Introduction
In western China, mountainous watersheds are the source areas of runoff
generation and water resources, and accurate precipitation measurements are
extremely important for calculating the water balance and understanding the
water cycle processes in these high mountains. It is widely recognised that
precipitation gauge measurements contain systematic errors caused mainly by
wetting, evaporation loss and wind-induced undercatch, and that snowfall
observation errors are very large under high wind (Sugiura et al., 2003).
These errors affect the evaluation of available water in a large number of
economic and environmental applications (Tian et al., 2007; Ye et al., 2012).
For decades, all knowledge of precipitation measurement errors has relied on
field experiments. Back in 1955, the World Meteorological Organization (WMO)
conducted the first precipitation measurement intercomparisons (Rodda,
1973). The reference standard was a British Meteorological Office Snowdon-type (Mk2)
gauge elevated 1 m above the ground and equipped with an
Alter wind shield, which did not accurately reflect the precipitation level
(Struzer, 1971). Rodda (1967) compared the catch of a UK 5 in. manual gauge,
exposed normally at the standard height of 30.5 cm above ground, with a
Koschmieder-type gauge exposed in a pit. The gauge in the pit caught 6 %
more precipitation than the normally exposed gauge. In the second WMO
precipitation measurement intercomparison (rain, 1972–1976), a pit with an
anti-splash grid was designated the reference standard shield for rain
gauges (Sevruk and Hamon,1984). In the third WMO precipitation measurement
intercomparison (snow, 1986–1993), the Double Fence International Reference (DFIR)
with a Tretyakov shield was designated the reference standard snow
gauge configuration (Goodison et al., 1998). In the fourth WMO precipitation
measurement intercomparison (rain intensity, 2004–2008), different
principles were tested to measure rainfall intensity and define a
standardised adjustment procedure (Lanza et al., 2005). Because automation
of precipitation measurements was widespread, the WMO Commission for
Instruments and Methods of Observation (CIMO) organised the WMO Solid
Precipitation Intercomparison Experiment (WMO-SPICE; Wolff et al., 2014) to
define and validate automatic field instruments as references for gauge
intercomparison, and to assess the automatic systems and operational
networks for precipitation observations. The experiments and investigations
are ongoing, and the WMO-SPICE project confirms the DFIR shield to be a part
of the reference configurations.
The DFIR shield has been operated at 25 stations in 13 countries around the
world (Golubev, 1985; Yang et al., 1993; Sevruk et al., 2009), but deviations from the DFIR
measurements vary by gauge type and precipitation type (Goodison et al.,
1998). In China, the Chinese standard precipitation gauge (CSPG) and the
Hellmann gauge were first compared using the DFIR shield as a reference
configuration at the Tianshan site (43∘7′ N,
86∘49′ E; 3720 m), during the third WMO precipitation
measurement intercomparison experiment from 1985 to 1987 (Yang, 1988; Yang
et al., 1991). The wetting loss, evaporation loss, wind-induced undercatch
and trace precipitation of the CSPGs were well quantified based on the large
volume of observation data at the Tianshan site (Yang et al., 1991). For
wind-induced undercatch, the derived CSPG catch ratio equations were based
on the 10 m height wind speed at the Daxigou meteorological station
(43.06∘ N, 86.5∘ E; 3540 m) and at several other
standard meteorological stations near the measurement site (Yang, 1988; Yang
et al., 1991). This intensive experimental field study created a basis for
later work on the correction of systematic bias in precipitation
measurements in China. From 1992 to 1998, Ren and Li (2007) conducted an
intercomparison experiment at 30 sites (the altitude ranged from about
4.8 to 3837 m) using the pit as a reference across China, and a total of
29 276 precipitation events were observed. Yang et al. (1999) emphasised that among
all known systematic errors in precipitation observation, wind-induced gauge
undercatch was the greatest source of bias, particularly in cold regions,
and recommended testing for the application of adjustment techniques in
regional observation networks. In the mountainous watersheds of western
China, the complex high mountain topography and underlying surfaces with
inhomogeneous glaciers, permafrost and alpine vegetation make the wind
vector field in the lower boundary layer extremely complex, causing equally
complex wind field deformations over the gauge orifice. At present, our
investigation of wind-induced error in precipitation measurements is based
on the horizontal time-averaged wind speed. Thus it is reasonable to
investigate the regional average characteristics of wind fields and the
interaction between wind fields and the precipitation gauges at our present
research level. In addition to Yang's experimental field work on systematic
error adjustments for precipitation measurements in eastern Tianshan from
1985 to 1987 (Yang, 1988), it is necessary to carry out field experiments on
precipitation measurement in the other mountainous regions of western China.
Precipitation gauge intercomparison experiment in the Qilian Mountains, Tibetan Plateau.
Adjustment procedures and reference measurements were developed during
several WMO international precipitation measurement intercomparisons
(Goodison et al., 1998; Sevruk et al., 2009; Yang, 2014). The application of
all of these adjustment procedures and methods depends on both environmental
factors and precipitation features, and among the factors considered, wind
speed and temperature have been found to have the most important effect on
gauge catch (Yang et al., 1999). Ye et al. (2004) developed a bias-error
adjustment method for CSPGs based on observation data from 1985 to 1997 at
the Tianshan site (Yang et al., 1991), and found a new precipitation trend
in the adjusted precipitation data for the past 50 years in China (Ding et
al., 2007). The new precipitation adjustment has improved the precipitation
estimation in water balance computation for many basins in China (Ye et al.,
2004, 2012; Tian et al., 2007). Ma et al. (2014) used the
adjusted equations from neighbouring countries in addition to the
experimental results from eastern Tianshan in China (Yang et al., 1991) to
correct for wind-induced errors on the Tibetan Plateau. However, the
precipitation gauges used in the neighbouring countries were the Tretyakov,
MK2, Nepal203, Indian standard and US 8 in. As the world's third
polar region, the Tibetan Plateau and its surrounding mountain ranges is
ecologically fragile and the source of several large rivers in China and
neighbouring countries, and accurate precipitation data are urgently needed
for water resource exploitation and environmental protection. The problem is
how to apply and test the already established principal adjustment
procedures and methods to correct for precipitation measurement errors in
the vast plateau and high mountains of western China, where climatic and
environmental conditions are highly complex and variable, both spatially and
temporally. To quantify and understand the specific influences of climatic
and environmental factors on wind-induced bias in precipitation measurements
in a mountain watershed, and then test and parameterise the adjustment
equations, an intercomparison experiment was carried out for nearly 5
years on both unshielded and shielded CSPGs in a watershed in the Qilian
Mountains on the north-eastern Tibetan Plateau in China.
The CSPG is the standard manual precipitation gauge that has been used by
the China Meteorological Administration (CMA) in more than 700 stations
since the 1950s. The Alter shield (Struzer, 1971) was used by the CMA to
enhance catch of automatic gauges (Yang, 2014), and the pit and DFIR were
used to provide true rainfall and snowfall values for the WMO
intercomparison project, respectively (Yang et al., 1999). Therefore, an
unshielded CSPG, a single Alter shield CSPG (SA), a DFIR with a
Tretyakov-shielded CSPG and a CSPG in a pit were selected as the field
experiment of wind-induced bias study. This paper presents the
intercomparison experiments and their relevant data, introduces the
adjustment methods, discusses wind-induced bias in precipitation
measurements by CSPGs for different precipitation phases, analyses the
correlations between shielded and unshielded CSPGs and quantifies the
relationships between catch ratio and wind speed. The results of the present
study are also compared with other studies. In addition, the pit gauge is
evaluated for solid precipitation under these climatic conditions. The
limitations of the present study are then discussed.
Experiments and methods
Intercomparisons and data
Precipitation intercomparison experiments (Fig. 1, Table 1) were conducted at
a grassland site (99∘52.9′ E, 38∘16.1′ N; 2980 m) in the Hulu watershed in the Qilian Mountains, on the
north-eastern edge of the Tibetan Plateau, in China. A meteorological
cryosphere–hydrology observation system (Chen et al., 2014) was established
in 2008 in the Hulu watershed. The mean annual precipitation was 447.2 mm
during 2010–2012 and was concentrated during the warm season from May to
September. The annual mean temperature was 1.1 ∘C, with a July
mean (Tmean) of 12.5 ∘C and a January mean of
-12.4 ∘C over the years (Table 1). The annual potential
evaporation (E0) was 1102 mm (Table 1).
The intercomparative experiments included (1) an unshielded CSPG
(CSPGUN; orifice diameter = 20 cm, height = 70 cm), (2) a single Alter
shield around a CSPG (CSPGSA), (3) a CSPG in a pit (CSPGPIT) and
(4) a DFIR with a Tretyakov-shielded CSPG (CSPGDFIR) (Fig. 1, Table 2).
The CSPGUN, CSPGSA and CSPGPIT were installed before
September 2010, whereas the CSPGDFIR was installed in September 2012
(Table 2). In the cold season (October to April), snowfalls dominated the
precipitation events, and in the warm season (May to September), rainfall
was dominate. The precipitation was measured manually twice a day at 08:00
and 20:00 LT (local time, Beijing time) according to the CMA's standard (CMA,
2007a). In the warm season, precipitation was measured by volume, whereas in
the cold season, the funnel and glass bottle were removed from the CSPG and
precipitation was weighed under a windproof box. Any frost on the outside
surface of the collector was wiped off using a dry hand towel. In rare cases
where snow had accumulated on the rim of the collector, this was removed
before weighing.
The precipitation phases (snow, rain and mixed) were distinguished using the
CMA's standard (CMA, 2007b). Meteorological elements, including maximum air
temperature Tmax and minimum Tmin, have been measured
in conformation with the meteorological observation manual at the site since
June 2009. A meteorological tower was used to measure wind speed
(Lisa/Rita, SG GmbH; Ws), air temperature (HMP45D, Vaisala) and relative
humidity (HMP45D, Vaisala) at 1.5 and 2.5 m heights in association with
precipitation measurements (Chen et al., 2014). The time step of the
observations of the tower was 30 s and half-hourly values were obtained. The
specific meteorological conditions at the site are summarised in Table 1.
Monthly climate values at the experimental site (2010–2012).
Element
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Yearly
Monthly precipitation (mm)
3.5
2.5
11.0
8.8
67.7
69.6
87.1
111.6
57.7
24.0
2.7
1.0
447.2
Monthly mean air temperature (∘C)
-12.4
-7.7
-4.4
2.2
7.0
11.2
12.5
12.1
8.0
1.4
-5.6
-11.3
1.1
Monthly mean daily maximum air temperature (∘C)
-4.0
0.7
3.5
10.3
14.3
18.2
19.5
19.7
15.4
10.2
3.6
-1.9
9.1
Monthly mean daily minimum air temperature (∘C)
-19.0
-14.8
-11.6
-5.2
0.6
4.9
6.8
5.8
1.8
-5.5
-12.7
-18.2
-5.6
Monthly mean wind speed at the 1.5 m height (m s-1)
1.79
1.96
2.30
2.55
2.42
1.98
1.82
1.81
1.93
1.81
2.08
1.96
2.03
Monthly mean wind speed at the 2.5 m height (m s-1)
1.79
2.02
2.43
2.77
2.65
2.16
2.04
2.02
2.16
1.99
2.19
2.01
2.18
Monthly potential evaporation (mm)
31.6
47.0
79.4
124.4
140.9
155.0
141.7
127.0
101.6
75.2
47.3
31.0
1102.2
The precipitation measurement intercomparison experiment in the Qilian Mountains.
Gauge
Abbreviation
Size (ϕ denotes orifice diameter
Start date
End date
Observation
and h is observation height)
time
Unshielded China standard
CSPGUN
ϕ = 20 cm, h = 70 cm
Jun 2009
Apr 2015
20:00 and
precipitation gauge (CMA, 2007a)
08:00 LT
Single Alter shield (Struzer, 1971)
CSPGSA
ϕ = 20 cm, h = 70 cm
Jun 2009
Apr 2015
20:00 and
around a CSPG
08:00 LT
A CSPG in a pit (Sevruk and
CSPGPIT
ϕ = 20 cm, h = 0 cm
Sep 2010
Apr 2015
20:00 and
Hamon, 1984)
08:00 LT
DFIR shield (Goodison et al.,
CSPGDFIR
ϕ = 20 cm, h = 3.0 m
Sep 2012
Apr 2015
20:00 and
1998) around a CSPG
08:00 LT
Adjustment methods
This field experiment focused on two key aspects. One was a comparison of
the CSPGUN, CSPGSA, CSPGPIT and CSPGDFIR gauges. The
other was the establishment of adjustment equations for the CSPGUN and
CSPGSA using the CSPGDFIR as a reference. To adjust gauge-measured
precipitation, Sevruk and Hamon (1984) provided the general formula as
Pc=KPg+ΔPw+ΔPe+ΔPt=ΔPDFIR+ΔPw+ΔPe+ΔPt,
where Pc is the adjusted precipitation, K is the wind-induced
coefficient, Pg is the gauge-measured precipitation. Pw is the
wetting loss, Pe is the evaporation loss, Pt is trace precipitation
and PDFIR is the DFIR-shielded precipitation. For loss by the CSPG per
observation, Pw is 0.23 mm for rainfall measurements, 0.30 mm for snow
and 0.29 mm for mixed precipitation (snow with rain, rain with snow), based
on the measurements at the Tianshan site (Yang, 1988; Yang et al., 1991).
Ren and Li (2007) reported a mean Pw of about 0.19 mm for the total
precipitation over eastern China. The CSPG design reduces Pe to a
near-zero value smaller than other losses in the warm, rainy season (Ye et
al., 2004; Ren and Li, 2007). In winter, Pe is already small
(0.10–0.20 mm day-1) according to the results from Finland (Aaltonen et al., 1993) and
Mongolia (Zhang et al., 2004). To prevent evaporation loss in Chinese
operational observations on particular days, e.g. hot, dry days or days of
snow, precipitation is measured as soon as the precipitation event stops
(CMA, 2007a; Ren and Li, 2007). A precipitation event of less than 0.10 mm
is beyond the resolution of the CSPG and is recorded as trace precipitation
(Pt). Ye et al. (2004) recommended assigning a value of 0.1 mm,
regardless of the number of trace observations per day. The present study
focused on wind-induced bias in precipitation measurement by CSPGs,
specifically in high mountain environments, therefore the above mentioned
Pw, Pe and Pt values were assumed to be constant in the
computation equations.
The WMO proposed Eqs. (2)–(4) to compute the catch ratio of unshielded over
shielded Tretyakov gauges on a daily time step for three precipitation
types, and the independent variables were wind speed (Ws, ms-1) at
the gauge height and the daily maximum and minimum temperatures
(Tmax, Tmin, ∘C) (Yang et al., 1995; Goodison et al.,
1998). These equations are used over a great range of environmental
conditions (Goodison et al., 1998).
CRsnow=103.1-8.67Ws+0.3TmaxCRmix=96.99-4.46Ws+0.88Tmax+0.22TminCRrain=100.0-4.77Ws0.56,
where CRsnow (%), CRmix (%) and CRrain (%) are the catch
ratios for snow, mixed precipitation and rain, respectively.
As the CMA stations usually observe wind speed at a height of 10 m,
Eqs. (5)–(7) were used for the CSPG catch ratio versus the daily mean wind
speed Ws (ms-1) at 10 m (Yang et al., 1991). These equations are
based on the large volume of experimental precipitation gauge
intercomparison data at the Tianshan site and the wind speed data at the
Daxigou station:
CRsnow=100exp-0.056Ws100<Ws<6.2CRrain=100exp-0.04Ws100<Ws<7.3CRmix=CRsnow-CRsnow-CRrainTmean+2/4,
where Tmean is the daily mean air temperature (∘C).
Referring to Eqs. (2)–(7), two types of equation were used. One is for easy
application using 10 m height wind speed during the period of
precipitation in China. These are similar to a revised version of
Eqs. (5)–(7). The other type is similar to Eqs. (2)–(4), which use the daily
mean wind speed at gauge height. For the CSPGs, the gauge height was 70 cm
(Table 2). The catch ratio uses CSPGDFIR as the reference
(CR = CSPGX/CSPGDFIR, %; X denotes UN, SA or PIT). The
equations were fitted using SPSS software version 19.0 (IBM, 2010) and
Microsoft Excel 2007 based on the mathematical least squares method (Charnes
et al., 1976). The significance of the equations was evaluated using the
F test method (Snedecor and Cochran, 1989). If the significance level
(α) of the F test is below 0.05, the fitted equation is
significant. The lower the α value, the greater the significance.
Wind speeds at gauge height (Ws0.7) and at 10 m height
(Ws10) were calculated using half-hourly wind speed data at 1.5 m
(Ws1.5) and 2.5 m (Ws2.5) according to the Monin–Obukhov
theory and the gradient method (Bagnold, 1941; Dyer and Bradley, 1982):
WsZ=lnZ-lnZ0ln1.5-lnZ0Ws1.5lnZ0=Ws2.5ln1.5-Ws1.5ln2.5Ws2.5-Ws1.5,
where Z denotes the height that is referred to.
Results
From September 2010 to April 2015, a total of 608 precipitation events were
recorded at the intercomparison site for CSPGUN, CSPGSA and
CSPGPIT, respectively (Table 3). Snow occurred 84 times, mixed
precipitation 44 times and rain 480 times during this period. From September 2012
to April 2015, a subset of 283 precipitation events was recorded for
the CSPGUN, CSPGSA, CSPGPIT and CSPGDFIR gauges,
respectively (Table 3). During this period, snow occurred 43 times, mixed
precipitation 29 times and rainfall 211 times.
Linear correlation of gauge precipitation
At the 14 WMO intercomparison sites, a strong linear relationship was found
between Alter-shielded and unshielded Belfort gauges, Alter-shielded and
unshielded NWS 8 in. gauges, and shielded and unshielded Tretyakov gauges
for all types of precipitation, with a higher correlation for rain than for
snow (Yang et al., 1999). In the present study in the Qilian Mountains,
which experiences different environmental conditions compared to the other
14 sites, the same strong linear correlation was found among the four CSPG
instalments for rainfall, mixed precipitation and snowfall, with a higher
correlation for rain than for mixed precipitation, successively more than
for snow (Figs. 2–4). It is therefore considered that in general the
precipitation measured by shielded gauges increases linearly with that of
unshielded gauges. However, the relative increase in linear correlation
should depend on the specific environmental conditions. For solid
precipitation, some non-linear factors interfered with the linear
relationship to reduce the correlation coefficient.
Comparisons of wind-induced bias
From September 2010 to April 2015, the CSPGPIT caught 4.7 and
3.4 % more rainfall than the CSPGUN and the CSPGSA respectively
((CSPGPIT - CSPGUN)/CSPGUN × 100; similarly hereinafter). The
CSPGSA caught 1.3 % more rainfall than the CSPGUN (Table 3).
During the period from September 2012 to April 2015, the CSPGSA,
CSPGPIT and CSPGDIFR caught 0.9, 4.5 and 3.4 % more
rainfall, respectively, than the CSPGUN, and the CSPGPIT and
CSPGDFIR caught 3.6 and 2.5 % more rainfall, respectively, than
the CSPGSA. However, the CSPGDFIR caught 1.0 % less rainfall
than the CSPGPIT (Table 3, Fig. 2). These comparative results indicate
that the CSPGPIT caught more rainfall and total precipitation compared
to the CSPGDFIR and other gauges at the experimental site (Table 3, Fig. 2).
A total of 29 mixed precipitation events were observed from September 2012
to April 2015. As shown in Table 3, the CSPGPIT caught the most mixed
precipitation among the gauges, capturing 82.2 mm of mixed precipitation in
29 events, but only 1.1 mm more than the CSPGDFIR. The linear
relationship between the CSPGPIT and CSPGDFIR is statistically
significant with an R2 value of about 0.98 (Fig. 3f). Thus for mixed
precipitation, in addition to the CSPGDFIR, the CSPGPIT could also
be selected as a reference gauge for the CSPGUN and CSPGSA at the
experimental site.
From September 2012 to April 2015, the CSPGSA, CSPGPIT and
CSPGDFIR caught 11.1, 16.0 and 20.6 % more snowfall,
respectively, than the CSPGUN, and the CSPGPIT and CSPGDFIR
caught 4.4 and 8.5 % more snowfall, respectively, than the CSPGSA
(Table 3). Although the CSPGDFIR caught 3.9 % more snowfall compared
to the CSPGPIT (Table 3), the difference in total snowfall (43 events)
between the CSPGDFIR and CSPGPIT was only about 3.4 mm (Table 3).
Their linear correlation was highly significant with an R2 value of 0.994
(Fig. 4f). Blowing snow and thick snow cover have traditionally limited
the pit's use as a reference for snowfall and mixed precipitation. At the
experimental site, blowing snow was rarely observed and the snow cover was
usually shallow. This suggests that the CSPGPIT could be used as a
reference gauge for snow precipitation events at the site with shallow snow
cover and rare blowing snow event.
Summary of precipitation observations at the Hulu watershed intercomparison
site, 2010–2015.
Date
Phase
No. of
Total precipitation and catch ratio (CR, %)
events
CSPGUN
CR
100CSPGSACSPGUN-1
100CSPGPITCSPGUN-1
100CSPGDFIRCSPGUN-1
CSPGSA
CR
100CSPGPITCSPGSA-1
100CSPGDFIRCSPGSA-1
CSPGPIT
CR
100CSPGDFIRCSPGPIT-1
CSPGDFIR
CR
(mm)
(mm)
(mm)
(mm)
Sep 2010–
All
608
1986.8
2.6
6.5
2038.1
3.8
2115.1
Apr 2015
rain
480
1700.7
1.3
4.7
1723.4
3.4
1781.4
mixed
44
139.9
6.1
12.1
148.5
5.6
156.8
snow
84
146.2
13.7
21.0
166.2
6.4
176.9
Sep 2012–
All
283
1066.7
94.9
2.0
6.0
5.3
1088.4
96.9
3.9
3.2
1130.9
100.6
-0.6
1123.7
100
Apr 2015
rain
211
920.7
96.7
0.9
4.5
3.4
928.6
97.5
3.6
2.5
961.8
101.0
-1.0
952.2
100
mixed
29
71.1
87.6
7.7
15.6
14.2
76.6
94.3
7.3
6.0
82.2
101.2
-1.2
81.2
100
snow
43
74.9
82.9
11.1
16.0
20.6
83.2
92.1
4.4
8.5
86.9
96.2
3.9
90.3
100
Intercomparison plots among CSPGUN, CSPGSA,
CSPGPIT and CSPGDFIR for the rainfall events from
September 2010 (a, b, d) and September 2012 (c, e, f) to April 2015.
Intercomparison plots among CSPGUN, CSPGSA,
CSPGPIT and CSPGDFIR for the mixed precipitation events from
September 2010 (a, b, d) and September 2012 (c, e, f) to April 2015.
Intercomparison plots among CSPGUN, CSPGSA,
CSPGPIT and CSPGDFIR for the snowfall events from
September 2010 (a, b, d) and September 2012 (c, e, f) to April 2015.
To sum up the comparisons of wind-induced bias, from most to least rainfall
and mixed precipitation measured, the instruments ranked as follows:
CSPGPIT > CSPGDFIR > CSPGSA > CSPGUN,
while for snowfall their ranking was CSPGDFIR > CSPGPIT > CSPGSA > CSPGUN.
Catch ratio vs. wind speed
Previous studies have shown that wind speed during the precipitation period
is the most significant variable affecting gauge catch efficiency (Metcalfe
and Goodison, 1993; Yang et al., 1995; Goodison et al., 1998). Because the
CMA stations observe wind speeds at 10 m height, the CSPGUN and
CSPGSA adjustment equations for a single precipitation event were
obtained for 10 m height wind speeds. On the daily scale, adjustment
equations similar to Eqs. (2)–(4) were also obtained, based on the daily
mean wind speed converted to gauge height (0.7 m for the CSPGs) and air temperature.
To minimise ratio scatter for the different gauges, precipitation events
greater than 3.0 mm are normally selected for the CR vs. wind analysis (Yang
et al., 1995, 2014). However, in the Hulu watershed, most
snowfall and mixed precipitation events were less than 3.0 mm, thus the
limit was reduced and single or daily snowfall and mixed precipitation
events greater than 1.0 mm were selected, while rainfall events greater than
3.0 mm were selected. The numbers of selected precipitation events are shown
in Table 4. The CR vs. wind speed relationships for different precipitation
types were determined using cubic polynomials and exponential functions and
are summarised in Table 4. The CRUN/DFIR and CRSA/DFIR vs. wind
speed relationships are statistically significant, but the CRPIT/DFIR
vs. Ws0.7 or Ws10 relationships do not pass the F test with
α = 0.10. This phenomenon indicates that both PIT and DFIR are
effective in preventing wind from influencing the gauge catch of
precipitation, therefore the CRPIT/DFIR is not related to wind speed.
Catch ratios (CRs) vs. wind speed for rainfall events (a, b)
and daily rainfall (c, d) greater than 3.0 mm.
Figure 5 presents scatter plots for the CRUN/DFIR and CRSA/DFIR
vs. wind speed for rainfall. The CRs vary from 80 to 110 %. With
increasing wind speed, the CRs decrease slightly. Only Eq. (10) shown in
Fig. 5 and Table 4 could be used to adjust the rainfall event data from the
CSPGSA. It is significant at 0.03 level (Table 4). As described in
Sect. 2.2, Eq.(10) was fitted using the NONLINEAR function in SPSS
software (Analyze\Regression\Nonlinear). The F value
was then calculated using regression and the residual sum of squares from
SPSS (Snedecor and Cochran, 1989). Based on the F value and the degrees of
freedom (Df), the significance level (α) was obtained using the
FDIST function in Microsoft Excel. Other forms such as the exponential
expression were treated in a similar way.
CRSA/DFIR,Rain=0.188Ws103-0.719Ws102+0.551Ws10+1000<Ws10<7.4,
where CRSA/DFIR,Rain is the rainfall catch ratio (%) per observation
of the CSPGSA and Ws10 is the wind speed at 10m during the rainfall
period (m s-1).
On the daily scale, the relationships between rainfall CR and wind speed at
gauge height (Ws0.7) are also cubic functions, but they do not pass the
F test with α = 0.25 (Table 4).
Catch ratio (CR) vs. wind speed relationships at the Hulu
watershed intercomparison site, 2012–2015.
Temporal
Phase
Gauges
Catch ratio (CR) vs. wind speed relationships*
Precipitation
No. of
F test
scale
(mm)
events
Precipitation
Rain
CSPGUN
CRUN/DFIR,Rain = 0.181Ws103 - 0.256Ws102 - 0.795 Ws10 + 100
> 3.0
103
α = 0.23
event
R2 = 0.042
CSPGSA
CRSA/DFIR,Rain = 0.188Ws103 - 0.719Ws102 + 0.551Ws10 + 100
α = 0.03
R2 = 0.083
CSPGPIT
CRPIT/DFIR,Rain = 0.150Ws103 - 0.425Ws102 + 1.119Ws10 + 100
α = 0.83
R2 = 0.008
Mixed
CSPGUN
CRUN/DFIR,Mixed = 100e-0.06Ws10 R2 = 0.194
> 1.0
24
α = 0.07
CSPGSA
CRSA/DFIR,Mixed = 100e-0.04Ws10 R2 = 0.100
α = 0.16
CSPGPIT
CRPIT/DFIR,Mixed = 100e-7E-0Ws10 R2 = 0.000
α = no data
Snow
CSPGUN
CRUN/DFIR,Snow = 100e-0.08Ws10 R2 = 0.412
> 1.0
34
α = 6.4 × 10-5
CSPGSA
CRSA/DFIR,Snow = 100Ws10-0.02 R2 = 0.090
α = 0.07
CSPGPIT
CRPIT/DFIR,Snow = 100e-0.01Ws10
α = 0.35
R2 = 0.024
Daily
Rain
CSPGUN
CRUN/DFIR,Rain = -1.400Ws0.73 + 2.987Ws0.72 - 6.116Ws0.7 + 100
> 3.0
90
α = 0.37
precipitation
R2 = 0.032
CSPGSA
CRSA/DFIR,Rain = -0.924Ws0.73 + 1.158Ws0.72 - 3.338Ws0.7 + 100
α = 0.55
R2 = 0.021
CSPGPIT
CRPIT/DFIR,Rain = -0.952Ws0.73 - 1.503Ws0.72 + 2.237Ws0.7 + 100
α = no data
R2 = -0.00
Mixed
CSPGUN
CRUN/DFIR,Mixed = 100e-0.12Ws0.7 R2 = 0.144
> 1.0
21
α = 0.09
CSPGSA
CRSA/DFIR,Mixed = 100e-0.07Ws0.7 R2 = 0.094
α = 0.18
CSPGPIT
CRPIT/DFIR,Mixed = 100e-0.001Ws0.7 R2 = 0.003
α = no data
Snow
CSPGUN
CRUN/DFIR,Snow = 100e-0.11Ws0.7 R2 = 0.477
> 1.0
27
α = 1.8 × 10-4
CSPGSA
CRSA/DFIR,Snow = 100e-0.03Ws0.7 R2 = 0.087
α = 0.14
CSPGPIT
CRPIT/DFIR,Snow = 100e-0.01Ws0.7
α = no data
R2 = -0.00
* Ws10 – wind speed during period of precipitation at 10 m height;
Ws0.7 – daily mean wind speed at gauge height (0.7 m for CSPG).
Catch ratios (CRs) vs. wind speed for mixed precipitation
events (a, b) and daily mixed precipitation (c, d) greater than 1.0 mm.
For the mixed precipitation events, the CR vs. Ws10 relationships
are exponential (Table 4, Fig. 6). The CRs vary greatly from about 60 to
120 %. For the CSPGUN, the exponential relationship Eq. (11) passes
the F test with α = 0.07, whereas for the CSPGSA, the Eq. (12)
α value is about 0.16 (Table 4).
CRUN/DFIR,Mixed=100e-0.06Ws100<Ws10<5.9CRSA/DFIR,Mixed=100e-0.04Ws100<Ws10<5.9
On the daily scale, the relationships between mixed precipitation CR and
wind speed at gauge height (Ws0.7) are also exponential expressions
(Table 4, Fig. 6). Similarly, for the CSPGUN, Eq. (13) passes the F test
with α < 0.10, whereas Eq. (14) with an α value of
about 0.18 does not (Table 4).
CRUN/DFIR,Mixed=100e-0.12Ws0.70<Ws0.7<2.9CRSA/DFIR,Mixed=100e-0.07Ws0.70<Ws0.7<2.9
From Eq. (3), air temperature may also affect the mixed precipitation CRs on
the daily scale. Equations (15)–(16) are obtained as follows. However, these two
new equations do not pass the F test with α < 0.20.
CRUN/DFIR,Mixed=13.83Ws0.7-4.91+1.25Tmax-0.88Tmin+62.21α=0.20
CRSA/DFIR,Mixed=10.74Ws0.7-4.74+0.85Tmax-0.18Tmin+76.20α=0.29,
where Tmax and Tmin are the daily maximum and minimum air
temperature (∘C), respectively.
Catch ratios (CRs) vs. wind speed for the snowfall event (a, b)
and the daily (c, d) snowfall greater than 1.0 mm.
For the snowfall events, the CRUN/DFIR,Snow and CRSA/DFIR,Snow
vs. Ws10 relationships are significant (Table 4, Fig. 7). For the
CSPGUN, the exponential relationship Eq. (17) passes the F test with
α < 0.001. Equation (17) is similar to Eq. (5) suggested by Yang et
al. (1991). For the CSPGSA, its exponential expression in Eq. (18)
passes the F test at α = 0.07 (Table 4).
CRUN/DFIR,Snow=100e-0.08Ws100<Ws10<4.8CRSA/DFIR,Snow=100e-0.02Ws100<Ws10<4.8
On the daily scale, the relationships between snowfall CRs and wind speed at
gauge height (Ws0.7) are also exponential expressions (Table 4, Fig. 7).
For the CSPGUN and CSPGSA, the Eqs. (19)–(20) pass the F test with
α < 0.001 and α = 0.14, respectively (Table 4).
Equations (17)–(19) could therefore be directly used to calibrate the
wind-induced snowfall measurement errors for the CSPGUN and CSPGSA.
CRUN/DFIR,Snow=100e-0.11Ws0.70<Ws0.7<3.1CRSA/DFIR,Snow=100e-0.03Ws0.70<Ws0.7<3.1
Air temperature may also affect the snowfall CR on the daily scale as shown
in Eq. (2). Equations (21)–(22) are the new equations associated with daily
maximum air temperature. However, these two new equations are not better
than Eqs. (19)–(20) according to their F test α values.
CRUN/DFIR,Snow=42.29Ws0.7-1.06-1.06Tmax+55.91α=4.2×10-5
CRSA/DFIR,Snow=-9.46lnWs0.7-0.31Tmax+98.76α=0.17
From the above mentioned relationships of CRUN/DFIR and CRSA/DFIR
vs. wind speed, the following points can be drawn for our understanding. For
daily rain and mixed precipitation, the relationships are not statistically
significant. The use of daily mean wind speed may lead to uncertainties in
gauge comparisons. Data collections and analyses on shorter timescales,
such as hourly or 6-hourly, are expected to produce more reliable results,
because wind speed may vary throughout the day and daily mean wind speeds
may not be representative of the wind conditions over the precipitation
period (Yang and Simonenko, 2014). Daily maximum and minimum temperatures
should reflect the atmospheric conditions of radiation and convection to
some degree, and their function in the CR vs. wind speed relationship needs
further investigation in a mountain environment.
Seasonal snowfall and its percentage from September 2010 to
April 2015 at the Hulu watershed site.
Discussion
Comparison with other studies
Yang et al. (1991) carried out a precipitation intercomparison experiment
from 1985 to 1987 at the Tianshan site. Their results indicated that the
CSPGDFIR/CSPGUN ratios for snowfall and mixed precipitation
were 1.222 and 1.160, respectively. In the Hulu watershed, these ratios were 1.165
(Fig. 4c) and 1.072 (Fig. 3c), while those for CSPGPIT/CSPGUN
were 1.162 (Fig. 4b) and 1.082 (Fig. 3b), respectively. Similar topographic
features and shading induced similar lower wind speeds and led to similar
catch ratios at both sites. For the Tianshan study site, wind speed
(Ws10) on rainfall or snowfall days never exceeded 6 m s-1,
and 88 % of the total annual precipitation took place with wind speeds
below 3 m s-1. At the Hulu watershed site, daily mean wind speeds
(Ws10) on precipitation days never exceeded 6.4 m s-1, and
over 55.2 % of the precipitation events occurred with daily mean wind
speeds below 3 m s-1. During the periods of precipitation, the highest
wind speed at 10 m height was about 8.8 m s-1, and over 54.2 % of
the precipitation events occurred with wind speeds below 3 m s-1.
(a) Annual snowfall (mm) and (b) ratio of annual
snowfall to total precipitation in China.
As Ren et al. (2003) reported, across 30 comparison stations in China, the
CSPGPIT caught 3.2 % (1.1–7.9 %) more rainfall and
11.0 % (2.2–24.8 %) more snowfall than the CSPGUN.
Large wind-induced differences were often observed at the mountainous
western stations and in north-eastern China. At the Gangcha station
(100∘08′ E, 37∘20′ N; 3015 m), which
also lies in the Qilian Mountains at a similar elevation about 200 km from
the Hulu watershed site, the CSPGPIT caught 7.9 % more rainfall and
16.8 % more snowfall than the CSPGUN from 1992 to 1998. In our study,
the CSPGPIT captured 4.7 % more rainfall, 21.0 % more snowfall and
12.1 % more mixed precipitation than the CSPGUN from September 2010
to April 2015 (Table 3). The outcome presented in this study is somewhat
different from that reported by Ren et al. (2003) due to differences in the
wind regime. At the Gangcha station, daily mean wind speeds
(Ws10) on precipitation days during the experimental period from
1992 to 1998 never exceeded 8.5 m s-1, and over 35.1 % of the
precipitation events occurred with daily mean wind speeds below 3 m s-1.
The average daily mean Ws10 was about 3.4 m s-1 on
precipitation days from 1992 to 1998 at the Gangcha station, whereas at the
Hulu watershed site from 2010 to 2015, the average value was about 2.9 m s-1
on precipitation days.
It is recognised that in western China, climatic and environmental
conditions in the mountains vary both spatially and temporally. To
understand the similarities and differences in wind-induced bias in
precipitation measurements for different mountain watersheds, field
experiments need to be carried out continuously. Further investigation is
also necessary to consider the influence of micrometeorology on gauge
observations, particularly wind distribution and turbulence across this site
(Yang and Simonenko, 2014).
Applicable regions in China for the CSPGPIT and
CSPGDFIR as reference gauges.
Surface roughness during the precipitation period from
September 2012 to April 2015.
CSPGPIT as a reference for solid precipitation
The pit is the WMO reference configuration for liquid precipitation
measurements and the DFIR is the reference configuration for solid
precipitation measurements (Sevruk et al., 2009). In this study, the
CSPGPIT measured more rainfall and mixed precipitation than the
CSPGDFIR. For snowfall, the catch ratio for CSPGPIT was 0.96,
close to that of the CSPGDFIR measurement. The difference in total
snowfall (43 events) between the CSPGPIT and CSPGDFIR was only
about 3.4 mm from September 2012 to April 2015 at the Hulu watershed site.
The snowfall for autumn and spring was greater than for winter during the
observation period at the intercomparison site (Fig. 8). The snowfall is
wetter in autumn and spring than in winter, and wetter snowfall means less
blowing or drifting snow. Thus the CSPGPIT could serve as a reference
for liquid and solid precipitation in environments similar to that of the
Hulu watershed site. Precipitation collected by the CSPGPIT would be
most affected by blowing or drifting snow, inducing a faulty precipitation
value (Goodison et al., 1998; Ren and Li, 2007). Previous studies have
indicated, however, that for most of China, the maximum snow depth in the
past 30 years has been less than 20 cm (Li, 1999), with average snow depths
below 3 cm (Li et al., 2008; Che et al., 2008). Figure 9 shows annual snowfall
amounts and annual snowfall proportion distributions for 644 meteorological
stations in China from 1960 to 1979, indicating that snowfall is
concentrated in the middle and south-western Tibetan Plateau, northern
Xinjiang province and north-eastern China. Statistical analysis indicates
that for more than 94 % of stations, solid precipitation comprises less
than 15 % of the annual precipitation. Ren et al. (2003) reported, that
among the 2286 snowfall events, only 54 were blowing or drifting snow events,
accounting for about 2.4 % for 26 stations across China. Based on the
regionalisation of snow drift in China, blowing or drifting snow events
occur mostly on the central and south-western Tibetan Plateau, in the
northern Xinjiang province and in north-eastern China (Wang and Zhang,
1999). In these regions, the CSPGDFIR should be used as a reference
gauge. In other regions, the CSPGPIT may be applicable. Based on the
CMA snowfall and snow depth data, and the regionalisation of snow drift in
China, the applicable regions for the CSPGPIT and CSPGDFIR as
reference gauges are shown in Fig. 10.
Limitations of this experiment
Although the measurement procedures were based on the CMA's standard, manual
observations were infrequent, and as a result, some precipitation events
were summarised as single events, especially in the evenings. The automatic
meteorological tower could observe precipitation and wind speeds half-hourly
during the precipitation period, but the CSPGUN, CSPGSA,
CSPGPIT and CSPGDFIR were observed only twice per day. In this
field experiment, the precipitation phases were also distinguished by
observers. This method is somewhat imprecise although this has remained the
traditional method since the 1950s at the CMA stations (CMA, 2007b).
Automatic sensors will also be important to detect precipitation types at
operational and research networks (Yang and Simonenko, 2014).
The wind speeds at gauge height and at 10 m height were not observed
directly but rather calculated from the observed data at 1.5 and 2.5 m
heights according to the Monin–Obukhov theory and the gradient method
(Eq. 8). Although this method is widely used, it is effective only under
neutral atmospheric conditions. For the precipitation period from
September 2012 to April 2015, the Z0 was calculated using Eq. (9). The
results showed the Z0 to be about 0.06 m on average but it varied from
nearly zero to 0.67 m. As shown in Fig. 11, in about 68.9 and 95.1 % of
instances, the Z0 was lower than 0.05 and 0.25 m, respectively. In rare
cases when the Z0 was very large, as shown in Fig. 11, the Z0 was
arbitrarily assigned 1/2 of the grass height (h) at the site based on the
equation Z0 = 0.5hLe provided by Lettau (1969). The very
large Z0 values usually appeared in late August and early September when
the vegetation coverage (Le) was close to 100 % at the Hulu watershed
site.
Conclusions
This study focused on wind-induced bias in precipitation
measurements by CSPGs specifically in a high mountain environment. The
precipitation intercomparison experiment in the Hulu watershed of the Qilian
Mountains indicated that the CSPGPIT caught more rainfall, mixed
precipitation and total precipitation but less snowfall than the
CSPGDFIR. From most to least rainfall and mixed precipitation
measured, their ranking was
CSPGPIT > CSPGDFIR > CSPGSA > CSPGUN,
whereas in the snowy season, better wind shielding increased the snow catch,
leading to
CSPGDFIR > CSPGPIT > CSPGSA > CSPGUN.
The measured daily precipitation by shielded gauges increases linearly with
that of unshielded gauges. For solid precipitation, some non-linear factors
interfere with the linear relationship to reduce the linear correlation
coefficient.
In regions with lower snowfall, such as the southern and central parts of
China (Zhang and Zhong, 2014), and in regions with a similar climate and
environment to that of the Hulu watershed site, the CSPGPIT could
be used as a reference gauge because of its high catch ratio, simplicity and
lower maintenance requirements. In north-eastern China, northern Xinjiang
province and the central and south-western Tibetan Plateau where snowfall
often occurs, the best choice of reference gauge would be the
CSPGPIT for rainfall and the CSPGDFIR for snowfall
observations.
The catch ratio vs. wind speed relationship for different precipitation types
is quantified by cubic polynomials and exponential functions. The
CRPIT/DFIR does not have a significant relationship to wind
speed, indicating that both PIT and DFIR are effective in preventing wind
from influencing the precipitation gauge catch. For daily rain and mixed
precipitation, the relationships are not statistically significant. Daily
maximum and minimum temperatures should reflect the atmospheric conditions of
radiation and convection to some degree, and their function in the CR
vs. wind speed relationship needs further investigation in mountain
environments. It is recognised that in western China, the climatic and
environmental conditions in the mountains vary both spatially and temporally.
To understand the similarities and differences among wind-induced biases in
precipitation measurements for the different mountain watersheds in western
China, field experiments and modelling of wind fields need to be carried out
continuously.