Prior to the beginning of the World Meteorological Organization's
(WMO) Solid Precipitation Inter-Comparison Experiment (SPICE, 2013–2015),
two precipitation measurement intercomparison sites were established in
Saskatchewan to help assess the systematic bias in the automated gauge
measurement of solid precipitation and the impact of wind on the undercatch
of snow. Caribou Creek, located in the southern boreal forest, and
Bratt's Lake, located in the southern plains, are a contribution to the
international SPICE project but also to examine national and regional issues
in measuring solid precipitation, including regional assessment of wind bias
in precipitation gauges and windshield configurations commonly used in
Canadian monitoring networks. Overlapping with WMO-SPICE, the Changing Cold
Regions Network (CCRN) Special Observation and Analysis Period (SOAP)
occurred from 2014 to 2015, involving other enhanced observations and cold
regions research projects in the same geographical domain as the
Saskatchewan SPICE sites. Following SPICE, the two Saskatchewan sites
continued to collect core meteorological data (temperature, humidity, wind
speed, etc.) as well as precipitation observations via several automated
gauge configurations, including the WMO automated reference and the
Meteorological Service of Canada's (MSC) network gauges. In addition, manual
snow surveys to collect snow cover depth, density, and water equivalent were
completed over the duration of the winter periods at the northern Caribou
Creek site. Starting in the fall of 2013, the core intercomparison
precipitation and ancillary data continued to be collected through the
winter of 2017. Automated observations were obtained at a temporal
resolution of 1 min, subjected to a rigorous quality control process, and
aggregated to a resolution of 30 min. The manual snow surveys at Caribou
Creek were typically performed every second week during the SPICE field
program with monthly surveys following the end of the SPICE intercomparison
period. The Saskatchewan SPICE data are available at
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Cold region hydrology and climatology research and monitoring requires accurate measurements of solid precipitation, which are crucial for water resource forecasting, driving climate and hydrological models, and climate monitoring and trend analysis (Barnett et al., 2005; Gray et al., 2001; Bartlett et al., 2006; Laukkanen, 2004). The systematic snowfall measurement biases, either via a manual observer or automated measurements, are well-documented (e.g. Sevruk et al., 1991; Goodison et al., 1998) and have resulted in international intercomparison initiatives such as the World Meteorological Organization's (WMO) Solid Precipitation Measurement Intercomparison (Goodison et al., 1998) and the Solid Precipitation Inter-Comparison Experiment (SPICE; Rasmussen et al., 2012; Nitu et al., 2012). The objectives of these intercomparisons were to examine the relative systematic biases of a variety of instrument configurations and to provide solutions for adjusting and homogenizing solid precipitation data such as in Yang et al. (1998, 2005), Sevruk et al. (2009), Wolff et al. (2015), and Kochendorfer et al. (2017a, b, 2018).
During SPICE, there were eight sites that operated at least one double-fence
automated reference (DFAR), including Caribou Creek and Bratt's Lake (Nitu
et al., 2012). The DFAR configuration consists of a large octagonal double
wind fence of the same specifications as used by the WMO Double Fence
Intercomparison Reference (DFIR; Yang et al., 1993; Goodison et al., 1998)
only with the DFIR manual Tretyakov precipitation gauge replaced with either
a Geonor T-200B or an OTT Pluvio
Conceptual diagram (
Another requirement of SPICE for sites operating a DFAR reference was the
inclusion of an Alter-shielded and unshielded gauge pair, either Geonor
T-200B or OTT Pluvio
The SPICE Alter-shielded
One of the legacies of the WMO-SPICE project is the high-quality precipitation and ancillary data set consisting of multiple precipitation gauge configurations (different gauge models with various measurement principles utilizing many wind shield designs), wind speed measurements at both gauge height and the standard 10 m height, air temperature, and often precipitation type observations (via optical sensors). The bulk of the international WMO-SPICE data set will be made available by the WMO once data agreements have been completed. In parallel to data collection, SPICE also developed robust data quality control techniques that can be applied to both SPICE and post-SPICE data (Kochendorfer et al., 2017b).
The SPICE precipitation data set, besides being useful for intercomparing gauge configurations for performance assessment and data homogenization, is useful for remote-sensing validation, hydrological modelling applications, and further refinement and testing of precipitation gauge transfer functions. Data collected in western Canada at the Saskatchewan SPICE sites are a contribution to the Changing Cold Regions Network (CCRN; DeBeer et al., 2016) and more specifically for the CCRN Special Operations and Analysis Period (SOAP) which was conducted from 1 October 2014 to 30 September 2015 across all of the CCRN “Water, Ecosystem, Cryosphere and Climate (WECC)” observatories, including the Saskatchewan WMO-SPICE sites.
Table 1 shows the location and climate details of both Bratt's Lake (XBK) and Caribou Creek (CCR). The locations of the sites are indicated on the map in Fig. 3.
Saskatchewan SPICE site locations (latitude, longitude, and
elevation), mean annual air temperature (
Location of the Caribou Creek and Bratt's Lake SK sites in western Canada (base map obtained from Google Earth; Data SIO, U.S. Navy, GEBCO © 2018 Google; Image Landsat/Copernicus).
The Caribou Creek SPICE site was established in November of 2012 and was
fully operational by February 2013. The site is located in the southern
Canadian boreal forest, about 100 km northeast of Prince Albert, Saskatchewan.
Harvested in 2004 and previously instrumented as part of the Boreal
Ecosystem Research and Monitoring Site project (BERMS; Barr et al., 2012)
and the FluxNet Canada program (Margolis et al., 2006), the site consists of
a regenerating jack pine canopy with tree heights of about 2 to 3 m. This
makes the site opportune for measuring precipitation in a bush-sheltered
area, similar to, but not exactly the same as, the Valdai site (Yang et al.,
1993) where unsheltered gauges were compared with gauges located in the
bush. The pre-existing Geonor T-200B (used for BERMS) was nearly ideally
located within the well-sheltered bush area and would become the site
“bush” gauge (Fig. 4a). Prior to the beginning of the SPICE
intercomparison period, a clearing with dimensions of approximately 60 m
Precipitation gauge installations at the Caribou Creek SPICE site:
Wind speed at CCR included in this data set was measured at 2 m above the ground in the clearing using a Gill cup wheel anemometer. Temperature was measured with a Campbell Scientific HMP45C mounted at 1.5 m above the ground inside a naturally aspirated radiation shield installed near the centre of the clearing.
During SPICE, CCR hosted an automated SWE (snow water equivalent) sensor, and to facilitate testing and intercomparison of this sensor, manual snow surveys to measure SWE were performed every 2 weeks throughout the SPICE campaign (Smith et al., 2017). Following SPICE, the manual snow surveys continued to be performed monthly (with the exception of the winter of 2015/2016 which had no snow surveys). The snow survey used a double sampling technique (Rovansek et al., 1993) in which five bulk density samples were taken using an ESC-30 snow tube sampler (Farnes et al., 1983) with a total of 50 snow depth measurements taken with a snow rod between the density samples. The snow survey transect started in the vegetated area south of the clearing and crossed the clearing into the vegetated area to the north.
The Bratt's Lake observatory is located approximately 30 km southwest of Regina, Saskatchewan. The site is situated on the open prairie with very little topographic relief, resulting in high exposure and, therefore, relatively high wind speeds (Table 1). The observation site is mown grass surrounded by agricultural crops. The lack of vegetation other than short grasses enhances the exposure. The precipitation infrastructure was installed in 2003 and included a DFIR as the manual reference for the DFAR as well as other automatic gauges including the Alter-shielded Geonor T-200B (Smith, 2009). Prior to SPICE, the site was fully automated, including the two DFARs (Fig. 1b) and the same Alter-shielded and unshielded Geonor T-200B precipitation gauges (Fig. 2) as at CCR. Wind speed was measured by an R.M. Young propeller anemometer at a height of approximately 2 m above the ground. Temperature and relative humidity (not reported) were measured with a Campbell Scientific HMP45C instrument inside a fan aspirated Stevenson screen at 1.5 m above the ground. Unlike CCR, there were no manual snow surveys performed at XBK.
Prior to the start of the SPICE field campaigns, the organizing committee
decided that the reference precipitation gauges used for SPICE needed to
have rim heaters to prevent gauge capping (where the gauge orifice is
blocked or partially blocked with snow). For the Geonor gauges discussed
here, the heaters and thermistors for monitoring and switching were added
prior to installation in the field. The heaters can be seen in Fig. 2b where the external “chimney” is wrapped with a heating element
(seen as yellow in the photo). The heating tape extends down into the lower
“chimney” which is not visible in the photo, preventing melted snow
from refreezing in the lower chimney before reaching the storage bucket
inside the gauge. The heaters were turned on when the air temperature
dropped below 2
To prevent freezing of the precipitation gauge bucket contents over the winter, the gauges were “charged” with 3 to 4 L of an antifreeze mixture consisting of 60 % methanol and 40 % propylene glycol. The methanol serves to decrease the density of the antifreeze mixture so that the contents do not stratify and freeze. A lightweight electrical insulating oil (approximately 0.5 L) was then poured on top of the bucket contents to prevent evaporation of both the antifreeze and the collected precipitation.
As both the XBK and CCR sites were required to be consistent with the other international SPICE sites, the data collection frequency for the automated data was standardized. The data loggers performed a program execution and instrument read every 20 s and these data were averaged and output once per minute. The 1 min data were stored on the site data loggers and retrieved daily by the site computer. Typically once per week, the site computers were accessed remotely and the data were retrieved for quality control and post-processing.
Following retrieval, the 1 min data were filed into time consistent (i.e. no gaps in the time series even if the data were missing) monthly files. The data were graphed and the time series examined for instrument failures and inconsistencies. The same quality control process applied to the international SPICE data (i.e. Kochendorfer et al., 2017b) was used for our data on both the SPICE and post-SPICE observation periods. This was an automated process which removed out-of-range outliers and data jumps, the thresholds for which were set using limits that were defined by physical possibility for each site. For the precipitation gauge bucket weight data, this also included the removal of data jumps related to gauge servicing (bucket emptying and/or charging). Anything missed or flagged by the automated quality control process was then examined and managed manually.
Quality control of the snow survey data was largely completed at the time of digitization when the field observation sheets were transferred into a spreadsheet. Snow depth data were plotted and examined for outliers, which were generally from misreading the snow rod or incorrectly transcribing the observation in the field. Outliers were removed and not included in the site mean and standard deviation. The same was done for the density samples.
The quality controlled 1 min bucket weight data from the precipitation gauges were first smoothed using a Gaussian filter with a 4 min running window. This filter smoothed spikes in the time series resulting from mechanical or electrical noise. The time series were then zeroed to the start of the season and further filtered using a revised version of the “brute force” precipitation filter developed by Environment and Climate Change Canada Climate Research Division, introduced by Pan et al. (2016), and henceforth called the “neutral aggregating filter” (NAF). Although Pan et al. (2016) briefly describes the filter, the NAF and some subsequent improvements are described in more detail here.
NAF is an automated method to remove noise from cumulative precipitation
time series by iteratively balancing positive and negative noise until all
changes below a user defined threshold,
The algorithm is conceptually simple: all non-zero changes in interval
precipitation,
Following the quality control described above, the NAF algorithm processing
steps are as follows:
The change in interval precipitation, All non-zero All points with From the point Steps (2) to (5) are repeated for the revised time series from (5), first
re-ranking the non-zero The cumulative precipitation time series is calculated as the cumulative sum
of the non-missing values from (6). The results from (7) are inspected by plotting the difference between the
filtered and original cumulative precipitation time series. Periods where
the differences diverge significantly from zero (e.g. by more than 1 mm) are
an indication of an anomaly in the cumulative time series, likely the result
of evaporative losses.
After step (8) above, if there are no large divergences in the cumulative time
series, the NAF cumulative time series can be accepted without further
processing. If periods with significant divergences are found, the time
series should be visually inspected to identify the cause, and if possible,
known problems should be eliminated from the time series. As indicated
above, the most common issue is negative drift resulting from evaporation
from the bucket contents, in which case the NAF flattens out periods of
negative drift between precipitation events. This typically occurs several
times within the time series. The result is an underestimation of the
precipitation amount at the start and end of each flattened, diverging
period, and sometimes the elimination of small precipitation events during
the period (see Fig. 5). There can also be spurious excursions over shorter
intervals that have no apparent cause and need removal.
NAF (red) and NAF-S (black) precipitation data time series data processing example compared to the Gaussian filtered raw bucket weight (blue). Data excerpt is from an actual precipitation time series observed at XBK in October 2015.
Typically, raw precipitation data from accumulating gauges, such as those presented here, have enough inherent evaporation or spurious excursions to create accumulating errors in seasonal precipitation as high as 10 % of the total, following the NAF process. At this time, there is no automated procedure to satisfactorily reduce this error. For this reason, a supervised process for adjusting the cumulative time series for evaporation and other spurious data was developed. This process uses NAF as the first guess and allows the user to interactively select the end points of diverging periods, identified in step (8) above, or the spurious events, and the algorithm effectively adjusts the “flattening” of the cumulative time series. This supervised process, called NAF-S, effectively reduces the impact of evaporation but does require some user subjectivity to identify the end points.
Figure 5 shows an example of the NAF (red) and NAF-S (black) post-processing
of the raw precipitation gauge bucket weight following data quality control
and the application of a Gaussian filter (blue) on an excerpt of real bucket
weight data obtained at XBK in October of 2015. Note how the NAF (using a
Following NAF-S on the SPICE data, the 1 min time series were resampled to 30 min intervals. The difference between the 30 min bucket weights are the 30 min precipitation amounts reported in this data set.
The missing data values in the SK SPICE data set were set to the numeric
value of
The precipitation data published here were not adjusted for wind undercatch.
However, this data set includes all of the ancillary data required to
perform adjustments using various published techniques and transfer
functions (i.e. Wolff et al., 2015; Kochendorfer et al., 2017b; Smith,
2009). The data flags were included to assist the user in making an
adjustment for wind. Precipitation data with a Flag
Seasonal totals of precipitation (October through April, where available) and the relative catch (%) for the Geonor SA and Geonor bush compared to the DFAR. Incomplete seasonal totals (I) are usually due to precipitation data starting later than 1 October (see footnotes).
Seasonal time series of accumulated precipitation for the various gauge configurations at Bratt's Lake and Caribou Creek. Note that although the accumulation season is from 1 October through 30 April, not all time series start at the beginning of the season due to gauge or site issues (see Table 2).
Table 2 shows the seasonal accumulations of precipitation for both Bratt's Lake and Caribou Creek and for the various gauge configurations. Note that the seasonal totals are for 1 October through 30 April, unless noted otherwise. Several seasonal accumulations are abbreviated due to data availability beginning later in the year. These are indicated as incomplete with an (I) in the table and the beginning of the accumulation period is noted in the footnote under the table. The corresponding accumulated precipitation time series are shown in Fig. 6.
Caribou Creek SWE observations by date for 2013/2014, 2014/2015, and 2016/2017. “mm w.e.” refers to millimetres of water equivalent.
Although the intent of this paper is not to present an intercomparison of
the gauge configuration catch efficiencies nor the precipitation differences
between the sites, there are several points that can be made about these
observations. Although it is difficult to ascertain due to incomplete
seasons, the winter precipitation (as measured by the DFAR) is generally
greater at CCR than it is at XBK. The October–April 2013/2014 accumulations
in Table 2 are an example of this, and are consistent with the climate
normals indicated in Table 1. Table 1 also shows that the average gauge
height wind speed at XBK (4.4 m s
Figure 7 shows the mean transect SWE observations from Caribou Creek,
calculated as the product of the mean transect snow depth (
The precipitation data collected at the Bratt's Lake and Caribou Creek sites during the SPICE intercomparison period (2013/2014 and 2014/2015) were contributed to the WMO-SPICE intercomparison and used to develop the SPICE transfer functions (Kochendorfer et al., 2017b, 2018). The snow survey data over the same period were used as the reference for assessing the performance of an automated SWE sensor (Smith et al., 2017). With the continuation of the data collection at these sites, the 2015/2016 and 2016/2017 (and beyond) data are used for an independent assessment of the SPICE transfer functions, providing data from both the reference gauge configuration (DFAR) and a test gauge configuration (Geonor SA). Figure 8, as an example, shows the unadjusted (solid black) and adjusted Geonor SA (solid red and blue; using the SPICE Eqs. 3 and 4 transfer functions from Kochendorfer et al., 2017b) accumulated time series of precipitation at Caribou Creek (Fig. 8a) and Bratt's Lake (Fig. 8b) for the 2016/2017 winter compared to the accumulated DFAR (dashed black) for the same period. Preliminary results from Caribou Creek (Fig. 8a) suggest that both of the SPICE transfer functions (Eq. 3 which incorporates air temperature and Eq. 4 which does not) over-adjust the winter precipitation at this site by approximately 8 %. Alternatively, the preliminary results from Bratt's Lake (Fig. 8b) suggest that both transfer functions under-adjust the winter precipitation at this site by nearly 25 % in this example. Although post-SPICE validation of the SPICE transfer functions is ongoing, results from Caribou Creek and Bratt's Lake suggest that the SPICE transfer functions tend to over-adjust at less windy sites and under-adjust at more windy sites, consistent with the results shown by Kochendorfer et al. (2017b). Extrapolation of the transfer function performance to sites without a DFAR can only be speculative.
The 2016/2017 winter accumulated precipitation time series from
Within the CCRN program, Pan et al. (2016) recently carried out precipitation bias adjustments at several research sites in the CCRN domain; however, Bratt's Lake and Caribou Creek were not included. That analysis used a transfer function derived from a single test site to adjust precipitation measured in the much wider network of CCRN stations, resulting in an unknown uncertainty in the application. The application of the SPICE transfer functions is also not without uncertainty (as shown in Fig. 8) but one would expect that transfer functions based on multiple sites and combined data should be more widely applicable and, therefore, used for future precipitation data adjustments in cold regions. This Saskatchewan SPICE and post-SPICE data set has, and will continue to be a valuable asset for both testing and refining precipitation adjustment methodologies.
The Saskatchewan SPICE data from the winters of 2013/2014 through 2016/2017
can be found on the Government of Canada Open Data portal at:
The Bratt's Lake and Caribou Creek Saskatchewan SPICE data collected by ECCC during the winters (October through April) of 2013/2014 to 2016/2017 include the WMO DFAR as a solid precipitation reference measurement, the single Alter Geonor T-200B (which is the configuration most commonly used in the MSC climate network), a bush-shielded Geonor T-200B (at CCR only as a proxy for a bush measurement as at Valdai, Russia), wind speed at gauge height, and air temperature. Although these data are not all of the data collected during and after WMO-SPICE at CCR and XBK, they do include the core precipitation and ancillary measurements. Available on the Government of Canada Open Data portal, these data have been, and will continue to be used for instrument intercomparisons and validation of precipitation gauge transfer functions. It will be a useful data set for NWP and hydrological model validation and remote-sensing ground-truthing, with the intercomparison sites and infrastructure available for future in situ intercomparison projects.
The supplement related to this article is available online at:
CDS was responsible for overseeing the collection, quality control, archiving, and distribution of the Saskatchewan SPICE data, and managed the research activities at the Saskatchewan SPICE sites. DY provided site management support and contributed expertise for the measurement of solid precipitation. AR was responsible for the precipitation data quality control and data post-processing. AB developed and coded the NAF and NAF-S precipitation post-processing procedures documented in this paper.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Water, ecosystem, cryosphere, and climate data from the interior of Western Canada and other cold regions”. It is not associated with a conference.
The authors would like to thank all of the students and staff of the Climate Research Division and the Watershed Hydrology and Ecology Research Division of ECCC who contributed to the success of the SPICE field projects in Saskatchewan, including Cuyler Onclin, Bruce Cole, Lauren Arnold, Scott Wood, Stephnie Watson, and Emma Wattie. We appreciate the contributions of Michael Earle (ECCC) and Audrey Reverdin (MeteoSwiss) in the development of the WMO-SPICE quality control procedures and for the project leadership of Rodica Nitu (ECCC). We would especially like to extend our gratitude to the reviewers, Tedd Hogg and John Kochendorfer, and the anonymous reviewers, who provided their valuable time to help us improve this paper.
This paper was edited by Warren D. Helgason and reviewed by John Kochendorfer and one anonymous referee.