less accentuated for
the turbulent free-stream configuration than in a uni-
form free stream, with relative percentage differences
of about 18% and 46% on the upwind and downwind
edges, respectively (see Fig. 5). This behavior can also
be observed in Fig. 6, where the normalized vertical
FIG. 4. Simulated profiles of the normalized vertical velocity U
z
/U
ref
along the non-
dimensional streamwise direction x/D,withDbeing the collector’s diameter, at y50and
elevation z50.1515Dabove the gauge collector (in gray with vertical dashed-line pro-
jections in black), for uniform free-stream velocity equal to U
ref
510 m s
21
(red) and
U
ref
518 m s
21
(black).
108 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 37
component of the flow velocity, along the vertical
direction close to the upwind edge of the collector
(x520.568D), decreases faster in the turbulent free-
stream condition, reaching, for example, 0.015 at a nor-
malized elevation of 0.68z/D rather than at 1.18z/D
like in the uniform free-stream condition.
The CFD simulations allowed computing the airflow
variables in the whole spatial domain surrounding the
gauge collector, differently to the WT measurements
that were taken locally in representative positions of
the domain. Figure 7 shows the simulated airflow fields
in terms of normalized magnitude of the flow velocity
and normalized vertical velocity component, for the
uniform and turbulent free-stream conditions. For
the normalized magnitude of flow velocity, the white
band indicates the region where the flow velocity is
equal to the undisturbed wind speed (U
mag
5U
ref
);
this boundary separates the region characterized by
accelerated airflow regime (U
mag
.U
ref
;redcolor)
from the recirculating zone (U
mag
,U
ref
; blue color).
For the normalized vertical component of the flow
velocity, the white band indicates the region with a
null vertical velocity component, while the red and
blue colors characterize the updraft and downdraft
regions, respectively. As already observed in the WT
measurements, also in the numerical simulation results
the normalized magnitude of the flow velocity for the
uniform free-stream configuration is about 20% larger
than in turbulent conditions (see Fig. 7).
With the aim to validate numerical simulations,
CFD results were compared with WT measurements.
In Fig. 8, the normalized vertical velocity component
at the upwind edge of the collector along the vertical
direction at y50 (left) and the normalized magnitude
of flow velocity along the streamwise direction at y50
and elevation z50.075D(right) are represented for
the uniform flow. Figure 9 depicts the same situations
in turbulent free-stream conditions. A good agreement
between WT measurements and numerical results was
observed for the uniform free-stream condition along
the vertical profile with differences on the order of
0.010U
z
/U
ref
at a few measurement elevations (see
Fig. 8). Along the longitudinal profiles the quantitative
velocity values differ in some positions but the airflow
behavior is mostly kept. Similarly, for the velocity pro-
files illustrated in Fig. 9 under turbulent free-stream
conditions, a good match between numerical simulation
and experiments was observed along the vertical profile,
with differences on the order of 0.030U
z
/U
ref
at a few
measurement elevations. In Fig. 10, a comparison between
the measured and simulated turbulence intensity is re-
ported. The values of the turbulence intensity measured in
FIG. 5. Wind-tunnel measurements of the normalized vertical velocity U
z
/U
ref
and turbu-
lence intensity I
turb
along the nondimensional streamwise direction x/D, with Dbeing the
collector’s diameter, at y50 and elevation z50.038Dabove the gauge collector (in gray with
vertical dashed-line projections in black), for the uniform (circles; U
ref
518 m s
21
) and tur-
bulent (triangles; U
ref
510 m s
21
) free-stream experiments.
JANUARY 2020 C A U T E R U C C I O E T A L . 109
the WT (white circles) are in consistent agreement with the
contour line levels of the simulated numerical field. Few
measurements, in the downwind part of the collector, dif-
fer from the numerical field up to a maximum of 0.1; the se
differences can be justified because the Cobra probe
is unsuited to measure reverse flow components and
because in this region elevated gradients of turbulence
intensity occurred, as can be observed close to the edge
of the gauge.
4. Discussion and conclusions
The problem of wind-induced undercatch of pre-
cipitation gauges was first addressed numerically
by Ne
spor and Sevruk (1999) for liquid precipita-
tion, and by Thériault et al. (2012) and Colli et al.
(2015,2016a,b) for solid precipitation. However,
results of the numerical models adopted in these and
other works are affected by the not negligible, sim-
plifying hypothesis of uniform free-stream airflow
conditions.
The comparison of simulated and measured airflow
fields in the uniform and turbulent free-stream config-
urations for wind velocity between 10 and 18 m s
21
,as
proposed in this work, provided insights about the role
of turbulence in attenuating the aerodynamic response
of precipitation gauges. Wind-tunnel measurements
(Fig. 5) showed that the normalized updraft in the up-
wind part, upstream the center of the collector, and
the downdraft in the downwind part are less accentu-
ated in the turbulent free-stream configuration than in
FIG. 7. Color maps of the simulated (a),(b) normalized magnitude of flow velocity U
mag
/U
ref
and (c),(d) normalized vertical
velocity U
z
/U
ref
in the vertical section along thestreamwisedirectionaty50, for the (left) uniform and (right) turbulent free-
stream conditions.
FIG. 6. Wind-tunnel measurements of the normalized vertical
velocity U
z
/U
ref
along the nondimensional vertical direction
z/D,withDbeing the collector’s diameter, at y50, upstream of
the gauge collector at x520.568D, for the uniform (circles;
U
ref
518 m s
21
) and turbulent (triangles; U
ref
510 m s
21
)free-
stream experiments.
110 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 37
uniform free-stream conditions. This is ascribable to
the energy dissipation induced by turbulent fluctua-
tions. The dissipative effect of the free-stream turbu-
lence also has a damping role on the acceleration of
the flow above the collector as demonstrated by CFD
results (Figs. 7a,b).
This conclusion is consistent with the literature about
the free-stream turbulence effect on the interaction of a
FIG.9.AsinFig. 8, but for the turbulent free-stream experiment (U
ref
510 m s
21
).
FIG. 8. (left) Wind-tunnel measurements and simulated profile of the normalized vertical velocity U
z
/U
ref
at the upwind edge of the
collector along the nondimensional vertical direction z/D, with Dbeing the collector’s diameter, at y50, and (right) normalized velocity
magnitude U
mag
/U
ref
along the nondimensional streamwise direction x/Dat y50 and elevation z50.075Dabove the gauge collector (in
gray with vertical dashed-line projections in black), for the uniform free-stream experiment (U
ref
518 m s
21
).
JANUARY 2020 C A U T E R U C C I O E T A L . 111
‘‘bluff body’’ with the incoming airflow, as reported by
various authors including, for example, Kiya and Sasaki
(1983). While studying experimentally the free-stream
turbulence effect on a separation bubble formed along
a side of a blunt plate with right-angled corners, they
concluded that the length of the separation bubble
reduces significantly with increasing the turbulence
intensity. Also, Counihan et al. (1974) proposed an
analytical theory for the mean velocity behind a
two-dimensional obstacle and derived that the wake
strength decreases as the surface roughness in front
of the obstacle (therefore, the free-stream turbulence)
increases.
Our conclusions are consistent with the work of Colli
et al. (2015) about the collection efficiency of precipi-
tation gauges, in which a general overestimation of the
wind-induced error when performing simulations under
steady-uniform free-stream conditions was evident from
the comparison with field observations.
This work is further substantiated by the performed
WT validation of the gauge exposure problem for both
turbulent and uniform free-stream configurations, that
was yet lacking in the literature. In Figs. 8 and 9the
simulated velocity profiles closely follow the experi-
mental measurements; some differences arise along the
streamwise direction in the region where the magnitude
of flow velocity is low and beyond the gauge collector, in
the turbulent wake. These differences are justified
since the velocity values in such cases approach the
minimum threshold velocity that the Cobra probe is
able to measure (about 2 ms
21
to get reliable values).
Also, the Cobra probe is unsuited to measure reverse
flow components. However, these inconsistencies oc-
cur in a region that is located beyond the key area of
interest to assess the collection performance of the
gauge and, therefore, have only a minor impact on our
conclusions.
From the CFD results and the validation provided by
WT observations, we conclude that accounting for the
free-stream airflow turbulence in the simulation is re-
quired to avoid underestimation of the collection effi-
ciency of precipitation gauges. This paper demonstrates
that numerical derivation of correction curves for use in
precipitation measurements as proposed hitherto in the
literature is affected by a systematic overestimation of
the wind-induced error due to the simplifying assump-
tion of uniform free-stream conditions. A turbulent free
stream is indeed the natural atmospheric condition of
the wind impacting on operational precipitation gauges
in the field. Since solid precipitation particles are more
sensitive to the wind, neglecting the role of free-stream
turbulence in the derivation of correction curves for
solid precipitation measurements may lead to a large
overestimation of the wind-induced errors.
Acknowledgments. This work was developed in the
framework of the Italian National Project PRIN 2015-
4WX5NA ‘‘Reconciling precipitation with runoff: The
role of understated measurement biases in the model-
ling of hydrological processes.’’
FIG. 10. Vertical section of the simulated I
turb
(colors, with contour-line levels in boldface
type) along the streamwise direction at y50, and WT local measurements (white circles with
lightface numbers) for the turbulent free-stream conditions (U
ref
510 m s
21
).
112 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 37
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JANUARY 2020 C A U T E R U C C I O E T A L . 113
... Further to the local generation of turbulence due to the flow-gauge interaction, in this work we investigated the natural free-stream turbulence intensity and its influence on precipitation measurement biases. To the best of the author's knowledge, only one attempt to assess the impact of free-stream turbulence on the wind-induced bias has been performed [15]. ...
... Results revealed that both the normalized vertical and horizontal components of the flow velocity above the gauge collector were less accentuated in the turbulent free-stream configuration than in uniform free-stream conditions, due to the energy dissipation induced by turbulent fluctuations. However, in a CFD approach based on the unsteady Reynolds-averaged Navier-Stokes (URANS) equations, the analysis was limited to the effect of turbulence on the airflow deformation around and above the gauge, while the impact on the hydrometeors trajectories was not quantified [15]. ...
... Along the transversal direction (y/D), the flow field started to become uniform many diameters away from the collector. As shown in Figure 9 In addition to the work presented in ref 15, where the free-stream turbulence effect was investigated using an unsteady Reynolds-averaged Navier-Stokes approach (URANS), and results were provided in terms of attenuation of the airflow updraft and acceleration above the collector of the gauge, in the present work the particle-fluid interaction (using a one-way coupled model) was also addressed, and the two free-stream turbulence conditions were compared in terms of catch ratios and collection efficiency. ...
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Accurate snowfall measurements are critical for a wide variety of research fields, including snowpack monitoring, climate variability, and hydrological applications. It has been recognized that systematic errors in snowfall measurements are often observed as a result of the gauge geometry and the weather conditions. The goal of this study is to understand better the scatter in the snowfall precipitation rate measured by a gauge. To address this issue, field observations and numerical simulations were carried out. First, a theoretical study using finite-element modeling was used to simulate the flow around the gauge. The snowflake trajectories were investigated using a Lagrangian model, and the derived flow field was used to compute a theoretical collection efficiency for different types of snowflakes. Second, field observations were undertaken to determine how different types, shapes, and sizes of snowflakes are collected inside a Geonor, Inc., precipitation gauge. The results show that the collection efficiency is influenced by the type of snowflakes as well as by their size distribution. Different types of snowflakes, which fall at different terminal velocities, interact differently with the airflow around the gauge. Fast-falling snowflakes are more efficiently collected by the gauge than slow-falling ones. The correction factor used to correct the data for the wind speed is improved by adding a parameter for each type of snowflake. The results show that accurate measure of snow depends on the wind speed as well as the type of snowflake observed during a snowstorm.
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By making simple assumptions, an analytical theory is deduced for the mean velocity behind a two-dimensional obstacle (of height h ) placed on a rigid plane over which flows a turbulent boundary layer (of thickness δ). It is assumed that h [Gt ] δ, and that the wake can be divided into three regions. The velocity deficit − u is greatest in the two regions in which the change in shear stress is important, a wall region (W) close to the wall and a mixing region (M) spreading from the top of the obstacle. Above these is the external region (E) in which the velocity field is an inviscid perturbation on the incident boundary-layer velocity, which is taken to have a power-law profile U ( y ) = U ∞( y − y 1 ) n / δ n , where n [Gt ] 1. In (M), assuming that an eddy viscosity (= KhU ( h )) can be defined for the perturbed flow in terms of the incident boundary-layer flow and that the velocity is self-preserving, it is found that u ( x,y ) has the form $\frac{u}{U(h)} = \frac{ C }{Kh^2U^2(h)} \frac{f(n)}{x/h},\;\;\;\; {\rm where}\;\;\;\; \eta = (y/h)/[Kx/h]^{1/(n+2)}$ , and the constant which defines the strength of the wake is $C = \int^\infty_0 y^U(y)(u-u_E)dy$ , where u = u E ( x, y ) as y → 0 in region (E). In region (W), u ( y ) is proportional to In y. By considering a large control surface enclosing the obstacle it is shown that the constant of the wake flow is not simply related to the drag of the obstacle, but is equal to the sum of the couple on the obstacle and an integral of the pressure field on the surface near the body. New wind-tunnel measurements of mean and turbulent velocities and Reynolds stresses in the wake behind a two-dimensional rectangular block on a roughened surface are presented. The turbulent boundary layer is artificially developed by well-established methods (Counihan 1969) in such a way that δ = 8 h . These measurements are compared with the theory, with other wind-tunnel measurements and also with full-scale measurements of the wind behind windbreaks. It is found that the theory describes the distribution of mean velocity reasonably well, in particular the ( x / h ) ⁻¹ decay law is well confirmed. The theory gives the correct self-preserving form for the distribution of Reynolds stress and the maximum increase of the mean-square turbulent velocity is found to decay downstream approximately as $ (\frac{x}{h})^{- \frac{3}{2}} $ in accordance with the theory. The theory also suggests that the velocity deficit is affected by the roughness of the terrain (as measured by the roughness length y 0 ) in proportion to In ( h / y 0 ), and there seems to be some experimental support for this hypothesis.
Article
Precipitation is one of the most important atmospheric variables for ecosystems, hydrologic systems, climate, and weather forecasting. Despite its importance, accurate measurement remains challenging, and the lack of recent and complete inter-comparisons leads researchers to discount the importance and severity of measurement errors. These errors are exacerbated for the automated measurement of solid precipitation and underestimates of 20-50% are common. While solid precipitation measurements have been the subject of many studies, there have been only a limited number of coordinated assessments on the accuracy, reliability, and repeatability of automatic precipitation measurements. The most recent comprehensive study, the "WMO Solid Precipitation Measurement Inter-comparison" focused on manual techniques of solid precipitation measurement. Precipitation gauge technology has changed considerably in the last 12 years and the focus has shifted to automated techniques. Given the strong need for automated solid precipitation data from both the climate and weather communities, and the widely varying catch efficiencies of the various instruments, inter-comparison studies are needed. The World Meteorological Organization Committee on Meteorological Instruments and Observations (WMO-CIMO) is organizing a Solid Precipitation Inter-comparison Experiment (WMO-SPICE) focused on automatic precipitation gauges and their configurations, in various climate conditions, building on the significant efforts currently underway in many countries. The inter-comparison will aim at understanding and improving our ability to reliably measure solid precipitation using automatic gauges. The study will take place starting in 2012 at sites around the world including the US, Norway, China, Canada, Japan, Switzerland, Russia, Finland and New Zealand. The NOAA /FAA/NCAR precipitation test bed in Marshall, CO. in partnership with Environment Canada will collect data during the winter of 2011/2012 to enable the WMO-SPICE organizing committee to determine the reference to be used by all other participants in 2012 for the measurement of solid precipitation. The NOAA/FAA/NCAR testbed has been chosen as one of the lead facilities for this study because of the comprehensive set of instrumentation in place for the measurement of solid precipitation. Results from the NCAR Marshall Field research site will be highlighted and an overview and update on the WMO-SPICE will be presented.
Article
A new method of estimating the wind-induced error of rainfall gauge measurements is presented. The method is based on a three-dimensional numerical simulation of the airflow around a precipitation gauge and subsequent computation of particle trajectories. Three-dimensional velocity and turbulence flow fields around a gauge are computed for wind speeds ranging between 1 and 12 m s-1 by employing the k-ε turbulence model. Two-dimensional measurements of the flow using a constant temperature anemometer are carried out in a wind tunnel. The measurement results are used to verify numerical flow simulations. Subsequently, the computed flow fields are used for raindrop trajectory simulations and assessment of wind-induced measurement errors related to a given unique drop diameter and wind speed. These errors are approximated by a gamma-type function and integrated over a gamma drop size distribution. The resulting wind-induced error is presented as a function of the rate of rainfall, wind speed, and drop size distribution parameters. The wind-induced measurement error is evaluated for three operational precipitation gauges. The results show an increase of the error with a decreasing rainfall rate, and increasing wind speed and fraction of smaller drops. The comparison of gauges also reveals differences. The computed wind-induced errors are compared with the errors derived from field rainfall measurements. The compared values show a relatively good agreement.