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Application of a snow particle counter to solid precipitation measurements under Arctic conditions
Cold Regions Science and Technology 58 (2009) 77–83
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Cold Regions Science and Technology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c o l d r e g i o n s
Application of a snow particle counter to solid precipitation measurements under Arctic conditions Konosuke Sugiura a,⁎, Tetsuo Ohata a, Daqing Yang b, Takeshi Sato c, Atsushi Sato d a
Institute of Observational Research for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka 237-0061, Japan Water and Environmental Research Center, University of Alaska, Fairbanks 99775-5860, USA National Research Institute for Earth Science and Disaster Prevention, Shinjo 996-0091, Japan d National Research Institute for Earth Science and Disaster Prevention, Nagaoka 940-0821, Japan b c
a r t i c l e
i n f o
Article history: Received 29 May 2008 Accepted 19 March 2009 Keywords: Solid precipitation Snow particle counter Double-Fence Intercomparison Reference Arctic
a b s t r a c t To develop a more reliable means for measuring precipitation in the Arctic, daily solid precipitation in Barrow, Alaska, was measured from October 24, 2002 to January 14, 2003 using a Double-Fence Intercomparison Reference (DFIR), which is the World Meteorological Organization (WMO) reference standard for solid precipitation measurements. Furthermore, a new approach using a snow particle counter (SPC) that outputs the number flux of snow particles without directly catching them was introduced. The correction procedures for wind, wetting, evaporation losses, and trace amounts were applied on a daily basis to the DFIR precipitation. The total corrected DFIR precipitation (61.35 mm) was found to be 1.47 times the uncorrected measurement (41.65 mm). The SPC-estimated precipitation measurements correlate well (r2 = 0.72) with the corrected DFIR measurements, suggesting that precipitation gauges that do not directly catch snow particles are effective in low-precipitation areas such as the polar regions. © 2009 Elsevier B.V. All rights reserved.
1. Introduction Solid precipitation is an essential part of the hydrological cycle over cold regions and seasons. It is generally recognized that the measurement via gauge of solid precipitation has systematic errors, mainly caused by wind-induced gauge undercatch. It is acknowledged that a precipitation gauge placed in a natural bush shelter, i.e., a bush gauge, would provide the best estimate of “ground true” precipitation, and is therefore considered a primary standard (Goodison et al., 1998). The bush gauge is encircled by bushes that are regularly trimmed to the level of the gauge orifice. However, this gauge cannot be used in all climatic regions. Therefore, Golubev (1989) discussed the need to adjust a Double-Fence Intercomparison Reference (DFIR) measurement to the “true” value of the bush gauge to reduce wind effects. The DFIR was selected as the World Meteorological Organization (WMO) reference standard for solid precipitation measurements during the WMO Solid Precipitation Measurement Intercomparison (Goodison et al., 1998). Although general correction procedures and reference measurements for precipitation have been developed for the Arctic
⁎ Corresponding author. Fax: +81 46 867 9255. E-mail address: [email protected] (K. Sugiura). 0165-232X/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.coldregions.2009.03.010
regions (Mekis and Hogg, 1999; Yang, 1999; Yang et al., 1998, 1999; Yang and Ohata, 2001; Bagdanova et al., 2002; Sugiura et al., 2003; Yang et al., 2005; Sugiura et al., 2006), further precipitation experiments based on WMO precipitation procedures are necessary to test and assess various types of precipitation gauges in Arctic regions, where the wind is strong and blowing snow occurs frequently. Struzer (1971) and Goodison et al. (1998) have reported the catch efficiency of precipitation gauges under strong wind conditions. Sugiura et al. (2003) suggested that, in order to counter the systematic errors in gauge-measured precipitation for Arctic conditions, we must consider not only wind-induced undercatch, wetting, and evaporation losses, but also the influence of blowing snow and trace precipitation. In this study, we analyzed solid precipitation using independent methods at a test site in the Arctic—Barrow, Alaska. First, we measured the precipitation using a DFIR; then we introduced a new approach for estimating solid precipitation using a snow particle counter (SPC) with an optical sensor. This SPC outputs the number flux of horizontally transported snow particles without directly catching them, and detects each particle. In this paper, we present meteorological data obtained from a DFIR and an automatic weather system (AWS) installed in Barrow, Alaska, and the correction methods applied to these data. Based on a comparison between the daily amounts of the corrected DFIR and SPC-estimated precipitation measurements, we propose using an SPC to measure solid precipitation in Arctic
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Fig. 1. Double-Fence Intercomparison Reference.
conditions. Our goal is to develop a more reliable means for solid precipitation observation in the Arctic. 2. Methodology 2.1. Study site, period, and instruments The observation site in Barrow, Alaska, is located at the northernmost point of the United States and is entirely covered by snow during winter. The precipitation climatology of Barrow, based on data obtained from the National Climatic Data Center, shows that monthly mean precipitation from 1971 to 2000 in October, November, December, and January is 10, 4, 3, and 3 mm, respectively. This indicates that the precipitation at Barrow during winter is low. In this study, continuous precipitation and blowing snow observations were conducted for three months, from October 24, 2002 to January 14, 2003. We installed a DFIR near the Barrow Observatory (71°19′N,156°36′W) of the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division, as shown in Fig. 1. The DFIR consists of a shielded Russian Tretyakov gauge encircled by two octagonal lath fences. The heights of the outer and inner fences above the ground surface are 3.5 m and 3 m, respectively, and the diameters of the outer and inner fences are 12 m and 4 m, respectively. The length and density of the lath fences are 1.5 m and 50%, respectively. The Russian Tretyakov gauge (a cylindrical bucket with a wind shield) is designed to collect precipitation (snow, mixed precipitation, and rain) throughout the year. The orifice area of the bucket is 0.02 m2. The amount of precipitation in the bucket is measured manually using a graduated cylinder. The minimum amount of precipitation using the graduated cylinder is 0.10 mm, and the reading on the amount of precipitation is at a resolution of 0.05 mm. Several researchers have attempted to measure blowing snow flux using wide trenches, a box-type drift gauge, a cyclone-type drift collector, and a net-type drift collector (Kimura, 1991). These devices must catch snow particles directly and also estimate the catch efficiency. Recently, new instruments using an optical sensor without directly catching snow particles have been developed by Schmidt (1977), Gubler (1981), Brown and Pomeroy (1989), Sato and Kimura (1991), and Sato et al. (1993). In the present study, one of these newly developed instruments, an SPC, was introduced to estimate solid precipitation. Fig. 2 shows the SPC (SPC-S7, Niigata Electric; e.g., Sugiura et al., 1998), which has a self-steering wind vane and is equipped with a
superluminescent diode sensor. Based on the electrical pulse signals that correspond to the sizes of snow particles passing through a sampling area, each signal is classified into one of 32 classes between 0.05 and 0.5 mm. Furthermore, the SPC measures the number flux of the snow particles based on each particle's diameter and classifies them into one of 32 classes from 0.037 to 0.667 mm, including the edge effect of the sensor (Sato, 1987). If the diameter of a snow particle is larger than that of the maximum diameter class, that is, N0.667 mm, this diameter is also considered to belong to the maximum diameter class. The sampling area of the SPC is small— 50 mm2 (2 mm in height and 25 mm in width)—which is 1/400 that of the DFIR. The SPC sensor was installed on the tower of the Barrow Observatory of the NOAA/ESRL Global Monitoring Division near the DFIR at a height of 3 m, which is equal to that of the DFIR. The validity of the SPC system in blowing snow has been confirmed by field observations in Japan (e.g., Sato and Kimura, 1991; Sato et al., 1993) and wind tunnel experiments (Sugiura et al., 1998). Sato (1991) showed the potential of an SPC with a double-slit type sensor as an instrument for snowfall intensity measurements based on a comparison between snowfall intensity measurements using an electronic balance and the number of snow particles per sampling area per minute obtained from an SPC. Because the SPC sensor used in the Sato study is a double-slit type, such as that developed by Schmidt (1977), it can also determine particle speed, and the fall velocity of different particle shapes is reported. Although the SPC used in this study does not have this feature, Sugiura and Ohata (2004) showed in preliminary data that the SPC is able to measure particle speed using the time-interval difference between the beginning and end of
Fig. 2. Snow particle counter.
K. Sugiura et al. / Cold Regions Science and Technology 58 (2009) 77–83
an output waveform by an electrical pulse signal of a snow particle passing through a sampling area. However, this technique was not used in this study. Several meteorological sensors were installed at the site. The wind speed, air temperature, and relative humidity at a height of 3 m above the ground, and surface air pressure, were measured at 10-minute intervals from noon on October 24, 2002 to noon on January 6, 2003. Although data are available for blowing snow observations and precipitation up to 13 and 14 January 2003, respectively, several analyses in this study were conducted using data up to January 6, 2003, as the meteorological sensors were undergoing maintenance on and after January 7, 2003. In this study, each day's precipitation is calculated as the sum of the precipitation from noon the previous day to noon that day. 2.2. DFIR measurements Allerup et al. (1997) presented a comprehensive model for correcting point precipitation; the general model for correcting systematic errors in precipitation measurements is X ΔPim Pc = k Pm +
ð1Þ
where Pc is the corrected or “true” amount of precipitation; Pm, the measured amount; ΣΔPim, the sum of various error sources; and k, the correction factor for wind effects. Based on manual precipitation observations conducted in Barrow, Alaska, Sugiura et al. (2003) presented ΣΔPim as the sum of the error sources: X
ΔPim = Pwetting + Pevaporation + Pblowing + Ptrace
ð2Þ
where Pwetting and Pevaporation are the wetting and evaporation losses of the gauge, respectively, Pblowing is the false precipitation due to blowing snow (Pblowing is a negative quantity), and Ptrace is the trace amount. The WMO Solid Precipitation Measurement Intercomparison (Goodison et al., 1998) recommended that the adjustment equations of the DFIR for snow (Eq. (3)) and mixed precipitation should be used in future study. Furthermore, it also suggested that no adjustment was necessary for rain measurements performed with the DFIR. In the case of snow, the DFIR correction factor to counter the wind effects, kDFIR, is given as kDFIR =
2 PBUSH 100 + 0:439UDFIR + 0:246UDFIR = PDFIR 100
ð3Þ
where PBUSH is the precipitation measured using the bush gauge [mm]; PDFIR is the DFIR-measured precipitation [mm]; and UDFIR is the wind speed [m s− 1] at the DFIR's height (3 m). The DFIR correction factor (Eq. (3)) is a regression equation fit to 183 data points with a determination coefficient of 0.151 (Goodison et al., 1998). This low correlation suggests that there may be significant error in the corrected DFIR measurements. If the daily measured precipitation was less than 0.1 mm, we recorded it as trace precipitation. Thus, the maximum daily trace precipitation is less than 0.1 mm. Sugiura et al. (2003) adopted the daily trace precipitation, Ptrace, as a quarter of the scale increment on a graduated cylinder, i.e., 0.025 mm, based on winter precipitation observations in Barrow. In this study, a trace precipitation of 0.025 mm was assigned for each day that trace precipitation was recorded. A wetting loss experiment using the Russian Tretyakov gauge was conducted before the analysis. The wetting loss of the gauge, Pwetting [mm] per observation, was estimated from experiments on the difference between the weight of the gauge when completely dry and its weight when wet. Since the conversion of kilograms to meters of water depth can be performed by considering that 1000 kg of water
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is equivalent to 1 m3, the wetting loss can be experimentally expressed as Pwetting = 1000
W Aρwater
ð4Þ
where W is the difference between the weights of the completely dry and wet gauges [kg]; A is the gauge orifice area [m2]; and ρwater is the density of water [kg m− 3]. For a completely wet gauge, on average an estimated maximum wetting loss value of 0.35 mm was obtained per experiment. Since the maximum wetting loss value is 0.35 mm, the average wetting loss per observation would typically be less than 0.35 mm. A wetting loss of 0.1 mm, which is less than half of 0.35 mm, is a conservative estimate. Therefore, in this study, a daily wetting loss value of 0.1 mm was applied as a correction per precipitation day, and no correction for wetting loss was applied for the trace precipitation days. On the basis of evaporation experiments conducted using the Tretyakov gauge at Jokioinen, Finland, Aaltonen et al. (1993) reported that mean daily evaporation loss during winter was much less than that during the rainy season, ranging from 0.1 to 0.2 mm. A daily evaporation loss value Pevaporation of 0.1 mm was adopted as a correction per precipitation day in the present study. No correction for evaporation loss was applied for the trace precipitation days. It is important to consider that both the DFIR and the SPC detect snow particles precipitated from clouds and those remobilized from the snow surface in windy conditions. The DFIR catches some, but not all, of the snow particles in windy conditions, with the missed fraction increasing with wind speed, i.e., wind-induced gauge undercatch. On the other hand, since the SPC detects snow particles without directly catching them, there is no need to estimate the catch efficiency. In this study, no adjustments were made for false precipitation due to blowing snow, Pblowing, in Eq. (2). The correction factor CF is the ratio of the amount of corrected DFIR precipitation, Pc_DFIR, to that of DFIR-measured precipitation, Pm_DFIR; it can be calculated as CF =
P Pc DFIR ΔPim DFIR = kDFIR 1 + Pm DFIR Pm DFIR
ð5Þ
where ∑ΔPim_DFIR is the sum of various error sources for DFIR. It is noteworthy that the daily correction factor has an inherent tendency to decrease with decreasing wind speed. 2.3. SPC measurements Since the number flux data in the minimum-diameter class included noise, and all classes included indistinct and instantaneous periodic noise at intervals of approximately 12 s for the entire observation period at Barrow, Alaska, the minimum-diameter data were excluded from the following analyses, and the number flux data for 6 s h− 1 were used to avoid subjective selection of the data. The procedures to calculate the SPC-estimated precipitation, PSPC, are as follows: 1) The horizontal number flux [m− 2 s− 1] measured using the SPC was converted into horizontal mass flux, Fh_SPC [kg m− 2 s− 1], as Fh
SPC
=
32 πρparticle X 3 Sd Nd Dd 6 d=1
ð6Þ
where ρparticle is the density of the snow particles, which is assumed to be that of ice, i.e., 917 kg m− 3; Nd is the number flux of the d-th class [m− 2 s− 1]; Dd is the diameter of the snow particles of the d-th class [m]; and Sd is the shape factor of snow particles of the d-th class, which is the ratio of a spherical cubic
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Table 1 Summary of the precipitation collected from October 24, 2002 to January 14, 2003 in Barrow, Alaska. Source of the precipitation data
No-observation days
No-precipitation days
Trace days (Precip b 0.1 mm)
Precipitation days including trace days
Type of precipitation
DFIR SPC
0 8
3 0
24 12a
80 75
Snow Snow
a
The SPC data has theoretically no trace days.
volume to the snow particle cubic volume and is assumed to be 1 in this study. 2) The horizontal mass flux, Fh_SPC, was converted into snow particle concentration, CSPC [kg m− 3], according to the horizontal speed of the snow particles as CSPC =
Fh SPC Uhorizontal
ð7Þ
where Uhorizontal is the horizontal speed of the snow particles [m s− 1], which is assumed to be the horizontal wind speed at the SPC's height (3 m) in this study, based on the result of Schmidt (1982). 3) The snow particle concentration, CSPC, was converted into vertical mass flux, Fv_SPC, according to the vertical speed of the snow particles, Vvertical, as Fv
SPC
= Vvertical CSPC :
ð8Þ
4) Then, the vertical mass flux, Fv_SPC, was converted into SPCestimated precipitation, PSPC, according to the density of water, ρwater, as
PSPC
F = v SPC = ρwater
πVvertical ρparticle
32 P
Sd Nd D3d
d=1
6Uhorizontal ρwater
:
ð9Þ
Several studies have been conducted on the speed of snow particles. Takahashi (1985) observed the vertical speed of snow particles that were guided into a closed dark box through a perpendicular shaft 2.5 m in length at the Mizuho Station, Antarctica; the vertical speed was between 0.3 and 0.9 m s− 1 and increased with the wind speed, which ranged from 5 to 13 m s− 1. Kajikawa (1975) measured the vertical speed of plane type, spatial type, and rimed snow crystals that were allowed to fall at terminal velocity through a tube 0.78 m in length over a range of particle diameters from 0.1 to 5 mm at Mt. Teine, Japan, using stroboscopic photographs; the vertical speed was distributed in a wide range between 0.1 and 1.3 m s− 1 and increased with particle diameter. Since the size of snow particles
Fig. 3. Histograms of daily mean diameter of snow particles from October 25, 2002 to January 13, 2003 using the SPC.
measured by us is small, as stated below, in this study the vertical speed of snow particles, Vvertical, is assumed to be 0.25 m s− 1, and PSPC is calculated using Eq. (9). 3. Results and discussion Table 1 summarizes the precipitation recorded from October 24, 2002 to January 14, 2003 (83 days). The type of precipitation for the entire observation period was snow. On a few days, no precipitation was observed. The number of trace precipitation days, as defined using the DFIR measurements (i.e., less than 0.1 mm), was 24. However, theoretically, the number of trace precipitation days using the SPC measurements cannot be determined, because the SPC can measure precipitation with a precision of one snow particle. As shown in Fig. 3, the daily mean diameter of snow particles using the SPC ranges from 0.04 mm to 0.48 mm with an average of 0.15 mm. Regarding the characteristic properties of precipitation particles in the Arctic, Kikuchi et al. (1982) observed mainly dendritic crystals in the warm air from the Pacific Ocean, and bullets, columns, and crossed plates in polar air mass, with the equivalent diameters ranging from 0.12 mm to 0.27 mm. Kajikawa et al. (1983) also observed snow crystals in Arctic Canada, and the diameter ranged from 0.1 mm to 0.3 mm. In comparison with the middle-latitude area, snow particles were relatively small. These typical snow particles are measurable by the SPC, which has a measurable range of 0.037–0.667 mm. The corrected DFIR and SPC-estimated precipitation amounts are listed in Table 2. The total corrected DFIR precipitation was 1.47 times the uncorrected measurement, that is, 61.35 and 41.65 mm, respectively. Fig. 4 shows sums of the daily measured and corrected DFIR precipitation amounts. The biweekly correction for trace amounts is small (0.00, 0.01, 0.03, 0.01, 0.02, and 0.00 times the uncorrected measurement for the respective periods of October 24–31, November 1–15, November 16–30, December 1–15, December 16–31, and January 1–14); however, the corrections for wetting (0.09, 0.18, 0.16, 0.12, 0.18, and 0.10 times, respectively), evaporation (0.09, 0.18, 0.16, 0.12, 0.18, and 0.10 times, respectively), and wind-induced loss (0.23, 0.14, 0.24, 0.24, 0.17, and 0.11 times, respectively) increased the total amount of corrected precipitation, especially the correction for windinduced loss. The sum of the corrected DFIR precipitation is 1.41, 1.51, 1.59, 1.49, 1.55, and 1.33 times the uncorrected measurement for the respective periods of October 24–31, November 1–15, November 16– 30, December 1–15, December 16–31, and January 1–14. Fig. 5 shows the daily correction factor, CF, which is the ratio of the amount of daily-corrected DFIR precipitation to that of daily DFIR-measured precipitation (Eq. (5)). The correction factor is often high (N2), suggesting that there may be significant errors in the resulting corrected DFIR measurements. Although the maximum correction factor during early winter is 3.96, the daily correction factor decreases gradually in the middle of winter. Fig. 6 shows the relationship between the daily-corrected DFIR and SPC-estimated precipitation values. On October 30, 2002, daily SPC-estimated precipitation was notably low: 3.5 mm using the SPC compared with 8.1 mm using the DFIR, as shown in Table 2. On that day, the diameter of several snow particles was over 0.667 mm. The SPC underestimates particles with a diameter of over 0.667 mm and counts particles larger than 0.667 mm as having a diameter equal to 0.667 mm. The percentage of the number of particles with a diameter of 0.667 mm was 2.8%, corresponding to a daily SPC-estimated
K. Sugiura et al. / Cold Regions Science and Technology 58 (2009) 77–83 Table 2 Bias corrections of the daily DFIR-measured and SPC-estimated precipitation measurements from October 24, 2002 to January 14, 2003 in Barrow, Alaska. Date
Pm_DFIR (mm)
U3m (m s− 1)
Pc_DFIR (mm)
PSPC (mm)
Oct 24, 2002 25 26 27 28 29 30 31 Nov 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Dec 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Jan 1, 2003 2 3 4 5 6
0.70 0.30 0.30 0.20 0.70 0.20 6.20 0.65 0.85 – T 0.35 T – 0.10 0.30 0.20 0.30 0.10 0.60 2.50 0.25 T 0.70 2.50 0.30 0.20 0.15 0.10 T T 0.50 1.00 T T T T 0.20 1.70 1.00 T T T T 1.20 0.60 0.10 0.10 0.30 1.30 1.65 0.70 0.75 1.20 0.35 T 0.20 0.40 1.05 0.25 0.15 0.50 0.45 T T T T 0.40 1.10 0.50 0.35 T 2.40 1.45 0.80
8.27 2.02 3.02 2.69 2.91 8.42 9.78 4.07 4.06 6.19 3.57 4.77 12.15 14.69 10.55 6.76 3.75 5.07 4.30 5.29 5.78 5.07 3.92 4.17 10.14 5.36 3.99 6.10 7.97 8.38 9.36 9.99 3.01 2.77 8.48 6.28 6.37 7.40 9.47 10.56 2.89 5.42 6.84 2.44 3.97 1.04 1.69 1.90 2.19 7.24 10.72 8.67 9.14 7.70 4.27 1.49 1.45 8.24 7.86 7.21 8.29 4.27 2.80 4.73 4.11 0.99 1.30 3.12 5.31 3.63 3.07 1.28 6.50 6.27 3.23
1.08 0.51 0.52 0.41 0.93 0.48 8.18 0.90 1.11 – 0.03 0.59 0.04 – 0.40 0.57 0.42 0.54 0.32 0.87 2.99 0.49 0.03 0.96 3.50 0.55 0.42 0.39 0.36 0.03 0.03 0.90 1.24 0.03 0.03 0.03 0.03 0.47 2.40 1.58 0.03 0.03 0.03 0.03 1.48 0.81 0.30 0.31 0.51 1.74 2.46 1.10 1.18 1.65 0.59 0.03 0.40 0.72 1.48 0.52 0.42 0.74 0.67 0.03 0.03 0.03 0.03 0.62 1.42 0.73 0.57 0.03 2.94 1.85 1.04
⁎⁎ 0.35 0.47 0.12 0.63 0.49 3.47 0.40 0.42 0.02 0.002 0.16 0.20 0.11 0.40 1.12 0.42 1.04 0.39 1.17 2.29 0.16 0.24 0.20 3.08 1.69 0.26 0.09 0.01 0.18 0.01 0.54 0.95 0.001 0.32 0.37 0.01 0.32 3.44 3.00 0.17 0.34 0.30 ⁎⁎ ⁎⁎ ⁎⁎ ⁎⁎ 0.01 1.20 2.23 1.41 0.33 0.73 2.23 0.40 0.001 0.12 ⁎⁎ 2.10 0.15 ⁎⁎ 0.38 0.03 0.10 0.23 0.21 0.003 0.87 1.24 0.29 0.48 0.001 2.45 2.32 1.05 (continued on next page)
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Table 2 (continued) Date
Pm_DFIR (mm)
U3m (m s− 1)
Pc_DFIR (mm)
PSPC (mm)
7 8 9 10 11 12 13 14
T T 0.30 T T – T 0.25
⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ 1.04
⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ 0.45
⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎⁎
T: trace precipitation defined by Pm_DFIR b 0.1 mm; –: no precipitation; ⁎: no analysis, as the AWS was undergoing maintenance; ⁎⁎: no observation, as the SPC was undergoing maintenance; and U3m: wind speed at a height of 3 m above the ground surface.
precipitation of N1.5 mm. Thus, the SPC underestimates precipitation considerably when a number of particles are larger than 0.667 mm in diameter. A regression equation based on the least squares method is given as Pc
DFIR
= 0:787PSPC + 0:169
ð10Þ
and the determination coefficient is 0.72. Since the daily SPCestimated precipitation on Oct. 30 was clearly an erroneous measurement, demonstrating a limitation of the instrument, it was removed from the regression equation. It is important to note that even when the daily-corrected DFIR precipitation was low—almost 0 mm—the SPC detected the precipitation. Thus, a small amount of precipitation, which even a DFIR may be unable to detect, and which is characteristic of the polar regions, can be easily detected using an SPC. It is also important to note that the relationship between the dailycorrected DFIR and SPC-estimated precipitation values is only slight. Although the daily DFIR and SPC precipitation values are close to each other, the data are scattered. One possible explanation is that the area of the DFIR (20,000 mm2) is 400 times that of the SPC (50 mm2). If the integrating period of the SPC-estimated precipitation were longer, the scattering might be less. All the wind speeds used in the study are daily averages. If wind speeds are extremely variable, this will introduce a large amount of error. As shown in Eq. (9), PSPC varies in proportion to Vvertical and in inverse proportion to Uhorizontal, and errors of Vvertical and Uhorizontal values adopted in this study seem to be unavoidable. Furthermore, Vvertical is considered a constant, and Vvertical and Uhorizontal are assumed to be independent of the diameter, as shown in Eq. (9). Thus, it is reasonable to expect that using a speed
Fig. 4. Corrections for wetting loss (Pw_DFIR), evaporation loss (Pe_DFIR), trace amount (Pt_DFIR), and wind-induced loss of the DFIR-measured precipitation (Pm_DFIR) in Barrow, Alaska from October 24, 2002 to January 14, 2003. Seven days of data are missing from January 1–14.
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4. Conclusions
Fig. 5. Time series of the daily correction factors CF (ratio of the amount of daily-corrected DFIR precipitation, Pc_DFIR, to that of daily DFIR-measured precipitation, Pm_DFIR) from October 24, 2002 to January 14, 2003.
The total corrected DFIR precipitation was 1.47 times the uncorrected measurement. Correction for wind-induced, wetting, and evaporation losses, especially wind-induced loss was significant. The daily SPC-estimated precipitation values were close to the daily-corrected DFIR values with the determination coefficient of 0.72, and the SPC was able to detect a small amount of precipitation that might remain undetected by the DFIR. Although it is necessary to determine the relationship between the snow particle speed and particle diameter/shape in order to use the SPC as a precipitation gauge, it is noteworthy that precipitation measurements using instruments that do not directly catch snow particles, such as the SPC, may perform well in specific regions of low precipitation, such as the polar regions. The results and discussion of this study, especially the analysis and interpretation of gauge-measured solid precipitation data, will be useful for conducting hydrological studies in high-latitude regions. Additional efforts are required to further reduce the uncertainty in the amount of blowing snow caught by each gauge and to determine a suitable procedure for their use in the Arctic. Acknowledgments This study was supported by Grant-in-Aid for Scientific Research (C) (19540470). The authors thank the staff of the NOAA/ESRL Global Monitoring Division (D.J. Endres and others), the Barrow Arctic Science Consortium (Dr. G.W. Sheehan and others) and the International Arctic Research Center of the University of Alaska, Fairbanks (Dr. S. Akasofu and others) for supporting the field observations. The authors also thank Dr. K. Satoh of Kitami Institute of Technology and Dr. K. Suzuki of the Japan Agency for Marine-Earth Science and Technology for help with the field observations. The authors are grateful for the helpful comments by anonymous reviewers.
Fig. 6. Relationship between the daily-corrected DFIR (Pc_DFIR) and SPC-estimated (PSPC) precipitation from October 25, 2002 to January 6, 2003. The daily PSPC on October 30 (3.5 mm) was clearly an erroneous measurement.
expressed as a function of particle diameter would enable us to estimate the precipitation amount better. The other point relevant to the validity of the SPC-estimated precipitation is the effect of the snow particles' shape. Sato et al. (2005) showed theoretically the effect of particle shape on mass flux measurements of drifting snow, and this theoretical study was in agreement with experimental results taking into consideration the shape of the particles. Therefore, if the particle shape (that is, the l-, m-, and n-rectangular axes in the case of square-pillar-like particles) is measured, the SPC precipitation estimate improves. That is, Sato et al. (2005) is applicable to Sd in Eqs. (6) and (9), as m2 = 3 n 1 = 3 m − 1 = 3 n − 2 = 3 n 1 = 3 2 = 3 Sd = 0:34 + 0:44 + : ð11Þ l m l m m
The Sd is 1 in spherical particles and decreases with an increase in the degree of nonsphericity. Since the speed of snow particles is also affected by the degree of nonsphericity (e.g., Kajikawa, 1975), better SPC-estimated precipitation requires a precise relationship between the speed of snow particles and Sd. Finally, we can conclude that precipitation measurements can be effectively conducted in specific regions with low rates of solid precipitation such as the polar regions by using optical sensors such as an SPC. In the Arctic, there are many advantages of using an SPC instead of a DFIR. For example: An SPC can be automated and left at remote sites; it is lightweight and easy to transport; and fencing material may be hard to come by in some Arctic (or Antarctic) locations.
References Aaltonen, A., Elomaa, E., Tuominen, A., Valkovuori, P., 1993. Measurement of precipitation. in Proceedings of the Symposium on Precipitation and Evaporation. In: Sevruk, B., Lapin, M. (Eds.), Bratislava, Slovakia, Slovak Hydrometeorological Institute and Swiss Federal Institute of Technology, vol. 1, pp. 42–46. Allerup, P., Madsen, H., Vejen, F., 1997. A comprehensive model for correcting point precipitation. Nordic Hydrology 28, 1–20. Bagdanova, E.G., Ilyin, B.M., Dragomilova, I.V., 2002. Application of a comprehensive bias-correction model to precipitation measured at Russian North Pole Drifting Stations. Journal of Hydrometeorology 3, 700–713. Brown, T., Pomeroy, J.W., 1989. A blowing snow particle detector. Cold Regions Science and Technology 16, 167–174. Gubler, H., 1981. An electronic remote snow-drift gauge. Journal of Glaciology 27, 164–174. Golubev, V.S., 1989. Assessment of accuracy characteristics of the reference precipitation gauge with a double-fence shelter. Final report of the Fourth Session of the International Organizing Committee for the WMO Solid Precipitation Measurement Intercomparison, St. Moritz, Switzerland, WMO, Geneva, pp. 22–29. Goodison, B.E., Louie, P.Y.T., Yang, D., 1998. WMO Solid Precipitation Measurement Intercomparison Final Report. World Meteorological Organization Instruments and Observing Methods Report No. 67. 212 pp. Kajikawa, M., 1975. Experimental formula of falling velocity of snow crystals. Journal of the Meteorological Society of Japan 53, 267–275. Kajikawa, M., Kikuchi, K., Endo, T., Magono, C., 1983. Observation of snow crystals in the lower atmosphere of Arctic Canada by means of “Snow Crystal Sondes. Journal of the Meteorological Society of Japan 61, 388–401. Kikuchi, K., Tsuboya, S., Sato, N., Asuma, Y., Takeda, T., Fujiyoshi, Y., 1982. Observation of wintertime clouds and precipitation in the Arctic Canada (POLEX-North) Part 2: characteristic properties of precipitation particles. Journal of the Meteorological Society of Japan 60, 1215–1226. Kimura, T., 1991. Measurements of drifting snow particles. Journal of Geography 100, 250–263 (in Japanese, with English summary). Mekis, E., Hogg, W.D., 1999. Rehabilitation and analysis of Canadian daily precipitation time series. Atmosphere-Ocean 37, 53–85. Sato, A., 1987. Calculation of size-effect of blowing snow particles on the snow particle counter (first report). Technical Report National Center Disaster Prevention 40, 93–101. Sato, A., 1991. Trial observation of snowfall intensity measurements. Tohoku-no-yukito-seikatsu 6, 47–48 (in Japanese).
K. Sugiura et al. / Cold Regions Science and Technology 58 (2009) 77–83 Sato, A., Kimura, T., 1991. Measurement of snow mass flux with snow particle counter. Proceedings of Japan–U.S. Workshop on Snow Avalanche and Landslide, Debris Flow Prediction and Control, pp. 67–74. Sato, T., Kimura, T., Ishimaru, T., Maruyama, T., 1993. Field test of a new snow-particle counter (SPC) system. Annals of Glaciology 18, 149–154. Sato, T., Mochizuki, S., Kosugi, K., Nemoto, M., 2005. Effects of particle shape on mass flux measurement of drifting snow by snow particle counter. Journal of the Japanese Society of Snow and Ice 67, 493–503. Schmidt, R.A., 1977. A system that measures blowing snow. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO. Research Paper RM-194. 80 pp. Schmidt, R.A., 1982. Vertical profiles of wind speed, snow concentration, and humidity in blowing snow. Boundary-Layer Meteorology 23, 223–246. Struzer, L.R., 1971. On the Ways of Account of Precipitation Gauge Errors Caused by Falling of False Precipitation into Precipitation Gauges During Blizzards. Trans. Main Geophys. Observ., vol. 260, pp. 35–60 (in Russian). Sugiura, K., Ohata, 2004. Estimation of blowing snow particle speeds using output waveforms of an optical sensor. The Twenty-eighth Symposium on polar Meteorology and Glaciology Programme and Abstracts, Research Organization of Information and Systems/National Institute of Polar Research, Tokyo, p. 7. Sugiura, K., Nishimura, K., Maeno, N., Kimura, T., 1998. Measurements of snow mass flux and transport rate at different particle diameters in drifting snow. Cold Regions Science and Technology 27, 83–89.
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Sugiura, K., Yang, D., Ohata, T., 2003. Systematic error aspects of gauge-measured solid precipitation in the Arctic, Barrow, Alaska. Geophysical Research Letters 30, 1192. doi:10.1029/2002GL015547. Sugiura, K., Ohata, T., Yang, D., 2006. Catch characteristics of precipitation gauges in high-latitude regions with high winds. Journal of Hydrometeorology 7, 984–994. Takahashi, S., 1985. Characteristics of drifting snow at Mizuho Station, Antarctica. Annals of Glaciology 6, 71–75. Yang, D., 1999. An improved precipitation climatology for the Arctic Ocean. Geophysical Research Letters 26, 1625–1628. Yang, D., Ohata, T., 2001. A bias-corrected Siberian regional precipitation climatology. Journal of Hydrometeorology 2, 122–139. Yang, D., Goodison, B.E., Ishida, S., 1998. Adjustment of daily precipitation data at 10 climate stations in Alaska: application of World Meteorological Organization intercomparison results. Water Resources Research 34, 241–256. Yang, D., Ishida, S., Goodison, B.E., Gunther, T., 1999. Bias correction of daily precipitation measurements for Greenland. Journal of Geophysical Research 104D, 6171–6181. Yang, D., Kane, D.L., Zhang, Z., Legates, D., Goodison, B.E., 2005. Bias-corrections of longterm (1973–2004) daily precipitation data over the northern regions. Geophysical Research Letters 32, L19501. doi:10.1029/2005GL024057.
Contents lists available at ScienceDirect
Cold Regions Science and Technology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c o l d r e g i o n s
Application of a snow particle counter to solid precipitation measurements under Arctic conditions Konosuke Sugiura a,⁎, Tetsuo Ohata a, Daqing Yang b, Takeshi Sato c, Atsushi Sato d a
Institute of Observational Research for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka 237-0061, Japan Water and Environmental Research Center, University of Alaska, Fairbanks 99775-5860, USA National Research Institute for Earth Science and Disaster Prevention, Shinjo 996-0091, Japan d National Research Institute for Earth Science and Disaster Prevention, Nagaoka 940-0821, Japan b c
a r t i c l e
i n f o
Article history: Received 29 May 2008 Accepted 19 March 2009 Keywords: Solid precipitation Snow particle counter Double-Fence Intercomparison Reference Arctic
a b s t r a c t To develop a more reliable means for measuring precipitation in the Arctic, daily solid precipitation in Barrow, Alaska, was measured from October 24, 2002 to January 14, 2003 using a Double-Fence Intercomparison Reference (DFIR), which is the World Meteorological Organization (WMO) reference standard for solid precipitation measurements. Furthermore, a new approach using a snow particle counter (SPC) that outputs the number flux of snow particles without directly catching them was introduced. The correction procedures for wind, wetting, evaporation losses, and trace amounts were applied on a daily basis to the DFIR precipitation. The total corrected DFIR precipitation (61.35 mm) was found to be 1.47 times the uncorrected measurement (41.65 mm). The SPC-estimated precipitation measurements correlate well (r2 = 0.72) with the corrected DFIR measurements, suggesting that precipitation gauges that do not directly catch snow particles are effective in low-precipitation areas such as the polar regions. © 2009 Elsevier B.V. All rights reserved.
1. Introduction Solid precipitation is an essential part of the hydrological cycle over cold regions and seasons. It is generally recognized that the measurement via gauge of solid precipitation has systematic errors, mainly caused by wind-induced gauge undercatch. It is acknowledged that a precipitation gauge placed in a natural bush shelter, i.e., a bush gauge, would provide the best estimate of “ground true” precipitation, and is therefore considered a primary standard (Goodison et al., 1998). The bush gauge is encircled by bushes that are regularly trimmed to the level of the gauge orifice. However, this gauge cannot be used in all climatic regions. Therefore, Golubev (1989) discussed the need to adjust a Double-Fence Intercomparison Reference (DFIR) measurement to the “true” value of the bush gauge to reduce wind effects. The DFIR was selected as the World Meteorological Organization (WMO) reference standard for solid precipitation measurements during the WMO Solid Precipitation Measurement Intercomparison (Goodison et al., 1998). Although general correction procedures and reference measurements for precipitation have been developed for the Arctic
⁎ Corresponding author. Fax: +81 46 867 9255. E-mail address: [email protected] (K. Sugiura). 0165-232X/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.coldregions.2009.03.010
regions (Mekis and Hogg, 1999; Yang, 1999; Yang et al., 1998, 1999; Yang and Ohata, 2001; Bagdanova et al., 2002; Sugiura et al., 2003; Yang et al., 2005; Sugiura et al., 2006), further precipitation experiments based on WMO precipitation procedures are necessary to test and assess various types of precipitation gauges in Arctic regions, where the wind is strong and blowing snow occurs frequently. Struzer (1971) and Goodison et al. (1998) have reported the catch efficiency of precipitation gauges under strong wind conditions. Sugiura et al. (2003) suggested that, in order to counter the systematic errors in gauge-measured precipitation for Arctic conditions, we must consider not only wind-induced undercatch, wetting, and evaporation losses, but also the influence of blowing snow and trace precipitation. In this study, we analyzed solid precipitation using independent methods at a test site in the Arctic—Barrow, Alaska. First, we measured the precipitation using a DFIR; then we introduced a new approach for estimating solid precipitation using a snow particle counter (SPC) with an optical sensor. This SPC outputs the number flux of horizontally transported snow particles without directly catching them, and detects each particle. In this paper, we present meteorological data obtained from a DFIR and an automatic weather system (AWS) installed in Barrow, Alaska, and the correction methods applied to these data. Based on a comparison between the daily amounts of the corrected DFIR and SPC-estimated precipitation measurements, we propose using an SPC to measure solid precipitation in Arctic
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K. Sugiura et al. / Cold Regions Science and Technology 58 (2009) 77–83
Fig. 1. Double-Fence Intercomparison Reference.
conditions. Our goal is to develop a more reliable means for solid precipitation observation in the Arctic. 2. Methodology 2.1. Study site, period, and instruments The observation site in Barrow, Alaska, is located at the northernmost point of the United States and is entirely covered by snow during winter. The precipitation climatology of Barrow, based on data obtained from the National Climatic Data Center, shows that monthly mean precipitation from 1971 to 2000 in October, November, December, and January is 10, 4, 3, and 3 mm, respectively. This indicates that the precipitation at Barrow during winter is low. In this study, continuous precipitation and blowing snow observations were conducted for three months, from October 24, 2002 to January 14, 2003. We installed a DFIR near the Barrow Observatory (71°19′N,156°36′W) of the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division, as shown in Fig. 1. The DFIR consists of a shielded Russian Tretyakov gauge encircled by two octagonal lath fences. The heights of the outer and inner fences above the ground surface are 3.5 m and 3 m, respectively, and the diameters of the outer and inner fences are 12 m and 4 m, respectively. The length and density of the lath fences are 1.5 m and 50%, respectively. The Russian Tretyakov gauge (a cylindrical bucket with a wind shield) is designed to collect precipitation (snow, mixed precipitation, and rain) throughout the year. The orifice area of the bucket is 0.02 m2. The amount of precipitation in the bucket is measured manually using a graduated cylinder. The minimum amount of precipitation using the graduated cylinder is 0.10 mm, and the reading on the amount of precipitation is at a resolution of 0.05 mm. Several researchers have attempted to measure blowing snow flux using wide trenches, a box-type drift gauge, a cyclone-type drift collector, and a net-type drift collector (Kimura, 1991). These devices must catch snow particles directly and also estimate the catch efficiency. Recently, new instruments using an optical sensor without directly catching snow particles have been developed by Schmidt (1977), Gubler (1981), Brown and Pomeroy (1989), Sato and Kimura (1991), and Sato et al. (1993). In the present study, one of these newly developed instruments, an SPC, was introduced to estimate solid precipitation. Fig. 2 shows the SPC (SPC-S7, Niigata Electric; e.g., Sugiura et al., 1998), which has a self-steering wind vane and is equipped with a
superluminescent diode sensor. Based on the electrical pulse signals that correspond to the sizes of snow particles passing through a sampling area, each signal is classified into one of 32 classes between 0.05 and 0.5 mm. Furthermore, the SPC measures the number flux of the snow particles based on each particle's diameter and classifies them into one of 32 classes from 0.037 to 0.667 mm, including the edge effect of the sensor (Sato, 1987). If the diameter of a snow particle is larger than that of the maximum diameter class, that is, N0.667 mm, this diameter is also considered to belong to the maximum diameter class. The sampling area of the SPC is small— 50 mm2 (2 mm in height and 25 mm in width)—which is 1/400 that of the DFIR. The SPC sensor was installed on the tower of the Barrow Observatory of the NOAA/ESRL Global Monitoring Division near the DFIR at a height of 3 m, which is equal to that of the DFIR. The validity of the SPC system in blowing snow has been confirmed by field observations in Japan (e.g., Sato and Kimura, 1991; Sato et al., 1993) and wind tunnel experiments (Sugiura et al., 1998). Sato (1991) showed the potential of an SPC with a double-slit type sensor as an instrument for snowfall intensity measurements based on a comparison between snowfall intensity measurements using an electronic balance and the number of snow particles per sampling area per minute obtained from an SPC. Because the SPC sensor used in the Sato study is a double-slit type, such as that developed by Schmidt (1977), it can also determine particle speed, and the fall velocity of different particle shapes is reported. Although the SPC used in this study does not have this feature, Sugiura and Ohata (2004) showed in preliminary data that the SPC is able to measure particle speed using the time-interval difference between the beginning and end of
Fig. 2. Snow particle counter.
K. Sugiura et al. / Cold Regions Science and Technology 58 (2009) 77–83
an output waveform by an electrical pulse signal of a snow particle passing through a sampling area. However, this technique was not used in this study. Several meteorological sensors were installed at the site. The wind speed, air temperature, and relative humidity at a height of 3 m above the ground, and surface air pressure, were measured at 10-minute intervals from noon on October 24, 2002 to noon on January 6, 2003. Although data are available for blowing snow observations and precipitation up to 13 and 14 January 2003, respectively, several analyses in this study were conducted using data up to January 6, 2003, as the meteorological sensors were undergoing maintenance on and after January 7, 2003. In this study, each day's precipitation is calculated as the sum of the precipitation from noon the previous day to noon that day. 2.2. DFIR measurements Allerup et al. (1997) presented a comprehensive model for correcting point precipitation; the general model for correcting systematic errors in precipitation measurements is X ΔPim Pc = k Pm +
ð1Þ
where Pc is the corrected or “true” amount of precipitation; Pm, the measured amount; ΣΔPim, the sum of various error sources; and k, the correction factor for wind effects. Based on manual precipitation observations conducted in Barrow, Alaska, Sugiura et al. (2003) presented ΣΔPim as the sum of the error sources: X
ΔPim = Pwetting + Pevaporation + Pblowing + Ptrace
ð2Þ
where Pwetting and Pevaporation are the wetting and evaporation losses of the gauge, respectively, Pblowing is the false precipitation due to blowing snow (Pblowing is a negative quantity), and Ptrace is the trace amount. The WMO Solid Precipitation Measurement Intercomparison (Goodison et al., 1998) recommended that the adjustment equations of the DFIR for snow (Eq. (3)) and mixed precipitation should be used in future study. Furthermore, it also suggested that no adjustment was necessary for rain measurements performed with the DFIR. In the case of snow, the DFIR correction factor to counter the wind effects, kDFIR, is given as kDFIR =
2 PBUSH 100 + 0:439UDFIR + 0:246UDFIR = PDFIR 100
ð3Þ
where PBUSH is the precipitation measured using the bush gauge [mm]; PDFIR is the DFIR-measured precipitation [mm]; and UDFIR is the wind speed [m s− 1] at the DFIR's height (3 m). The DFIR correction factor (Eq. (3)) is a regression equation fit to 183 data points with a determination coefficient of 0.151 (Goodison et al., 1998). This low correlation suggests that there may be significant error in the corrected DFIR measurements. If the daily measured precipitation was less than 0.1 mm, we recorded it as trace precipitation. Thus, the maximum daily trace precipitation is less than 0.1 mm. Sugiura et al. (2003) adopted the daily trace precipitation, Ptrace, as a quarter of the scale increment on a graduated cylinder, i.e., 0.025 mm, based on winter precipitation observations in Barrow. In this study, a trace precipitation of 0.025 mm was assigned for each day that trace precipitation was recorded. A wetting loss experiment using the Russian Tretyakov gauge was conducted before the analysis. The wetting loss of the gauge, Pwetting [mm] per observation, was estimated from experiments on the difference between the weight of the gauge when completely dry and its weight when wet. Since the conversion of kilograms to meters of water depth can be performed by considering that 1000 kg of water
79
is equivalent to 1 m3, the wetting loss can be experimentally expressed as Pwetting = 1000
W Aρwater
ð4Þ
where W is the difference between the weights of the completely dry and wet gauges [kg]; A is the gauge orifice area [m2]; and ρwater is the density of water [kg m− 3]. For a completely wet gauge, on average an estimated maximum wetting loss value of 0.35 mm was obtained per experiment. Since the maximum wetting loss value is 0.35 mm, the average wetting loss per observation would typically be less than 0.35 mm. A wetting loss of 0.1 mm, which is less than half of 0.35 mm, is a conservative estimate. Therefore, in this study, a daily wetting loss value of 0.1 mm was applied as a correction per precipitation day, and no correction for wetting loss was applied for the trace precipitation days. On the basis of evaporation experiments conducted using the Tretyakov gauge at Jokioinen, Finland, Aaltonen et al. (1993) reported that mean daily evaporation loss during winter was much less than that during the rainy season, ranging from 0.1 to 0.2 mm. A daily evaporation loss value Pevaporation of 0.1 mm was adopted as a correction per precipitation day in the present study. No correction for evaporation loss was applied for the trace precipitation days. It is important to consider that both the DFIR and the SPC detect snow particles precipitated from clouds and those remobilized from the snow surface in windy conditions. The DFIR catches some, but not all, of the snow particles in windy conditions, with the missed fraction increasing with wind speed, i.e., wind-induced gauge undercatch. On the other hand, since the SPC detects snow particles without directly catching them, there is no need to estimate the catch efficiency. In this study, no adjustments were made for false precipitation due to blowing snow, Pblowing, in Eq. (2). The correction factor CF is the ratio of the amount of corrected DFIR precipitation, Pc_DFIR, to that of DFIR-measured precipitation, Pm_DFIR; it can be calculated as CF =
P Pc DFIR ΔPim DFIR = kDFIR 1 + Pm DFIR Pm DFIR
ð5Þ
where ∑ΔPim_DFIR is the sum of various error sources for DFIR. It is noteworthy that the daily correction factor has an inherent tendency to decrease with decreasing wind speed. 2.3. SPC measurements Since the number flux data in the minimum-diameter class included noise, and all classes included indistinct and instantaneous periodic noise at intervals of approximately 12 s for the entire observation period at Barrow, Alaska, the minimum-diameter data were excluded from the following analyses, and the number flux data for 6 s h− 1 were used to avoid subjective selection of the data. The procedures to calculate the SPC-estimated precipitation, PSPC, are as follows: 1) The horizontal number flux [m− 2 s− 1] measured using the SPC was converted into horizontal mass flux, Fh_SPC [kg m− 2 s− 1], as Fh
SPC
=
32 πρparticle X 3 Sd Nd Dd 6 d=1
ð6Þ
where ρparticle is the density of the snow particles, which is assumed to be that of ice, i.e., 917 kg m− 3; Nd is the number flux of the d-th class [m− 2 s− 1]; Dd is the diameter of the snow particles of the d-th class [m]; and Sd is the shape factor of snow particles of the d-th class, which is the ratio of a spherical cubic
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K. Sugiura et al. / Cold Regions Science and Technology 58 (2009) 77–83
Table 1 Summary of the precipitation collected from October 24, 2002 to January 14, 2003 in Barrow, Alaska. Source of the precipitation data
No-observation days
No-precipitation days
Trace days (Precip b 0.1 mm)
Precipitation days including trace days
Type of precipitation
DFIR SPC
0 8
3 0
24 12a
80 75
Snow Snow
a
The SPC data has theoretically no trace days.
volume to the snow particle cubic volume and is assumed to be 1 in this study. 2) The horizontal mass flux, Fh_SPC, was converted into snow particle concentration, CSPC [kg m− 3], according to the horizontal speed of the snow particles as CSPC =
Fh SPC Uhorizontal
ð7Þ
where Uhorizontal is the horizontal speed of the snow particles [m s− 1], which is assumed to be the horizontal wind speed at the SPC's height (3 m) in this study, based on the result of Schmidt (1982). 3) The snow particle concentration, CSPC, was converted into vertical mass flux, Fv_SPC, according to the vertical speed of the snow particles, Vvertical, as Fv
SPC
= Vvertical CSPC :
ð8Þ
4) Then, the vertical mass flux, Fv_SPC, was converted into SPCestimated precipitation, PSPC, according to the density of water, ρwater, as
PSPC
F = v SPC = ρwater
πVvertical ρparticle
32 P
Sd Nd D3d
d=1
6Uhorizontal ρwater
:
ð9Þ
Several studies have been conducted on the speed of snow particles. Takahashi (1985) observed the vertical speed of snow particles that were guided into a closed dark box through a perpendicular shaft 2.5 m in length at the Mizuho Station, Antarctica; the vertical speed was between 0.3 and 0.9 m s− 1 and increased with the wind speed, which ranged from 5 to 13 m s− 1. Kajikawa (1975) measured the vertical speed of plane type, spatial type, and rimed snow crystals that were allowed to fall at terminal velocity through a tube 0.78 m in length over a range of particle diameters from 0.1 to 5 mm at Mt. Teine, Japan, using stroboscopic photographs; the vertical speed was distributed in a wide range between 0.1 and 1.3 m s− 1 and increased with particle diameter. Since the size of snow particles
Fig. 3. Histograms of daily mean diameter of snow particles from October 25, 2002 to January 13, 2003 using the SPC.
measured by us is small, as stated below, in this study the vertical speed of snow particles, Vvertical, is assumed to be 0.25 m s− 1, and PSPC is calculated using Eq. (9). 3. Results and discussion Table 1 summarizes the precipitation recorded from October 24, 2002 to January 14, 2003 (83 days). The type of precipitation for the entire observation period was snow. On a few days, no precipitation was observed. The number of trace precipitation days, as defined using the DFIR measurements (i.e., less than 0.1 mm), was 24. However, theoretically, the number of trace precipitation days using the SPC measurements cannot be determined, because the SPC can measure precipitation with a precision of one snow particle. As shown in Fig. 3, the daily mean diameter of snow particles using the SPC ranges from 0.04 mm to 0.48 mm with an average of 0.15 mm. Regarding the characteristic properties of precipitation particles in the Arctic, Kikuchi et al. (1982) observed mainly dendritic crystals in the warm air from the Pacific Ocean, and bullets, columns, and crossed plates in polar air mass, with the equivalent diameters ranging from 0.12 mm to 0.27 mm. Kajikawa et al. (1983) also observed snow crystals in Arctic Canada, and the diameter ranged from 0.1 mm to 0.3 mm. In comparison with the middle-latitude area, snow particles were relatively small. These typical snow particles are measurable by the SPC, which has a measurable range of 0.037–0.667 mm. The corrected DFIR and SPC-estimated precipitation amounts are listed in Table 2. The total corrected DFIR precipitation was 1.47 times the uncorrected measurement, that is, 61.35 and 41.65 mm, respectively. Fig. 4 shows sums of the daily measured and corrected DFIR precipitation amounts. The biweekly correction for trace amounts is small (0.00, 0.01, 0.03, 0.01, 0.02, and 0.00 times the uncorrected measurement for the respective periods of October 24–31, November 1–15, November 16–30, December 1–15, December 16–31, and January 1–14); however, the corrections for wetting (0.09, 0.18, 0.16, 0.12, 0.18, and 0.10 times, respectively), evaporation (0.09, 0.18, 0.16, 0.12, 0.18, and 0.10 times, respectively), and wind-induced loss (0.23, 0.14, 0.24, 0.24, 0.17, and 0.11 times, respectively) increased the total amount of corrected precipitation, especially the correction for windinduced loss. The sum of the corrected DFIR precipitation is 1.41, 1.51, 1.59, 1.49, 1.55, and 1.33 times the uncorrected measurement for the respective periods of October 24–31, November 1–15, November 16– 30, December 1–15, December 16–31, and January 1–14. Fig. 5 shows the daily correction factor, CF, which is the ratio of the amount of daily-corrected DFIR precipitation to that of daily DFIR-measured precipitation (Eq. (5)). The correction factor is often high (N2), suggesting that there may be significant errors in the resulting corrected DFIR measurements. Although the maximum correction factor during early winter is 3.96, the daily correction factor decreases gradually in the middle of winter. Fig. 6 shows the relationship between the daily-corrected DFIR and SPC-estimated precipitation values. On October 30, 2002, daily SPC-estimated precipitation was notably low: 3.5 mm using the SPC compared with 8.1 mm using the DFIR, as shown in Table 2. On that day, the diameter of several snow particles was over 0.667 mm. The SPC underestimates particles with a diameter of over 0.667 mm and counts particles larger than 0.667 mm as having a diameter equal to 0.667 mm. The percentage of the number of particles with a diameter of 0.667 mm was 2.8%, corresponding to a daily SPC-estimated
K. Sugiura et al. / Cold Regions Science and Technology 58 (2009) 77–83 Table 2 Bias corrections of the daily DFIR-measured and SPC-estimated precipitation measurements from October 24, 2002 to January 14, 2003 in Barrow, Alaska. Date
Pm_DFIR (mm)
U3m (m s− 1)
Pc_DFIR (mm)
PSPC (mm)
Oct 24, 2002 25 26 27 28 29 30 31 Nov 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Dec 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Jan 1, 2003 2 3 4 5 6
0.70 0.30 0.30 0.20 0.70 0.20 6.20 0.65 0.85 – T 0.35 T – 0.10 0.30 0.20 0.30 0.10 0.60 2.50 0.25 T 0.70 2.50 0.30 0.20 0.15 0.10 T T 0.50 1.00 T T T T 0.20 1.70 1.00 T T T T 1.20 0.60 0.10 0.10 0.30 1.30 1.65 0.70 0.75 1.20 0.35 T 0.20 0.40 1.05 0.25 0.15 0.50 0.45 T T T T 0.40 1.10 0.50 0.35 T 2.40 1.45 0.80
8.27 2.02 3.02 2.69 2.91 8.42 9.78 4.07 4.06 6.19 3.57 4.77 12.15 14.69 10.55 6.76 3.75 5.07 4.30 5.29 5.78 5.07 3.92 4.17 10.14 5.36 3.99 6.10 7.97 8.38 9.36 9.99 3.01 2.77 8.48 6.28 6.37 7.40 9.47 10.56 2.89 5.42 6.84 2.44 3.97 1.04 1.69 1.90 2.19 7.24 10.72 8.67 9.14 7.70 4.27 1.49 1.45 8.24 7.86 7.21 8.29 4.27 2.80 4.73 4.11 0.99 1.30 3.12 5.31 3.63 3.07 1.28 6.50 6.27 3.23
1.08 0.51 0.52 0.41 0.93 0.48 8.18 0.90 1.11 – 0.03 0.59 0.04 – 0.40 0.57 0.42 0.54 0.32 0.87 2.99 0.49 0.03 0.96 3.50 0.55 0.42 0.39 0.36 0.03 0.03 0.90 1.24 0.03 0.03 0.03 0.03 0.47 2.40 1.58 0.03 0.03 0.03 0.03 1.48 0.81 0.30 0.31 0.51 1.74 2.46 1.10 1.18 1.65 0.59 0.03 0.40 0.72 1.48 0.52 0.42 0.74 0.67 0.03 0.03 0.03 0.03 0.62 1.42 0.73 0.57 0.03 2.94 1.85 1.04
⁎⁎ 0.35 0.47 0.12 0.63 0.49 3.47 0.40 0.42 0.02 0.002 0.16 0.20 0.11 0.40 1.12 0.42 1.04 0.39 1.17 2.29 0.16 0.24 0.20 3.08 1.69 0.26 0.09 0.01 0.18 0.01 0.54 0.95 0.001 0.32 0.37 0.01 0.32 3.44 3.00 0.17 0.34 0.30 ⁎⁎ ⁎⁎ ⁎⁎ ⁎⁎ 0.01 1.20 2.23 1.41 0.33 0.73 2.23 0.40 0.001 0.12 ⁎⁎ 2.10 0.15 ⁎⁎ 0.38 0.03 0.10 0.23 0.21 0.003 0.87 1.24 0.29 0.48 0.001 2.45 2.32 1.05 (continued on next page)
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Table 2 (continued) Date
Pm_DFIR (mm)
U3m (m s− 1)
Pc_DFIR (mm)
PSPC (mm)
7 8 9 10 11 12 13 14
T T 0.30 T T – T 0.25
⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ 1.04
⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ 0.45
⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎⁎
T: trace precipitation defined by Pm_DFIR b 0.1 mm; –: no precipitation; ⁎: no analysis, as the AWS was undergoing maintenance; ⁎⁎: no observation, as the SPC was undergoing maintenance; and U3m: wind speed at a height of 3 m above the ground surface.
precipitation of N1.5 mm. Thus, the SPC underestimates precipitation considerably when a number of particles are larger than 0.667 mm in diameter. A regression equation based on the least squares method is given as Pc
DFIR
= 0:787PSPC + 0:169
ð10Þ
and the determination coefficient is 0.72. Since the daily SPCestimated precipitation on Oct. 30 was clearly an erroneous measurement, demonstrating a limitation of the instrument, it was removed from the regression equation. It is important to note that even when the daily-corrected DFIR precipitation was low—almost 0 mm—the SPC detected the precipitation. Thus, a small amount of precipitation, which even a DFIR may be unable to detect, and which is characteristic of the polar regions, can be easily detected using an SPC. It is also important to note that the relationship between the dailycorrected DFIR and SPC-estimated precipitation values is only slight. Although the daily DFIR and SPC precipitation values are close to each other, the data are scattered. One possible explanation is that the area of the DFIR (20,000 mm2) is 400 times that of the SPC (50 mm2). If the integrating period of the SPC-estimated precipitation were longer, the scattering might be less. All the wind speeds used in the study are daily averages. If wind speeds are extremely variable, this will introduce a large amount of error. As shown in Eq. (9), PSPC varies in proportion to Vvertical and in inverse proportion to Uhorizontal, and errors of Vvertical and Uhorizontal values adopted in this study seem to be unavoidable. Furthermore, Vvertical is considered a constant, and Vvertical and Uhorizontal are assumed to be independent of the diameter, as shown in Eq. (9). Thus, it is reasonable to expect that using a speed
Fig. 4. Corrections for wetting loss (Pw_DFIR), evaporation loss (Pe_DFIR), trace amount (Pt_DFIR), and wind-induced loss of the DFIR-measured precipitation (Pm_DFIR) in Barrow, Alaska from October 24, 2002 to January 14, 2003. Seven days of data are missing from January 1–14.
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4. Conclusions
Fig. 5. Time series of the daily correction factors CF (ratio of the amount of daily-corrected DFIR precipitation, Pc_DFIR, to that of daily DFIR-measured precipitation, Pm_DFIR) from October 24, 2002 to January 14, 2003.
The total corrected DFIR precipitation was 1.47 times the uncorrected measurement. Correction for wind-induced, wetting, and evaporation losses, especially wind-induced loss was significant. The daily SPC-estimated precipitation values were close to the daily-corrected DFIR values with the determination coefficient of 0.72, and the SPC was able to detect a small amount of precipitation that might remain undetected by the DFIR. Although it is necessary to determine the relationship between the snow particle speed and particle diameter/shape in order to use the SPC as a precipitation gauge, it is noteworthy that precipitation measurements using instruments that do not directly catch snow particles, such as the SPC, may perform well in specific regions of low precipitation, such as the polar regions. The results and discussion of this study, especially the analysis and interpretation of gauge-measured solid precipitation data, will be useful for conducting hydrological studies in high-latitude regions. Additional efforts are required to further reduce the uncertainty in the amount of blowing snow caught by each gauge and to determine a suitable procedure for their use in the Arctic. Acknowledgments This study was supported by Grant-in-Aid for Scientific Research (C) (19540470). The authors thank the staff of the NOAA/ESRL Global Monitoring Division (D.J. Endres and others), the Barrow Arctic Science Consortium (Dr. G.W. Sheehan and others) and the International Arctic Research Center of the University of Alaska, Fairbanks (Dr. S. Akasofu and others) for supporting the field observations. The authors also thank Dr. K. Satoh of Kitami Institute of Technology and Dr. K. Suzuki of the Japan Agency for Marine-Earth Science and Technology for help with the field observations. The authors are grateful for the helpful comments by anonymous reviewers.
Fig. 6. Relationship between the daily-corrected DFIR (Pc_DFIR) and SPC-estimated (PSPC) precipitation from October 25, 2002 to January 6, 2003. The daily PSPC on October 30 (3.5 mm) was clearly an erroneous measurement.
expressed as a function of particle diameter would enable us to estimate the precipitation amount better. The other point relevant to the validity of the SPC-estimated precipitation is the effect of the snow particles' shape. Sato et al. (2005) showed theoretically the effect of particle shape on mass flux measurements of drifting snow, and this theoretical study was in agreement with experimental results taking into consideration the shape of the particles. Therefore, if the particle shape (that is, the l-, m-, and n-rectangular axes in the case of square-pillar-like particles) is measured, the SPC precipitation estimate improves. That is, Sato et al. (2005) is applicable to Sd in Eqs. (6) and (9), as m2 = 3 n 1 = 3 m − 1 = 3 n − 2 = 3 n 1 = 3 2 = 3 Sd = 0:34 + 0:44 + : ð11Þ l m l m m
The Sd is 1 in spherical particles and decreases with an increase in the degree of nonsphericity. Since the speed of snow particles is also affected by the degree of nonsphericity (e.g., Kajikawa, 1975), better SPC-estimated precipitation requires a precise relationship between the speed of snow particles and Sd. Finally, we can conclude that precipitation measurements can be effectively conducted in specific regions with low rates of solid precipitation such as the polar regions by using optical sensors such as an SPC. In the Arctic, there are many advantages of using an SPC instead of a DFIR. For example: An SPC can be automated and left at remote sites; it is lightweight and easy to transport; and fencing material may be hard to come by in some Arctic (or Antarctic) locations.
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