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Performance of Emerging Technologies for Measuring Solid and LiquidPrecipitation in Cold Climate as Compared to the Traditional Manual Gauges
FAISAL S. BOUDALA
Cloud Physics and Severe Weather Research Section, Environment and Climate Change Canada, Toronto,
Ontario, Canada
GEORGE A. ISAAC
Weather Impacts Consulting Inc., Barrie, Ontario, Canada
PETER FILMAN
Department of National Defence, Government of Canada, Cold Lake, Alberta, Canada
ROBERT CRAWFORD AND DAVID HUDAK
Cloud Physics and Severe Weather Research Section, Environment and Climate Change Canada,
Toronto, Ontario, Canada
MARTHA ANDERSON
Department of National Defence, Government of Canada, Ottawa, Ontario, Canada
(Manuscript received 26 April 2016, in final form 7 September 2016)
ABSTRACT
Precipitation amount, type, and snow depth (Ds) have been analyzed using data collected during the 4WingCold
LakeResearch Project in northeasternAlberta, Canada. The instruments used include theVaisala present weather
detector PWD22 and present weather sensor (FS)11P, the OTT Pluvio2 automatic catchment-type gauge, the
manual standard Canadian Nipher (CN) and Type B rain gauges, and a snow ruler. Both the PWD22 and FS11P
performed well at detecting snow, rain, and drizzle events as compared to the human observer. The sensors
predicted a higher frequency of ice pellet cases than the human observer. Segregation of precipitation phase using
temperature alone appeared unrealistic at near-freezing temperatures. All the sensors agreed well at measuring
liquid precipitation, but the Pluvio2 gauge with a single Alter shield underestimated the snowfall amount by 40%,
mostly due to wind effects. After correcting the CN gauge catch efficiency (CE) due to wind effects, the CE of the
Pluvio2 relative to theCNgaugewas founddependent onwind speed (ws).Using these data, a new transfer function
(TF) for the Pluvio2 as a function ofws has been developed. The newTFwas used to correct the Pluvio2 gauge, and
the corrected data agreed well with the PWD22measurements. Using theDs and corrected CN data, snow density
ratios (ry) were derived, varying from 4.2 to 35 with a mean value of 12.2. The mean value derived in this study is
higher than the 10:1 ratio usually assumed for converting Ds to snow water equivalent in Canada. On average ryincreases with increasing temperature and the 10:1 ratio appears to be more appropriate for warmer temperatures.
1. Introduction
Precipitation plays a critical role on our planet by
modulating the hydrological cycle and by influencing daily
human activities, including air and ground transportation.
Validation of climate and numerical weather prediction
models, and radar and satellite remote sensing algorithms,
require accurate precipitationmeasurements. Precipitation
amount is normally measured using a weighing gauge,
which is an open container on the ground that collects
precipitating hydrometeors, including raindrops, snow and
Publisher’s Note:This article was revised on 3March 2017 in order
to correct the affiliations of the last three authors.
Corresponding author e-mail: Dr. Faisal Boudala, faisal.boudala@
canada.ca
JANUARY 2017 BOUDALA ET AL . 167
DOI: 10.1175/JTECH-D-16-0088.1
� 2017 American Meteorological Society
hail particles, etc. However, accurately measuring the
precipitation is usuallymore complex, particularly for snow
because of many factors, including losses from wind, wet-
ting, and evaporation (Sevruk and Klemm 1989; Goodison
et al. 1998; Rasmussen et al. 2012; Yang et al. 2005) and
potential enhancement due to blowing snow (Yang et al.
1999). Recent studies also indicate that snow gauge catch
efficiency depends on snow type and density (Thériaultet al. 2015, 2012; Colli et al. 2015, 2016a,b). This is partic-
ularly challenging in the cold northern latitudes, where the
snowfall intensities are relatively low. The manual gauges
that are deployed in the Canadian precipitation networks
include the Type B and Canadian Nipher (Mekis and
Vincent 2011; Metcalfe and Goodison 1993). These
gauges are normally referred to as standard rain gauges.
Automatic gauges, such as Geonor and Pluvio, are cur-
rently being considered for the networks.
One way to reduce the wind-induced loss is by using
some kind of wind shield, by placing a given gauge inside
some bushes, or using specially designed shields, including
the double-fenced structure with a Tretyakov manual
gauge suggested by the World Meteorological Organiza-
tion (WMO), which is normally referred to as Double
Fence IntercomparisonReference (DFIR) (Goodison et al.
1998). There is no absolute reference standard for snow
measurement. The DFIR is usually considered the sec-
ondary standard with the bush-surrounded gauge being the
primary standard (Goodison et al. 1998;Yang 2014). Part of
the reason why the bush gauge is considered to be the
primary standard is because it showed higher catch effi-
ciency as compared to the gauge placed inside the DFIR
(Goodison et al. 1998).Amore recent studybyYang (2014)
showed that the DFIR underestimated solid precipitation
by 5% as compared to the bush gauge. During the WMO
Solid PrecipitationMeasurement Intercomparison (SPMI),
the Canadian Nipher gauge outperformed many other
partially shielded or unshielded gauges when compared
against a Russian Tretyakov manual gauge as the DFIR
(Goodison et al. 1998). One of the outcomes of the WMO
SPMI was the development of correction factors for un-
shielded and partially shielded gauges that are normally
referred to as transfer functions (Goodison et al. 1998).
These transfer functions were developed based on daily
manual snow measurements, and so there are some un-
certainties with their use at shorter time scales due to the
strong variability normally seen during precipitation and
associated weather conditions. These automatic gauges are
currently being used with various wind shield configura-
tions, including the double-fenced structure suggested by
the WMO, but their accuracy under various atmospheric
conditions are not well known (Rasmussen et al. 2012;
Theriault et al. 2015), particularly for the light solid pre-
cipitation that normally occurs in cold climates. There are
also noncatchment-type optical instruments that employ a
forward light scattering method, and hot plates and dis-
trometers that measure hydrometer size and fall velocity
distributions to estimate the precipitation intensities
(Rasmussen et al. 2011; Boudala et al. 2014a,b; Brandes
et al. 2007). These emerging technologies usually are more
sensitive and measure precipitation intensity at higher
temporal resolution down to minutes. Some of these in-
struments do not suffer from the wind-induced and other
losses mentioned earlier and hence are suitable for mea-
suring particularly light precipitation. However, these in-
struments are relatively new and their response under
various atmospheric conditions is not well known.
This study aims to address some of the issues associated
with both catchment- and noncatchment-type gauges, in-
cluding testing their performance under a cold climate,
providing suitable guidance to improve the accuracy of the
catchment-type gauges, and characterize snow density
under various weather conditions. For this purpose, pre-
cipitation and type, and snow depth data were collected
and analyzed as part of the 4Wing Cold Lake Research
Project (4WCLRP). Various instruments were used, in-
cluding the Vaisala present weather sensors PWD22 and
FS11P, the OTT Pluvio2 gauge, the Canadian Nipher and
Type Bmanual gauges, and a snow ruler to measure snow
depth. The 4WCLRP was initiated by the Cloud Physics
and Severe Weather Research Section of Environment
and Climate Change Canada (ECCC) with the co-
operation of theDepartment ofNationalDefence (DND).
The data used in this study were collected during the pe-
riod between September 2014 andAugust 2015. The paper
is organized as follows: The observation site and in-
strumentation are discussed in section 2, the precipitation
amount and type data analysis and results are given in
section 3, the snow density is discussed in section 4, and the
summary and conclusions are given in section 5.
2. Observation sites and instrumentation
The study area, the Cold Lake Regional Airport
(CYOD), is located in northeastern Alberta, Canada
(at 541m MSL; 54823059.800N, 11081706.200W). The geo-
graphical locations of CYOD (Fig. 1a) and the surround-
ing areas, and inside COYD where the instruments were
located (Figs. 1a,b) are shown. In Fig. 1b, in addition to the
ECCC observation site used in this study, there is a col-
located permanent meteorological observation site used
by the DND. The region is generally characterized by a
humid continental climate with warm summers and cold
winters as indicated in Fig. 2, which shows monthly aver-
aged temperature (Fig. 2a), relative humidity (Fig. 2b), and
the frequency distributions for temperature and humidity
(Figs. 2c,d, respectively) based on hourly observations
168 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
taken during the 2014–15 period. The monthly mean
temperature varies from 2128C in January to 188C in
July. Theminimum temperature in winter reached near
2358C, the warmest temperature is close to 278C in
summer, and the most frequent temperature is close to
08C (Fig. 2c). The monthly averaged humidity varied
from 85% in winter (December) to near 55% in sum-
mer (May). The humidity may reach as low as 15% in
April and May, but within a given month the humidity
may reach 100%, and the most frequent humidity is
90% (Fig. 2d). As indicated in Fig. 1a, there are a
number of local conditions that can potentially affect the
local weather and produce precipitation. To the west and
northwest of the airport, there are four small lakes (Marie,
Ethel, Crane, and Hilda) and to the northeast there are
two large lakes (Cold and Primrose) known to produce
precipitation and other weather phenomena when the
flow is from a northeasterly direction. The west and
southwest sides of the airport are surrounded by the
Beaver River valley with an east–west orientation,
FIG. 1. Google map showing (a) Cold Lake and surrounding areas and (b) two instrument sites. In (b) the site
marked as ECC is where the ECCC instruments were located, and the site marked as DND is where the DND
permanent observing site is located.
JANUARY 2017 BOUDALA ET AL . 169
which is also known to produce various weather con-
ditions at the airport.
The list of the deployed instruments along with the
measured microphysical and meteorological parame-
ters, measurement principles, and associated uncer-
tainties are given in Table 1. The instrument setup at the
ECCC site is given in the top panel of Fig. 3, and the
instruments installed at the DND site are given in
Figs. 3a,b. The DND observation site is located ap-
proximately 948m from the ECCC site. The descrip-
tions and measurement principles of the instruments
installed at the ECCC and DND sites are given below.
The Vaisala PWD22 and FS11P present weather
sensors measure precipitation, precipitation type, and
visibility. The operating principles of these two Vaisala
sensors are similar with some minor differences (Table
1). These probes have two arms facing each other, one
equipped with a near-infrared transmitter and the other
with a receiver. The two arms are arranged in a way that
the infrared light can reach the receiver only if it is
forward scattered at a given angle (458 for PWD22 and
428 for FS11P) by particles between the arms. Signal
processing software analyzes the voltage output from
the receiver, along with the current temperature, to
determine the type and intensity of precipitation. The
reported precipitation types include the WMO synoptic
(SYNOP) and METAR codes, as well as the National
Weather Service (NWS) code. Each instrument also
has a heated capacitive surface that provides a liquid
water equivalent measurement. The FS11P is also
equipped with a background light detection sensor.
The OTT Pluvio2 precipitation gauge used here has a
precipitation-collecting container with a collecting area
of 200 cm2 and a capacity of up to 1500mm. The weight
of the precipitation collected in the container is mea-
sured every 6 s by an electronic weighing cell at a reso-
lution of 0.01mm. The measured precipitation intensity
and amount are reported everyminute. TheOTTPluvio2
uses a special filter algorithm to correct the 6-s pre-
cipitation weights for wind effects. Additionally, the
gauge is fitted with a single Alter shield, as shown in
Fig. 3a, to minimize the wind effect during snow (see
Table 1 for more information). The OTT Pluvio2, ver-
sion 200, is also supplied with an orifice rim heating
system. This reliably keeps the orifice ring rim free of
snow and ice during low-temperature operations.
The Vaisala weather transmitter (WXT) 520 mea-
sures temperature, relative humidity, and wind speed
and direction. The wind sensor has an array of equally
spaced ultrasonic transducers on a horizontal plane.
The wind speed and direction are determined by
measuring the time it takes the ultrasound to travel
from one transducer to the other. The instrument uses
separate sensors for measuring temperature and rela-
tive humidity. The accuracy and resolution of these
measurements are given in Table 1.
The manual gauges used for measuring the total
precipitation and rain amounts are the Type B and
Canadian Nipher gauges shown in Fig. 3b. The Type B
and Nipher gauges are the standard instruments for
rain and snow observations, respectively, in Canada
(Mekis and Vincent 2011; Metcalfe and Goodison
1993). The snow amount in its water equivalent form
is determined using the Canadian Nipher gauge. The
falling snow is collected and melted and then measured
bypouring it into a graduated cylinder. The rainfall amount
FIG. 2. Climatology of Cold Lake based on 2 years of hourly reported data.
170 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
TABLE1.ThedescriptionoftheinstrumentsinstalledatCYOD
andrelatedinform
ation.
InstrumentName
Model
Sensortype
Parameter
measured
Range
Resolution
Accuracy
Data
recorded
Capacitance
RH
0%
to100%
0.47
60.8%
at238C
10s
Vaisalaweather
transm
itter
WXT520
Ultrasonic
transducers
Windspeed
0–60m
s21
0.1m
s21
60.3m
s21(0–35m
s21)
1min
5%
(36–60m
s21)
Ultrasonic
transducers
Wind
direction
0–360
18
638
1min
Capacitiveceramic
THERMOCAP
T2528–608C
0.18C
60.38C
1min
Capacitivethin-film
HUMIC
AP
RH
0%
–100%
0.1%
63%
(0%–90%)
1min
65%
(90%–100%
)
Vaisalapresent
weatherdetector
PW
D22
Forw
ard
scattered(458)
lightdetector(photo
diode),
lightsourcenear-IR
875nm
Visibility
10–20
000m
610%,10–10
000m
1min
615%,10–20km
Precipitation
intensity
and
type
—0.01mm
h21
—1min
Vaisalapresent
weathersensor
FS11P
Forw
ard
scattered(428)
lightdetector(photo
diode)
Lightsourcenear-IR
875nm
Visibility
5–75m
610%,5–10m
1min
620%,10–75km
Precipitation
intensity
andtype
—0.01mm
h21
—1min
OTTPluvio
gauge
Pluvio2,version200;
1500-mm
capacity
Stainless
steelload
cell-w
eighingsystem
Precipitation
amount
0.20–500mm
0.01mm
60.1mm
1min
JANUARY 2017 BOUDALA ET AL . 171
ismeasured using theTypeB rain gauge. Inside theTypeB
gauge, there is a graduated cylinder that holds up to 25mm
of rain. Rainfall of more than 25mm can be made by
measuring the overflow of the cylinder into the surround-
ing container. The manually measured data were collected
every 6 h and segregated as solid, mixed, and liquid
phases. More details about how the precipitation phase
was segregated are given in section 3. As well the
depths of snow accumulated over aWeaver snow board
were measured by taking the average of 10 snow ruler
measurements in an undisturbed area around the me-
teorological compound.
3. Data analysis and results
a. Frequency distribution of precipitation type basedon 1-min data
Figure 4 shows the frequency distribution of pre-
cipitation type (PT) reported by the PWD22 and FS11P
present weather sensors based on 1-min data between
September 2014 and August 2015. The weather symbols
shown in Fig. 4 and Table 2 are C, S, SP, IP, SG, IC, R,
ZR, ZL, P, RLS, L, and RL represent clear, snow, snow
pellets, ice pellets, snow grains, ice crystals, rain, freez-
ing rain, freezing drizzle, unknown, mixed, drizzle, and
FIG. 3. (top) The ECCC instrument setup at CYOD. (a) The Type B and (b) the Canadian
Nipher manual gauges at the DND site.
172 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
rain and drizzle, respectively.When no precipitation was
reported (;88% of the time) or the clear case, the
PWD22 agreed well with the FS11P. Based on the
PWD22 and FS11P sensors, the frequency of pre-
cipitation events during the entire measurement period
was quite low, only 12%. Most of the reported pre-
cipitation types were snow with a frequency of 8% and
followed by rain 3%. There were only a few cases of L
and IP reported (see Fig. 4).
b. Frequency distribution of precipitation typecompared with human observer
For this comparison hourly data collected during the
same period mentioned in section 3a were used. From
the present weather sensors, hourly precipitation type
reports were extracted by using the nearest to human
observer time via an interpolation method. It is cus-
tomary to segregate precipitation phases using temper-
ature; for example, Rasmussen et al. (2011) assumed
precipitation phase as solid for (T , 08C), liquid for
(T. 48C), andmixed for other temperatures. To test such
assumptions, the hourly precipitation type frequency
data were also segregated based on these temperature
intervals as shown in Fig. 5. When all the temperatures
were included, the observer reported no precipitation
with a frequency of 86%, which was very close to the
frequency of ;88% that was reported by both the
PWD22 and FS11P, which is similar to the 1-min data.
The human observer reported a frequency of 9.8% for
snow events and 3.5% for rain events as compared to
;8% and 4% for snow and rain events, respectively,
reported by both the PWD22 and FS11P sensors. This
shows that the observer reported only ;1% more snow
cases, indicating excellent agreement with the sensors.
According to the observer, a relatively small frequency
of drizzle cases (0.15%) occurred during the observation
period, and the optical sensors reported more drizzle
cases (;0.4%). Both optical sensors, the PWD22 and
FS11P probes, reported significantly more ice pel-
lets events with frequencies of 0.14% and 0.2%, re-
spectively, as compared to the value reported by the
human observer, which was 0.024%. According to the
observer, there were a small number of solid pre-
cipitation cases associated with IC and SG that were not
seen by the probes (see Fig. 5). The observer reported
some freezing rain, freezing drizzle, and mixed-phase
cases, but the sensors are not capable of reporting these
events. When the data were segregated for T. 48C, theoccurrence of no-precipitation events increased from
86% to 94%, and the rain events also increased as ex-
pected, but based on the observer, snow and mixed-
phase events were not totally eliminated under this
condition. Similarly, when the temperatures were below
freezing, although the proportion of snow events in-
creased as compared to the unsegregated data (by a
factor of 2.5 based on the observer), there were still a
few reported cases of rain, freezing precipitation, and
mixed-phase events. When the temperatures were be-
tween 08 and 48C, based on the human observer the
proportion of the mixed-phase events increased from
0.2% to 1.13%, which was quite significant, but the rain
events were also increased by more than a factor of 1.6
as compared to the unsegregated data. Based on the
human observation, it was only when T,228C that all
the rain and drizzle cases were eliminated, but the
liquid phase in the form of freezing rain and freezing
drizzle was still reported (see Fig. 5). The sensors
appeared to be reporting the ZL and ZR cases as
rain. Therefore, based on this study, identification
of precipitation type based on temperature alone
could be misleading, particularly for near-freezing
temperatures.
FIG. 4. The frequency distribution of precipitation type PT re-
ported by the PWD22 and FS11P present weather sensors reported
based on 1-min data between September 2014 and August 2015.
TABLE 2. The precipitation amounts measured for solid, liquid, and mixed events.
Instruments Solid LWE (mm) Liquid (mm) Mixed (mm) Total (mm)
Pluvio2 54.5 244.95 20.8 320.3
PWD22 120.9 303.1 27.3 451.3
FS11P 92.8 318.8 30.9 442.5
Manual 90.8 249.6 24.7 365.2
JANUARY 2017 BOUDALA ET AL . 173
c. Precipitation
1) BEFORE CONSIDERATION OF THE WIND EFFECT
The data used for these comparisons include 1-min-
averaged precipitation measured using the Vaisala
FS11P and PWD22, and OTT Pluvio2 gauges, and
6-hourly manually measured liquid water equivalent
(LWE) and rain amounts measured using the Nipher
shield and Type B gauges, respectively. The data were
collected during the same September 2014–August
2015 observation period.
The time series of the total precipitation intensities
(Fig. 6a), accumulations and type (Fig. 6b), temperature
(Fig. 6c), and wind speed (Fig. 6d) for the entire mea-
surement period is given. In Fig. 6d, the red line repre-
sents median filtered wind speed data using a 6-h window
for clarity. The monthly amounts of total precipitation
accumulation are given in Fig. 6b. In September the
present weather sensor indicated all liquid phase pre-
cipitation except for one ice pellet case (Fig. 6b). For the
liquid phase, all sensors are expected to perform well,
particularly the catchment-type gauges (Pluvio2 and
Type B), since the wind effect is minimal. Both optical
sensors, the FS11P and PWD22, agreed with the Type B
manual gauge, but the Pluvio2 measured a relatively
lower amount (Fig. 6b). The precipitation amount mea-
sured in October 2014 was relatively low and mostly
occurred in liquid form with some snow cases, since the
temperature remainedmainly above freezing. In this case
the Pluvio2 agreed with the total precipitation measured
manually using the Nipher and Type B gauges. The two
sensors, PWD22 and FS11P, measured a much larger
precipitation amount. Relatively large amounts of
snowfall occurred in November. There were also a few
cases of rain and drizzle during the early period of the
month, when the temperatures were warmer. The FS11P
agreed with the manual measurement remarkably well
within 98% of the manual measurements (Fig. 6b, No-
vember 2014). On the other hand, the Pluvio2 gauge
collected only 50% of the amount that was collected
manually and the PWD22 measured about 43% higher
than the manual measurements (Fig. 6b, November
2014). The underestimation of the snow amount by the
Pluvio2 gauge is associated with wind speed, since the
wind speed sometimes reached 10ms21 (Fig. 6d), and
this will be discussed in more depth later. As with Octo-
ber, December was a relatively dry month, only 5mm of
snow was measured based on the manual measurements,
but the FS11P and PWD22 measured higher amounts.
The precipitation intensities measured in December and
also in October were mostly light and hence the opti-
cal probes measured higher amounts of precipitation
(Fig. 6b), indicating that noncatchment-type optical sen-
sors are more sensitive than the catchment-type gauges.
There were some warmer periods in December: the
temperature ranged from 2308C to almost 108C, ex-
hibiting very large temperature variations (Fig. 6c). Similar
temperature variations also occurred in January 2015 and
were associated with mixed-phase precipitation (drizzle,
FIG. 5. The frequency distributions of precipitation type PT based on the PWD22 measurements for various
temperatures.
174 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
rain, ice pellets, and snow). Under this mixed-phase con-
dition, the total amounts of precipitation measured in
January were 15, 19, 26, and 26mm using the Pluvio2
gauge, manual method, and PWD2 and FS11P optical
probes, respectively, indicating that the Pluvio2 gauge un-
derestimated the precipitation relative to the other probes
(Fig. 6b, January 2015). The temperature in February
mostly remained below freezing and hence the pre-
cipitation type measured was mainly snow and ice pellets.
In this case the snowfall amount measured using the
PWD22 sensor (37mm) was closer to the manual mea-
surement (30mm) compared to the relatively smaller
amounts measured with the FS11P sensor (23mm) and
Pluvio2 gauge (19mm). In March 2015, the temperature
varied from2358 to 168Cand receivedmixedprecipitation,
although not very significant, and the measured values
rangedonly fromapproximately 8 to 10mm, indicating that
all the instruments showed similar performance. In April
the temperature varied from 2158 to 258C and the
precipitation phase was mixed, and relatively significant
total precipitation amounts of 64, 57, 40, and 38mm were
measured by the FS11P, PWD22, Pluvio2, and manual
gauges, respectively. In this case the Pluvio2 and manual
gauges agreed reasonably well. In May and June, the pre-
cipitation phase wasmainly rain except for a few snow and
ice pellet cases that occurred during the early part of May.
For both months the Pluvio2 and manual gauges agreed
reasonably well, indicating that when rain dominates, the
two probes measure similar amounts of precipitation.
In July and August 2015, the Pluvio2 and Type B both
measured 34mm of rain in August, and 72 and 76mm,
respectively, in July, indicating good agreement. The
optical probes, PWD22 and FS11P, both measured rel-
atively higher rain amounts of 86 and 41mm for July and
August, respectively. The ratios of the total precipitation
relative to the manual measurements were 1.21, 1.24, and
0.87 for the FS11P, PWD22, and Pluvio2, respectively,
indicating that the FS11P and PWD22 overestimate
FIG. 6. (a) The time series of the total precipitation intensities, (b) accumulations and type, (c) temperature, and
(d) ws for the entire measurement period. In (d), the red line represents the median filtered wind speed data using
a 6-h window. (bottom) Monthly precipitation amount measured using all the instruments.
JANUARY 2017 BOUDALA ET AL . 175
precipitation by 21% and 24%, respectively, and the
Pluvio2 underestimates the amount by 13% as compared
to the manually measured value. The underestimation of
the Pluvio2 could be partly attributed to wind-induced
loss during snowfall (Rasmussen et al. 2012 and references
therein). Since the optical probes are not expected to be
significantly affected by wind, the overestimation of the
precipitation amount by these probes could be attributed
to the fact that they are more sensitive than the manual
gauges. Validity of these possibilities will be explored in
the next section.
2) DETERMINATION OF PRECIPITATION TYPE FOR
6-HOURLY PRECIPITATION DATA
To assess the effect of wind on the collection efficiency
of the gauges, it is necessary to use 6-hourly precipitation
amounts measured using the manual gauges and the
present weather sensors because manual measurements
are available only on a 6-hourly time scale. Since the
sensor-based available observed precipitation type data
have a 1-min time resolution, and there is an hourly time
resolution for the human observer, it is challenging to
identify a 6-hourly precipitation type using these datasets.
As discussed earlier, for relatively warmer near-freezing
temperatures, it is particularly difficult to segregate the
precipitation phase. However, the manual 6-hourly
precipitation data are already segregated as solid,
liquid, and mixed. This was normally done by a hu-
man observer using the hourly weather observations,
6-hourly snow depth, and total precipitation gauge
(Nipher shield gauge) measurements. For all snow
cases, the snowfall amount is determined by using the
Canadian Nipher gauge as described in section 2. For
mixed-phase cases, the amount of rain is estimated by
subtracting the liquid water equivalent estimate based
on themeasured snow depth assuming a 10:1 snowwater
ratio from the total precipitation measured using the
Canadian Nipher. Thus, in mixed-phase cases, there is
some uncertainty associated with the 10:1 snow density
assumption. Figure 7 shows the fraction of LWE pre-
cipitation amount plotted against the observed mean
temperature based on 6-hourly data. In themixed-phase
cases, the solid fraction approximately linearly in-
creased from 0.3 to 0.9 with decreasing temperature
from about 48 to 248C, but there were also all solid and
all liquid cases within this temperature interval. In this
study, the precipitation phase is segregated based on
solid fraction as shown in Fig. 7.
3) SENSITIVITY
Figure 8 shows the frequency distributions of 1-min-
averaged precipitation intensities for solid precipitation
(Fig. 8a), and the associated frequency distributions for
temperature (Fig. 8b) and wind (Fig. 8c), and the dis-
tribution of liquid phase intensities (Fig. 8d), and the
associated temperature and wind distributions are given
in Figs. 8e and 8f, respectively. The snow and rain cases
were identified based on 1-min PWD22 data. The Pluvio2
gauge reported more no-precipitation events (94%) as
compared to the optical probes (;70%) in solid phase,
and 78% in liquid phase as compared to 41% in rain cases
for the optical probes, indicating the Pluvio2 gauge
missed some precipitation events. The number of light
snow and rain-rate (,1mmh21) cases were much lower
for the Pluvio2 thanmeasured by the PWD22 and FS11P,
but the number of cases that the Pluvio2 measured in-
creased for higher precipitation intensities (.2mmh21).
In fact, the Pluvio2 gauge appears to report more pre-
cipitation cases for rates higher than 2mmh21 for snow.
This could have some compensating effect in the total
amount. The temperature distribution during snow var-
ied from about 08 to2308Cwith amaximumnear2108C,and during the liquid phase the temperature varied
from 258 to 208C with a maximum near 158C. The wind
speed distributions were identical during both snow and
liquid phase cases, varying from near 0 to 11ms21. For
comparison, the sensitivity of these instruments during
snowand rainwere also investigatedusing 10-min-averaged
data. In this case the precipitation phase was identified
based on the 10-min-averaged temperature (snow for
T,248C and rain forT. 48C) and the results are given
in Fig. 9. The figure shows the fraction of the snowfall-
rate (Fig. 9a) and rain-rate (Fig. 9b) contribution to the
perspective total snow and rain amount, respectively. As
depicted in the figure, for 10-min-averaged data, the light
snowfall rate (,’0.4mmh21) contributes a relatively
small fraction to the total amount for the Pluvio2 (’20%)
as compared to almost 40%–50% for the optical FS11P
and PWD22 probes (Fig. 9a). For 10-min-averaged data,
FIG. 7. The observed fraction of LWEprecipitation amount plotted
against the observed mean temperature.
176 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
the sensitivity of Pluvio2 for rain is relatively comparable
to the optical probes (Fig. 9b).
4) THE EFFECT OF WIND AND CORRECTIONS
Figure 10 shows the 6-hourly precipitation amounts
measured using the PWD22, FS11P, and Pluvio2 plotted
against the manual measurements for solid precipitation
(Figs. 10a–c) and liquid precipitation (Figs. 10d–f). For
the liquid phase precipitation, all instruments agreed
quite well with a correlation coefficient (R) close to 0.96.
For the solid phase precipitation, however, the correla-
tion coefficient varied from 0.62 for the FS11P to 0.88 for
FIG. 9. The fractional contribution of the observed precipitation rate to the total amount for (a) solid phase and
(b) liquid phase.
FIG. 8. (a) The frequency distributions of precipitation intensities for solid, and the associated frequency dis-
tributions for (b) temperature and (c) wind; (d) the distribution of liquid phase intensities; and the associated
(e) temperature and (f) wind distributions.
JANUARY 2017 BOUDALA ET AL . 177
the Pluvio2, indicating that the Pluvio2 was correlated
with the manual gauge better than the optical gauges.
However, as depicted in Figs. 10a–c, there is significant
scatter for the solid phase case, particularly for the
Pluvio2 gaugewhen LWE rates, 1mmh21. This scatter
is mainly due to the wind effect and also the type of snow
(Thériault et al. 2015, 2012; Colli et al. 2015, 2016a,b).The precipitation amounts measured by each in-
strument and for each phase are given in Table 2.
Generally, themeasured liquid phase precipitation amounts
were larger than the solid phase andmixed-phase amounts.
Overall, the instrumentsmeasured similar amounts in liquid
precipitation as compared to the solid phase case, particu-
larly the Pluvio2 gauge. The collection efficiencies of the
instruments as compared to the manual gauges are given in
Table 3. The collection efficiency of the Pluvio2 gauge was
0.98 for the liquid phase and 0.84 for themixed-phase cases,
indicating relatively good agreement with the manual
measurements. During the solid precipitation events, the
Pluvio2 gauge underestimated the precipitation by 40%,
which is quite significant and suggests the effect of wind.
ThePWD22 sensor overestimated the amount as compared
to themanualmeasurements by33%during solidphase and
21% during liquid precipitation events. The FS11P probe
FIG. 10. The 6-hourly precipitation amounts measured using the PWD22, FS11P, and Pluvio2 plotted against the manual measurements
for (a)–(c) solid precipitation and (d)–(f) liquid precipitation.
TABLE 3. The collection efficiency of the instruments as compared
to the manual gauges before correction for wind speed.
Precipitation phase Pluvio2 PWD22 FS11P
Snow 0.6 1.33 1.02
Rain 0.98 1.21 1.3
Mixed 0.84 1.11 1.25
178 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
agreed with the manual measurements in the solid phase
but overestimated the amount by about 30% in the liquid
phase. The measurement differences that were observed
between these instruments during liquid precipitation
events could be partly associated with the difference in the
sensitivity of the instrument, but generally such small dif-
ferences are expected and hence it can be assumed that the
instruments agreed well within measurement uncertainty.
However, the undercatch by the Pluvio2 gauge in snow is
significant and this is investigated in the following section.
To consider the effect of wind in the catch efficiency
of the single Alter Pluvio2 gauge (CEPluvio2), it is first
necessary to investigate the collection efficiency of the
Nipher shield gauge (CENipher). It should be noted that
theCanadianNipher gauge that was used tomeasure the
solid phase (LWE) amount could also be affected by
wind in addition to wetting and evaporation losses
(Goodison et al. 1998; Yang et al. 2005). Here only the
effect of wind is considered. Figure 11 shows the transfer
function CENipher [wind speed (ws)] given in Goodison
et al. 1998. This function was derived based on the daily
observed wind and snow accumulation data, and hence
it should be noted that this transfer function has some
unquantified uncertainty. Using this transfer function,
the LWE amount measured using the Nipher shield
gauge was corrected as
LWEcorNipher 5
LWENipher
CENipher
. (1)
Figure 12 shows the collection efficiency of the Pluvio2
gauge with a single Alter shield (SA) relative to the
corrected manually obtained data [given in Eq. (1)]
plotted against the observed mean wind speed (Fig. 12a),
and the number of data points averaged for every wind
speed bin width of 0.6ms21 (Fig. 12b). The best-fit curve
of the mean data, which is the collection efficiency of the
Pluvio2 gauge, is given as
CEPluvio2
5 12 1:517e24:597/ws, (2)
where the wind speed is given in meters per second. The
correlation coefficient of the fit and the root-mean-
square error were 0.9 and 0.13mm, respectively. Based
on the transfer function given in Fig. 12, the SA Pluvio2
gauge caught approximately 85% of the true snowfall
amount at 2m s21 as compared to about 40% at 5ms21,
FIG. 11. Transfer functionCENipher proposedbyGoodisonet al. (1998)
is plotted as function of the observed wind speed during snow.
FIG. 12. The collection efficiency of the Pluvio2 data relative to the corrected data [given in Eq. (1)] plotted against
(a) the observed mean wind speed and (b) the number of data points averaged for each wind speed interval.
JANUARY 2017 BOUDALA ET AL . 179
and these results are consistent with the findings for the
Geonor gauge with an SA shield catch efficiency calcu-
lated based on the DFIR (Wolff et al. 2015). Note that
the catch efficiency slope shown in Fig. 12 tends to level
off near 7–8ms21, approaching near 20%catch efficiency
but not to the degree suggested by Wolff et al. Similar
studies using both computational fluid dynamics model-
ing and observation data based on an SA Geonor gauge
suggest some leveling off of the catch efficiency slope at
higher wind speeds (Thériault et al. 2012; Colli et al. 2015,2016a,b). However, the modeled catch efficiency curves
provided in these studies mainly focused on two
extremes—dry and wet snow cases—and therefore it is
difficult tomake direct comparisons to this study, but the
results could be used to explain some of the uncer-
tainties shown in Fig. 12. Figure 13 shows the corrected
values of precipitation data from the Nipher gauge using
Eq. (1) and the adjusted Pluvio2 gauge data using
Eq. (2), and measured precipitation using the PWD22
and FS11P plotted against the number of hours of solid
precipitation during the entire measurement period. It is
interesting to note that the corrected Nipher gauge and
adjusted Pluvio2 data agreedwell with the datameasured
using the PWD22, but the FS11P appears to slightly un-
derestimate the precipitation.
5) PARAMETERIZATION OF THE FREQUENCY
DISTRIBUTION OF PRECIPITATION INTENSITY
Understanding the statistical characteristics of pre-
cipitation intensity is important for many applications,
including understanding the hydrological cycle of a
given location. Traditionally, the measured precipita-
tion is fitted using a given statistical probability density
function, such as a lognormal function (Kedem and Chiu
1987; Cho et al. 2004) or a gamma distribution function
(Kedem et al. 1994; Dan’azumi et al. 2010). In this sec-
tion four possible probability density functions (pdfs)—
Gaussian (normal), lognormal, and inverse Gaussian
are given in Eqs. (3)–(5), respectively, and gamma
function (not given below)—are considered and
tested,
fln(p
r,m,s)5
1
prs
ffiffiffiffiffiffi2p
p Exp
"2(lnp
r2m)2
2s2
#, p
r. 0,
(3)
fn[ln(p
r),m,s]5
1
sffiffiffiffiffiffi2p
p Exp
"2(lnp
r2m)2
2s2
#, p
r. 0,
(4)
flg(p
r,m,l)5
�l
2pp3r
�1/2
Exp
2642l(p
r2m
g)2
2m2pr
375,
pr. 0, l. 0, m. 0, (5)
where l is the shape parameter; mg is the scale param-
eters; m and s are natural logarithm of the geometric
mean and standard deviation, respectively; and pr rep-
resents the precipitation intensity for a given phase.
Based on the analysis discussed earlier in this study, it
was demonstrated that the PWD22 performed quite well
as compared to the corrected manual Nipher gauge mea-
surements. Thus, for this statistical analysis, the entire
1-min-averaged dataset was used. The PWD22 data were
segregated as rain, solid, and drizzle precipitation types
measured using the same instrument. To test the log-
normality of the observed precipitation intensity, the
frequency distribution of the log of the observed precipi-
tation intensity [ ln(pr)] for solid (Fig. 14a, top), rain
(Fig. 14b, top), drizzle (Fig. 14c, top), and total pre-
cipitation (Fig. 14d, top) are given. This suggests that the
observed ln(pr) follows a normal pdf, which is consistent
with previous studies (Kedem and Chiu 1987; Cho et al.
2004). However, a visual inspection of Fig. 14b (top) shows
that for snow there are some discrepancies at the very low
precipitation intensities. This is where some measurement
uncertainties are expected. Fig. 14b shows the histogramof
the observed precipitation intensities similar to Fig. 14a
and the best-fit curves of the lognormal and inverse-
Gaussian probability density functions. A chi-square (x2)
goodness-of-fit test was performed for each curve at a 0.05
significance level by employing a null hypothesis that the
data came from a given pdf. The test returns a decision (h)
represented by 1 (no) or zero (yes), including a P value of
the test. If theP value is less than 0.05 combined with h5 1
statistically considered that the null hypothesis has to be
rejected and hence the given pdf can be considered for
describing the observed data. The results of these tests,
FIG. 13. Comparisons of corrected values of precipitation data
from the Nipher gauge and Pluvio2 and measured using the
PWD22 and FS11P are plotted against the number of hours of solid
precipitation during the entire measurement period.
180 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
FIG. 14. (top) The frequency distribution of the natural log of the observed precipitation rate values for (a) rain,
(b) snow or solid, (c) drizzle, and (d) total precipitation. The black line represents the best-fit curve for a normal
distribution. (bottom) As in (top), but for the frequency distribution of precipitation rate values. The LogN and
InvG are the best-fit curves for lognormal and inverse Gaussian, respectively.
JANUARY 2017 BOUDALA ET AL . 181
and the associated statistical parameters and values of
the coefficients, including arithmetic mean and variance
(V) of the distributions, are given in Table 4. As given in
the table, the null hypothesis was rejected for every pdf
considered and the calculated P values are much lower
than 0.05 except for the lognormal drizzle distribution.
This indicates that the considered distributions fit the
observed data. The gamma distribution function was
also tested, but the x2 null hypothesis statistical tests
indicated that the function is not suited for fitting the
solid phase precipitation (not shown in the table).
Generally, the rain distribution has higher mean and
variance values for all considered distributions, sug-
gesting more extreme events as compared to snow,
drizzle, and the combined case.
4. Snow density
As mentioned earlier, snow depth (Ds) was measured
four times daily using a snow ruler by taking the average
of 10 consecutive measurements. The snow density can
be calculated using Ds and the corrected Nipher gauge
measurements, assuming that LWEcorNipher as the snow
water equivalent (SWE). Following Rasmussen et al.
(2012), SWE is defined as the liquid equivalent of the
snow accumulation on the ground and snow depth (cm)
is a measurement of the total depth of the accumulation.
The liquid water equivalent (LWE) rate is the mass accu-
mulation rate of solid precipitation normally measured
using precipitation gauges (mmh21). Using these two
measurements, the snow-to-liquid ratio can be calcu-
lated as
ry5
10Ds
SWE, (6)
where Ds is given in centimeters and SWE is given in
millimeters. The snow density rs (g cm23) can be
given as
rs5
rw
ry
, (7)
where rw is the density of water, which is assumed to be
1 g cm23. Using the entire 6-hourly dataset mentioned
earlier, rs and ry were calculated. In this data analysis,
all the mixed-phase cases were eliminated from the data
to avoid the 10:1 snow density assumption mentioned
earlier. Figure 15 shows LWEcorNipher plotted against Ds
(Fig. 15a), and rs and ry plotted against the mean tem-
perature (Figs. 15b,c, respectively). The snow depth
varied from 0.2 to 8.4 cm, and the snow water equivalent
values varied from 0.2 to 9.93mm. As indicated in
Fig. 15a, LWEcorNipher is increasing with increasing Ds
following the best-fit nonlinear equation, defined as
LWEcorNipher 5 0:034D2
s 1 0:9Ds, r2 5 0:91, (8)
suggesting that the snow density is increasing with snow
depth. This is consistent with the findings using a much
more complex modeling approach (Sturn et al. 2010)
and large dataset analysis (Jonas et al. 2009). The snow
density ry varied from 4.2 to 35 with amean value of 12.2
or 0.082 g cm23. The mean value calculated in this study
is higher than the 10:1 ratio usually assumed for con-
verting Ds to snow water equivalent in Canada (Potter
1965; see Roebber et al. 2003 for discussions). Using 30
years of NWS Cooperative Summary of the Day data,
Baxter et al. (2005) found that themean of ry for most of
the contiguous United State to be close 13, which is very
close to the value found in this study.Onaverage, based on
the temperature-binned data as indicated by black circles,
ry is increasing with increasing temperature (Fig. 15c),
written as
ry5 0:0053T2
mean 2 0:275Tmean
1 9:92, r2 5 0:7 (9)
and hence the 10:1 ratio on average appears to be more
appropriate for warmer temperatures (T . 258C). A
TABLE 4. The best-fit coefficients for normal, lognormal, and inverse-Gaussian pdfs.
Distribution name pr phase m or mg s or l Mean V h P value
Lognormal—f ln(pr , m, s) Rain 20.829 1.551 1.453 21.26 1 2.2 3 1024
Snow 21.914 1.308 0.347 0.54 1 1.8 3 10227
Drizzle 21.633 0.859 0.282 0.09 1 0.124
Total 21.591 1.455 0.587 2.52 1 1.4 3 10225
Normal—fn[ln(pr), m, s] Rain 20.829 1.551 0.437 22.23 1 2.3 3 10248
Snow 21.914 1.308 0.148 13.68 1 0
Drizzle 21.633 0.859 0.195 5.57 1 1.4 3 10215
Total 21.591 1.455 0.204 18.36 1 1.5 3 102112
Inverse Gaussian—flg(pr , mg, l) Rain 1.311 0.166 1.311 13.57 1 1.0 3 1028
Snow 0.330 0.091 0.330 0.395 1 3 3 10236
Drizzle 0.276 0.248 0.276 0.055 1 0.01
Total 0.639 0.099 0.639 2.636 1 8.0 3 10281
182 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 34
recent study conducted by Boudala et al. (2014b) in a
relatively wetter and warmer climate in Vancouver,
British Columbia, Canada, showed that the mean value
of ry for wet snow was close to 10:1, which is on average
lower than the mean ry value determined in this study
but closer to the value determined for warmer temper-
atures as would be expected.
5. Summary and conclusions
In this paper precipitation amount and type, and snow
depth data collected during the 4Wing Cold Lake Re-
search Project covering the period from September 2014
to August 2015 using various instruments, including the
Vaisala PWD22 and FS11P sensors, the OTT Pluvio2
gauge, the Canadian Nipher and Type Bmanual gauges,
and a snow rule measuring depth, have been analyzed.
The analysis indicated that most (80% of the total) of
the measured snow intensities, by LWE, were relatively
low (,1.5mmh21; Fig. 9). During snow events the ob-
served temperature varied from 2358 to 48C and the
wind speed reached 12ms21 with amean value of 4ms21
and a standard deviation of 2ms21. Based on the 1-min-
averaged data collected using the optical PWD22 and
FS11P sensors, the precipitation events during the entire
measurement period represented only 12% of the total
time. Most of the reported precipitation types were snow
with a frequency of 8% followed by rain at 3% of the
time. There were only a few cases of drizzle and ice pellet
events. The precipitation types reported by the present
weather sensors were also compared with the hourly
human observation data for various temperature in-
tervals. When all the temperatures were included, the
observer reported no precipitation with a frequency of
86%,which was very close to the frequency of;88% that
was reported by both the PWD22 and FS11P. The human
observer reported snow and rain 10%and 4%of the time,
respectively, as compared to approximately 8% and 4%
measured by both PWD22 andFS11P sensors. This shows
that the observer reported only ;1% more snow cases,
indicating excellent agreement with the sensors. Ac-
cording to the observer, drizzle cases occurred 0.15% of
the time during the observation period, which was very
close to the value from the sensors (;0.4%). Both the
PWD22 andFS11P probes reported significantlymore ice
pellets events with frequencies of 0.14% and 0.2%, re-
spectively, as compared to the value reported by the
human observer, which was 0.024% of the time.
The amounts of 6-hourly precipitation measured us-
ing the Pluvio2 gauge with a single Alter shield, Cana-
dianNipher and Type B gauges, and theVaisala PWD22
and FS11P sensors were also compared. The comparisons
revealed that the collection efficiencies of the Pluvio2
gauge as compared to the manual measurements were
0.98 and 0.83 for liquid and mixed-phase cases, re-
spectively, indicating relatively good agreement with the
manual observations. For the solid phase, however, the
Pluvio2 gauge significantly undercaught with a ratio of
only 0.57, possibly due to wind effects. The Vaisala
PWD22 overestimated the amount as compared to the
manual measurements by 33% and 21% during solid
and liquid precipitation events, respectively. The FS11P
probe agreed with the manual measurements during
snow, but it overestimated the amount by about 30%
during rain. After correcting for the effect of wind dur-
ing snow for both the Canadian Nipher and the Pluvio2
FIG. 15. (a) Liquid wqater equivalent LWEcorNipher plotted againstDs, and (b) ry and (c) rs plotted against the mean temperature. The open
circles in (b) and (c) represents the temperature-binned data.
JANUARY 2017 BOUDALA ET AL . 183
gauge, the data agreed remarkably well with the PWD22
measurements, but the FS11P measurement was still
slightly lower. Thus, these findings have demonstrated
the usefulness of emerging technologies, such as the
PWD22 and FS11P probes, showing that they can be
used for measuring snowfall in cold climates, where the
snowfall intensity tends to be relatively low.
After demonstrating the good performance of the
PWD22, the 1-min-averaged precipitation intensity fre-
quency distributions were obtained using this probe, in-
cluding the solid and liquid phases. Several probability
density functions, including gamma, normal, lognormal,
and inverse Gaussian, were used to fit the observed fre-
quency distributions. The goodness of the fits were tested
using null hypothesis chi-square statistics, and based on
these tests it was found that the observed precipitation
intensities can be described by lognormal and inverse-
Gaussian distributions.
Using the snow depth and corrected Nipher gauge
data, snow densities were derived. The snow density or
snow-to-liquid ratio varied from 4.2 to 35 with a mean
value of 12.2 or 0.082 g cm23, which suggests that the
mean value derived in this study is higher than the
10:1 ratio usually assumed for converting snow depth
to snow water equivalent in Canada. On average, the
snow density depends on temperature, increasing
with increasing temperature, and the 10:1 ratio ap-
pears to be more appropriate for relatively warmer
temperatures.
Acknowledgments. This work was partially funded by
the Department of National Defence (DND) and the
Canadian National Search and Rescue New Initiative
Fund (SAR-NIF) under the SAR Project (SN201532).
We like to thank Mike Harwood and Robert Reed for
helping with the installation of the instruments at Cold
Lake. The authors also would like to extend their thanks
to Ramond Dooley, Amy Slade-Campbell, and Gordon
Lee of DND for providing the hourly precipitation data,
and also Randy Blackwell for helping during the in-
strument installation process.
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