Solid Precipitation Intercomparison Experiment Research Articles

Correction of precipitation measurement for weighing precipitation gauges in a glacierized basin in the Tianshan Mountains

Precipitation is one of the most important climatological data for global hydrothermal cycle and climate change. The accuracy of precipitation data not only directly affects the hydrological processes, but also plays an important role in the climate and hydrology at regional and global scales. According to the in situ datasets, the precipitation measurement in automatic weather stations for Geonor T-200B was corrected by the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE) transfer functions. The parameters of transfer functions were tested and recalibrated by the local datasets. The results showed that the transfer functions showed better performance after recalibrating parameters by the local datasets. The root-mean-square error (RMSE) and mean bias decreased by an average of 34% and 42%, respectively. The corrected snowfall increased by 7% (14 mm) at the test station. Then, the new parameters were used in other automatic weather stations to correct precipitation, and it was found that solid precipitation was underestimated by 13% on the glacier surface affected by wind speed. Moreover, according to the corrected precipitation datasets observed in automatic weather stations and national meteorological stations, the precipitation–altitude relationship in the Urumqi River Basin was analyzed. The annual precipitation gradient was 115 mm km-1, and the maximum seasonal altitude occurred in summer with a value of 35 mm km-1 and in autumn with the lowest value of 1 mm km-1. When considering precipitation on the glacier surface, the yearly precipitation gradient was increased with the value of 158 mm km -1 in 2019.

The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)

Abstract. The measurement of precipitation in the Environment and Climate Change Canada (ECCC) surface network is a crucial component for climate and weather monitoring, flood and water resource forecasting, numerical weather prediction, and many other applications that impact the health and safety of Canadians. Through the late 1990s and early 2000s, the ECCC surface network modernization resulted in a shift from manual to automated precipitation measurements. Although many advantages to automation are realized, such as enhanced capabilities for monitoring in remote locations and a higher frequency of observations at lower cost, the increased reliance on automated precipitation gauges has also resulted in additional challenges, especially with data quality and homogenization. The automated weighing precipitation gauges used in the ECCC operational network have an increased propensity for wind-induced undercatch of solid precipitation. One outcome of the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) was the development of transfer functions for the adjustment of high-frequency solid precipitation measurements made with gauge/wind shield configurations used in the ECCC surface network. Using the SPICE universal transfer function (UTF), hourly precipitation measurements from 397 ECCC automated climate stations were retroactively adjusted for wind undercatch. The data format, quality control, and adjustment procedures are described here. The hourly adjusted data set (2001–2019; version v2019UTF) is available via the ECCC data catalogue at https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52 (ECCC, 2021). A basic spatial impact assessment shows that the highest relative total precipitation adjustments occur in the Arctic, where solid precipitation has an overall higher annual occurrence ratio. The highest adjustments for solid precipitation are shared by the Arctic, Southern Prairies, and the coastal Maritimes, where stations tend to be more exposed and snowfall events occur at higher wind speeds.

Adjustment of Solid Precipitation during the Filomena Extreme Snowfall Event in Spain: From Observations to “True Precipitation”

Abstract On January 2021, the heaviest snowfall in five decades hit central Spain, especially affecting Madrid. The city’s Barajas International Airport closed, along with a number of roads, and all trains to and from Madrid were cancelled. This storm was named Filomena by the Spanish Meteorological Agency (AEMET), and produced continuous snowfall in Spain on 7–10 January. The observed snow depth was around 50 cm in 24 h in Madrid, and even higher in other areas of Spain. However, the measured accumulation of national precipitation gauges was not consistent with the observed accumulated snow on the ground and with the modeled weather forecast. The undercatch of solid precipitation was the primary reason for this inconsistency. This undercatch was quantified using transfer functions developed from the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE). Results show that an underestimation of 20%–30% of solid precipitation in large areas of Spain was observed, with some areas experiencing even larger differences. Without adjustments, it was impossible to accurately validate the model forecast. The adjusted precipitation was also more realistically distributed, and it was more consistent with all the damage that occurred. The same methods can be applied to other snowfall events occurring anywhere in the world, and also using different precipitation gauges and/or models. This an example of the type of extreme events that modelers, forecasters, and climatologists should be aware of to avoid misinterpreting differences between modeled precipitation, observed precipitation, and nowcasting.

How Well Are We Measuring Snow Post-SPICE?

Abstract Accurate snowfall measurements are necessary for meteorology, hydrology, and climate research. Typical uses include creating and calibrating gridded precipitation products, the verification of model simulations, driving hydrologic models, input into aircraft deicing processes, and estimating streamflow runoff in the spring. These applications are significantly impacted by errors in solid precipitation measurements. The recent WMO Solid Precipitation Intercomparison Experiment (SPICE) attempted to characterize and reduce some of the measurement uncertainties through an international effort involving 15 countries utilizing over 20 types and models of precipitation gauges from various manufacturers. Key results from WMO-SPICE are presented herein. Recent work and future research opportunities that build on the results of WMO-SPICE are also highlighted.

Improvement of Snow Gauge Collection Efficiency through a Knowledge of Solid Precipitation Fall Speed

AbstractThe collection efficiency of a typical precipitation gauge-shield configuration decreases with increasing wind speed, with a high scatter for a given wind speed. The high scatter in the collection efficiency for a given wind speed arises in part from the variability in the characteristics of falling snow and atmospheric turbulence. This study uses weighing gauge data collected at the Marshall Field Site near Boulder, Colorado, during the WMO Solid Precipitation Intercomparison Experiment (SPICE). Particle diameter and fall speed data from a laser disdrometer were used to show that the scatter in the collection efficiency can be reduced by considering the fall speed of solid precipitation particles. The collection efficiency was divided into two classes depending on the measured mean-event particle fall speed during precipitation events. Slower-falling particles were associated with a lower collection efficiency. A new transfer function (i.e., the relationship between collection efficiency and other meteorological variables, such as wind speed or air temperature) that includes the fall speed of the hydrometeors was developed. The root-mean-square error of the adjusted precipitation with the new transfer function with respect to a weighing gauge placed in a double fence intercomparison reference was lower than using previously developed transfer functions that only consider wind speed and air temperature. This shows that the measured fall speed of solid precipitation with a laser disdrometer accounts for a large amount of the observed scatter in weighing gauge collection efficiency.

Improvement of Solid Precipitation Measurements Using a Hotplate Precipitation Gauge

AbstractAccurate snowfall measurement is challenging because it depends on the precipitation gauge used, meteorological conditions, and the precipitation microphysics. Upstream of weighing gauges, the flow field is disturbed by the gauge and any shielding used usually creates an updraft, which deflects solid precipitation from falling in the gauge, resulting in significant undercatch. Wind shields are often used with weighing gauges to reduce this updraft, and transfer functions are required to adjust the snowfall measurements to consider gauge undercatch. Using these functions reduces the bias in precipitation measurement but not the root-mean-square error (RMSE). In this study, the accuracy of the Hotplate precipitation gauge was compared to standard unshielded and shielded weighing gauges collected during the WMO Solid Precipitation Intercomparison Experiment program. The analysis performed in this study shows that the Hotplate precipitation gauge bias after wind correction is near zero and similar to wind corrected weighing gauges. The RMSE of the Hotplate precipitation gauge measurements is lower than weighing gauges (with or without an Alter shield) for wind speeds up to 5 m s−1, the wind speed limit at which sufficient data were available. This study shows that the Hotplate precipitation gauge measurement has a low bias and RMSE due to its aerodynamic shape, making its performance mostly independent of the type of solid precipitation.

Evaluation of the WMO Solid Precipitation Intercomparison Experiment (SPICE) transfer functions for adjusting the wind bias in solid precipitation measurements

Abstract. The World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) involved extensive field intercomparisons of automated instruments for measuring snow during the 2013/2014 and 2014/2015 winter seasons. A key outcome of SPICE was the development of transfer functions for the wind bias adjustment of solid precipitation measurements using various precipitation gauge and wind shield configurations. Due to the short intercomparison period, the data set was not sufficiently large to develop and evaluate transfer functions using independent precipitation measurements, although on average the adjustments were effective at reducing the bias in unshielded gauges from −33.4 % to 1.1 %. The present analysis uses data collected at eight SPICE sites over the 2015/2016 and 2016/2017 winter periods, comparing 30 min adjusted and unadjusted measurements from Geonor T-200B3 and OTT Pluvio2 precipitation gauges in different shield configurations to the WMO Double Fence Automated Reference (DFAR) for the evaluation of the transfer function. Performance is assessed in terms of relative total catch (RTC), root mean square error (RMSE), Pearson correlation (r), and percentage of events (PEs) within 0.1 mm of the DFAR. Metrics are reported for combined precipitation types and for snow only. The evaluation shows that the performance varies substantially by site. Adjusted RTC varies from 54 % to 123 %, RMSE from 0.07 to 0.38 mm, r from 0.28 to 0.94, and PEs from 37 % to 84 %, depending on precipitation phase, site, and gauge configuration (gauge and wind screen type). Generally, windier sites, such as Haukeliseter (Norway) and Bratt's Lake (Canada), exhibit a net under-adjustment (RTC of 54 % to 83 %), while the less windy sites, such as Sodankylä (Finland) and Caribou Creek (Canada), exhibit a net over-adjustment (RTC of 102 % to 123 %). Although the application of transfer functions is necessary to mitigate wind bias in solid precipitation measurements, especially at windy sites and for unshielded gauges, the variability in the performance metrics among sites suggests that the functions be applied with caution.

An improved post-processing technique for automatic precipitation gauge time series

Abstract. The unconditioned data retrieved from accumulating automated weighing precipitation gauges are inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: (1) the operational 15 min filter (O15), (2) the neutral aggregating filter (NAF), (3) the supervised neutral aggregating filter (NAF-S), and (4) the segmented neutral aggregating filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation primarily focuses on cold-season (October to April) accumulating automated weighing precipitation gauge data at 1 min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift and raw (minimally processed) data from several WMO Solid Precipitation Intercomparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99 %, 100 %, and 102 % of the control total precipitation for low-, medium-, and high-noise scenarios respectively for the cold-season (October–April) and 97 % of the control total precipitation for all noise scenarios in the warm season (May–September). Among the filters, the fully automated NAF-SEG produced the highest correlation coefficients and lowest root-mean-square error (RMSE) for all synthetic noise levels, with comparable performance to the supervised and manually intensive NAF-S method. Compared to the O15 method in cold-season testing, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. In warm-season testing, the NAF-SEG bias was lower or similar in 7 of 11 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24 h latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in combination with a simple real-time filter, such as the O15 or similar filter.

Undercatch Adjustments for Tipping-Bucket Gauge Measurements of Solid Precipitation

AbstractHeated tipping-bucket (TB) gauges are used broadly in national weather monitoring networks, but their performance for the measurement of solid precipitation has not been well characterized. Manufacturer-provided TB gauges were evaluated at five test sites during the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), with most gauge types tested at more than one site. The test results were used to develop and evaluate adjustments for the undercatch of solid precipitation by heated TB gauges. New methods were also developed to address challenges specific to measurements from heated TB gauges. Tipping-bucket transfer functions were created specifically to minimize the sum of errors over the course of the adjusted multiseasonal accumulation. This was based on the hypothesis that the best transfer function produces the most accurate long-term precipitation records, rather than accurate catch efficiency measurements or accurate daily or hourly precipitation measurements. Using this new approach, an adjustment function derived from multiple gauges was developed that performed better than traditional gauge-specific and multigauge catch efficiency derived adjustments. Because this new multigauge adjustment was developed using six different types of gauges tested at five different sites, it may be applicable to solid precipitation measurements from unshielded heated TB gauges that were not evaluated in WMO-SPICE. In addition, this new method of optimizing transfer functions may be useful for other types of precipitation gauges, as it has many practical advantages over the traditional catch efficiency methods used to derive undercatch adjustments.

Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity

AbstractAdjustments for the wind-induced undercatch of snowfall measurements use transfer functions to account for the expected reduction of the collection efficiency with increasing the wind speed for a particular catching-type gauge. Based on field experiments or numerical simulation, collection efficiency curves as a function of wind speed also involve further explanatory variables such as surface air temperature and/or precipitation type. However, while the wind speed or wind speed and temperature approach is generally effective at reducing the measurement bias, it does not significantly reduce the root-mean-square error (RMSE) of the residuals, implying that part of the variance is still unexplained. In this study, we show that using precipitation intensity as the explanatory variable significantly reduces the scatter of the residuals. This is achieved by optimized curve fitting of field measurements from the Marshall Field Site (Colorado, United States), using a nongradient optimization algorithm to ensure optimal binning of experimental data. The analysis of a recent quality-controlled dataset from the Solid Precipitation Intercomparison Experiment (SPICE) campaign of the World Meteorological Organization confirms the scatter reduction, showing that this approach is suitable to a variety of locations and catching-type gauges. Using computational fluid dynamics simulations, we demonstrate that the physical basis of the reduction in RMSE is the correlation of precipitation intensity with the particle size distribution. Overall, these findings could be relevant in operational conditions since the proposed adjustment of precipitation measurements only requires wind sensor and precipitation gauge data.

The Environment and Climate Change Canada solid precipitation intercomparison data from Bratt's Lake and Caribou Creek, Saskatchewan

Abstract. 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 https://doi.org/10.18164/63773b5b-5529-4b1e-9150-10acb84d59f0 (Smith and Yang, 2018). The data collected at the Saskatchewan SPICE sites will continue to be useful for transfer function testing, numerical weather prediction and hydrological forecasting verification, ground truth for remote-sensing applications, as well as providing reference precipitation measurements for other concurrent research applications in the cold regions.

Testing and development of transfer functions for weighing precipitation gauges in WMO-SPICE

Abstract. Weighing precipitation gauges are used widely for the measurement of all forms of precipitation, and are typically more accurate than tipping-bucket precipitation gauges. This is especially true for the measurement of solid precipitation; however, weighing precipitation gauge measurements must still be adjusted for undercatch in snowy, windy conditions. In WMO-SPICE (World Meteorological Organization Solid Precipitation InterComparison Experiment), different types of weighing precipitation gauges and shields were compared, and adjustments were determined for the undercatch of solid precipitation caused by wind. For the various combinations of gauges and shields, adjustments using both new and previously existing transfer functions were evaluated. For most of the gauge and shield combinations, previously derived transfer functions were found to perform as well as those more recently derived. This indicates that wind shield type (or lack thereof) is more important in determining the magnitude of wind-induced undercatch than the type of weighing precipitation gauge. It also demonstrates the potential for widespread use of the previously developed transfer functions. Another overarching result was that, in general, the more effective shields, which were associated with smaller unadjusted errors, also produced more accurate measurements after adjustment. This indicates that although transfer functions can effectively reduce measurement biases, effective wind shielding is still required for the most accurate measurement of solid precipitation.

Measuring precipitation with a geolysimeter

Abstract. Using the relationship between measured groundwater pressures in deep observation wells and total surface loading, a geological weighing lysimeter (geolysimeter) has the capability of measuring precipitation event totals independently of conventional precipitation gauge observations. Correlations between groundwater pressure change and event precipitation were observed at a co-located site near Duck Lake, SK, over a multi-year and multi-season period. Correlation coefficients (r2) varied from 0.99 for rainfall to 0.94 for snowfall. The geolysimeter was shown to underestimate rainfall by 7 % while overestimating snowfall by 9 % as compared to the unadjusted gauge precipitation. It is speculated that the underestimation of rainfall is due to unmeasured run-off and evapotranspiration within the response area of the geolysimeter during larger rainfall events, while the overestimation of snow is at least partially due to the systematic undercatch common to most precipitation gauges due to wind. Using recently developed transfer functions from the World Meteorological Organization's (WMO) Solid Precipitation Intercomparison Experiment (SPICE), bias adjustments were applied to the Alter-shielded, Geonor T-200B precipitation gauge measurements of snowfall to mitigate wind-induced errors. The bias between the gauge and geolysimeter measurements was reduced to 3 %. This suggests that the geolysimeter is capable of accurately measuring solid precipitation and can be used as an independent and representative reference of true precipitation.

Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE

Abstract. Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant inaccuracies. Solid precipitation is particularly difficult to measure accurately, and wintertime precipitation measurement biases between different observing networks or different regions can exceed 100 %. Using precipitation gauge results from the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, crystal habit, and wind were quantified. The methods used to calculate gauge catch efficiency and correct known biases are described. Adjustments, in the form of transfer functions that describe catch efficiency as a function of air temperature and wind speed, were derived using measurements from eight separate WMO-SPICE sites for both unshielded and single-Alter-shielded precipitation-weighing gauges. For the unshielded gauges, the average undercatch for all eight sites was 0.50 mm h−1 (34 %), and for the single-Alter-shielded gauges it was 0.35 mm h−1 (24 %). After adjustment, the mean bias for both the unshielded and single-Alter measurements was within 0.03 mm h−1 (2 %) of zero. The use of multiple sites to derive such adjustments makes these results unique and more broadly applicable to other sites with various climatic conditions. In addition, errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites were estimated.

The quantification and correction of wind-induced precipitation measurement errors

Abstract. Hydrologic measurements are important for both the short- and long-term management of water resources. Of the terms in the hydrologic budget, precipitation is typically the most important input; however, measurements of precipitation are subject to large errors and biases. For example, an all-weather unshielded weighing precipitation gauge can collect less than 50 % of the actual amount of solid precipitation when wind speeds exceed 5 m s−1. Using results from two different precipitation test beds, such errors have been assessed for unshielded weighing gauges and for weighing gauges employing four of the most common windshields currently in use. Functions to correct wind-induced undercatch were developed and tested. In addition, corrections for the single-Alter weighing gauge were developed using the combined results of two separate sites in Norway and the USA. In general, the results indicate that the functions effectively correct the undercatch bias that affects such precipitation measurements. In addition, a single function developed for the single-Alter gauges effectively decreased the bias at both sites, with the bias at the US site improving from −12 to 0 %, and the bias at the Norwegian site improving from −27 to −4 %. These correction functions require only wind speed and air temperature as inputs, and were developed for use in national and local precipitation networks, hydrological monitoring, roadway and airport safety work, and climate change research. The techniques used to develop and test these transfer functions at more than one site can also be used for other more comprehensive studies, such as the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE).

Assessment of snowfall accumulation underestimation by tipping bucket gauges in the Spanish operational network

Abstract. Within the framework of the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), the Thies tipping bucket precipitation gauge was assessed against the SPICE reference configuration at the Formigal–Sarrios test site located in the Pyrenees mountain range of Spain. The Thies gauge is the most widely used precipitation gauge by the Spanish Meteorological State Agency (AEMET) for the measurement of all precipitation types including snow. It is therefore critical that its performance is characterized. The first objective of this study is to derive transfer functions based on the relationships between catch ratio and wind speed and temperature. Multiple linear regression was applied to 1 and 3 h accumulation periods, confirming that wind is the most dominant environmental variable affecting the gauge catch efficiency, especially during snowfall events. At wind speeds of 1.5 m s−1 the tipping bucket recorded only 70 % of the reference precipitation. At 3 m s−1, the amount of measured precipitation decreased to 50 % of the reference, was even lower for temperatures colder than −2 °C and decreased to 20 % or less for higher wind speeds.The implications of precipitation underestimation for areas in northern Spain are discussed within the context of the present analysis, by applying the transfer function developed at the Formigal–Sarrios and using results from previous studies.

An assessment of two automated snow water equivalent instruments during the WMO Solid Precipitation Intercomparison Experiment

Abstract. During the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE), automated measurements of snow water equivalent (SWE) were made at the Sodankylä (Finland), Weissfluhjoch (Switzerland) and Caribou Creek (Canada) SPICE sites during the northern hemispheric winters of 2013/14 and 2014/15. Supplementary intercomparison measurements were made at Fortress Mountain (Kananaskis, Canada) during the 2013/14 winter. The objectives of this analysis are to compare automated SWE measurements with a reference, comment on their performance and, where possible, to make recommendations on how to best use the instruments and interpret their measurements. Sodankylä, Caribou Creek and Fortress Mountain hosted a Campbell Scientific CS725 passive gamma radiation SWE sensor. Sodankylä and Weissfluhjoch hosted a Sommer Messtechnik SSG1000 snow scale. The CS725 operating principle is based on measuring the attenuation of soil emitted gamma radiation by the snowpack and relating the attenuation to SWE. The SSG1000 measures the mass of the overlying snowpack directly by using a weighing platform and load cell. Manual SWE measurements were obtained at the intercomparison sites on a bi-weekly basis over the accumulation–ablation periods using bulk density samplers. These manual measurements are considered to be the reference for the intercomparison. Results from Sodankylä and Caribou Creek showed that the CS725 generally overestimates SWE as compared to manual measurements by roughly 30–35 % with correlations (r2) as high as 0.99 for Sodankylä and 0.90 for Caribou Creek. The RMSE varied from 30 to 43 mm water equivalent (mm w.e.) and from 18 to 25 mm w.e. at Sodankylä and Caribou Creek, which had respective SWE maximums of approximately 200 and 120 mm w.e. The correlation at Fortress Mountain was 0.94 (RMSE of 48 mm w.e. with a maximum SWE of approximately 650 mm w.e.) with no systematic overestimation. The SSG1000 snow scale, having a different measurement principle, agreed quite closely with the manual measurements at Sodankylä and Weissfluhjoch throughout the periods when data were available (r2 as high as 0.99 and RMSE from 8 to 24 mm w.e. at Sodankylä and from 56 to 59 mm w.e. at Weissfluhjoch, where maximum SWE was approximately 850 mm w.e.). When the SSG1000 was compared to the CS725 at Sodankylä, the agreement was linear until the start of ablation when the positive bias in the CS725 increases substantially relative to the SSG1000. Since both Caribou Creek and Sodankylä have sandy soil, water from the snowpack readily infiltrates into the soil during melt, even if the soil is frozen. However, the CS725 does not differentiate this water from the unmelted snow. This issue can be identified, at least during the late spring ablation period, with soil moisture and temperature observations like those measured at Caribou Creek. With a less permeable soil and surface runoff, the increase in the instrument bias during ablation is not as significant, as shown by the Fortress Mountain intercomparison.