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
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.
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Variability of snowfall accumulation strongly influences the ecology and hydrology of mountainous areas and cold regions, impacting economic activities including winter tourism, hydropower generation, floods and water supply for agriculture. (Beniston, 2003; Barnett et al., 2005; Lasanta et al., 2007; Mellander et al., 2007; Jonas et al., 2008a, b; Uhlmann et al., 2009). For this reason, an accurate measurement of snowfall accumulation is critical. Moreover, suitable snowfall warnings based on reliable real-time data must be issued by the National Weather Services because snowfall disrupts transport, increases the number of traffic accidents and injuries and affects the normal function of infrastructures in inhabited areas.
It is well known that the undercatch of solid precipitation resulting from wind effects at gauge orifices is the main factor affecting the quality and accuracy of measured amounts of solid precipitation (Goodison et al., 1998). This effect can be reduced by the use of different wind shields; however, a bias still remains, and an adjustment is needed. To derive adjustment functions for different gauge and shield configurations, the test gauge needs to be compared against a standard reference configuration. During the first World Meteorological Organization (WMO) Solid Precipitation Intercomparison (Goodison and Metcalfe, 1992; Goodison et al., 1998; Yang et al., 1995, 1998a, b), the World Meteorological Organization (WMO) defined the Double Fence Intercomparison Reference (DFIR) as a secondary reference for solid precipitation to be used for intercomparisons. The DFIR consists of two concentric, octagonal wind fences paired with a manual Tretyakov precipitation gauge and wind shield (Goodison et al., 1998). Due to modernization and automation of the many different national operational networks, the variation in instrumentation has increased in the last two decades (Nitu and Wong, 2010), making it more difficult to intercompare long climate data series from different countries (Scaff et al., 2015). This is one of the reasons why a WMO Commission for Instruments and Methods of Observations (CIMO) multi-site intercomparison of instruments for the measurement of solid precipitation was initiated in 2012.
The focus of the World Meteorological Organization Solid Precipitation
Intercomparison Experiment (WMO-SPICE) is on assessing the performance of
different types of automatic precipitation gauges and configurations in
different climate regimes. WMO-SPICE has defined a reference configuration
with a DFIR shield and a single-Alter shielded automatic precipitation gauge
(Geonor T200-B3 or OTT Pluvio
Numerous studies have been conducted that have focused on the spatial variability and trends of precipitation in Spain (Begueria et al., 2009; Vicente-Serrano et al., 2011, 2015; Lopez-Moreno et al., 2010; Cortesi et al., 2014; El-Kenawy et al., 2012; Buisan et al., 2016a). All of these studies have used long-term data from Hellman gauges and, more recently, from automated tipping bucket gauges, which are the main subject of this study. With the relatively recent switch from manual gauges to the automated tipping bucket, it is now critical that both the science and operational communities have a clear understanding of how these gauges measure winter precipitation. Data users must be aware of the underestimation of precipitation during snowfall events, especially in windy environments, and be able to identify areas where the impact of underestimation is higher.
To facilitate precipitation gauge intercomparison experiments in Spain, a
WMO-SPICE site has been established by AEMET (Spanish State Meteorological
Agency) at Formigal–Sarrios (Figs. 1 and 2), located in the Pyrenees range
(Latitude: 42.76
The objective of this work is to assess the reliability and performance of the Thies automated tipping bucket gauge used in the Spanish operational network and to demonstrate the importance of accurate snowfall measurements within this network. A transfer function for the estimation of snowfall amounts by this gauge is derived from the comparison against the DFAR. Wind speed and temperature data during snowfall events were used in this analysis to help determine the potential impact of wind-induced undercatch on Spanish snowfall measurements. These results are used to identify areas within Spain where errors affecting snowfall accumulation are most significant.
List of instruments being tested at the Formigal–Sarrios WMO-SPICE site.
Layout
The Formigal–Sarrios test site is located on a small plateau at 1800 m a.s.l.
in the Pyrenees mountain range (Fig. 1). This is an alpine environment,
consisting of a mixture of bare ground and only very low grasses. Snowfalls
are frequent, with maximum measured snow depths of almost 300 cm during the
2013–2014 and 2014–2015 winter seasons. Southerly and south-westerly snowfall
events are typically associated with light winds and mild temperatures (at
approximately 0
Table 1 shows the list of instruments under test. The automatic weighing
gauge used in reference configurations is an OTT Pluvio
Orography of Spain and location of the Formigal–Sarrios site. Blue points indicate the location of automatic weather stations in the operational network.
As described in the Pluvio
Figure 1 shows the site and the distribution of instruments on the site. The height of the gauge orifice for all precipitation gauges and the disdrometer was 3.5 m. Two webcams provided real-time images of all instruments and enabled the detection of any problems, such as snow capping of gauge orifices or freezing rain.
Air temperature was measured with a PT100 from Thies and was protected by an unaspirated standard radiation screen at a height of 4.5 m. Wind was measured at a standard height of 10 m with a heated anemometer (Wind sensor, Thies Clima, Göttingen, Germany). These instruments are the same as those used in a standard AWS at AEMET. Integrated data were delivered every 1 min for all instruments and recorded using two Campbell CR1000 data loggers. The sampling frequency was different depending on the instrument, according to WMO guidelines.
A large number of snowfall events occurred during the 2014–2015 winter (December to April), providing a sufficient quantity of data for analysis. To assure high data quality, the quality control procedures removed all capping events and filtered out periods (1 and 3 h) in which less than 90 % of the 1 min precipitation data were available. All events considered as doubtful or erroneous were removed.
The orography of northern Spain, an area where the probability of snowfall is higher relative to other areas of Spain, is quite complex in terms of elevation, with an elevated plateau in the centre and numerous mountain ranges and basins surrounding the plateau (Fig. 2). The northernmost part of northern Spain, within the Pyrenees and the north side of Cantabrian Range, is characterized by narrow valleys. This region is mountainous, with numerous peaks above 3000 m a.s.l. Minor ranges such as the Iberian and Central Iberian ranges also surround the plateau, but these areas are more tabular, with less dramatic changes in elevation. In this area, the villages tend to be located in more open areas and often at a higher elevation than in the Pyrenees and Cantabrian Range, where habitation is largely in the valleys.
The AWS in the AEMET operational network are mainly located in villages and are installed according to WMO recommendations (WMO CIMO Guide, 2010). For this reason, the stations are usually in open flat areas far from obstacles (such as buildings and trees). However, during snowfall events, these locations often experience windier conditions due to exposure, which tends to result in increased undercatch of precipitation.
The long-term historical precipitation record in Spain relies mainly on Hellman rain gauges managed by collaborators, but in order to assure the continuation of these records, these gauges have been progressively replaced by automatic gauges, which are mainly tipping-bucket type gauges. The historical climate data used in this analysis to characterize automated snowfall measurement errors were retrieved from the national archive. All temperature, wind, relative humidity and precipitation data from the AEMET network are sent daily to the National Climatic Data Center, where 10 min data are available from 2009 onwards.
Automatic weather stations were not equipped with disdrometers; thus we
used temperature data to select snowfall events. For the purpose of this
analysis, snowfall events were defined as precipitation events that occurred
when the average maximum temperature was colder than 0
Event selection was focused on data from northern Spain from January 2010 to April 2015, as it is the area with the highest frequency of snowfall (and therefore the most snowfall data for analysis). The locations with more winter precipitation on average are located north of the Cantabrian Range and in the westerns areas of the Pyrenees (Pons et al., 2010; Bootey et al., 2013; Buisan et al., 2014). The selection of specific AWS within this area was limited to those for which the TPB and anemometer were heated, and the number of hourly snowfall events was greater than 75.
Accumulated precipitation, wind speed and temperature at Formigal–Sarrios during
snowfall events on
Figure 3 shows time series of accumulated precipitation measured at the
Formigal–Sarrios test field site for two different types of weather
conditions. These plots are based on 1 min data, instead of on averages over
longer periods of time, as considered in the previous WMO solid
precipitation intercomparison (Goodison et al., 1998). This higher temporal
resolution allows one to see the evolution of the accumulation reported by
different gauges and the effects of precipitation intensity, wind speed and
temperature variations with greater detail. Figure 3a shows a typical
snowfall event occurring within a southerly flow characterized by mild
temperatures and light winds. In this situation, the differences in snowfall
accumulation between the instruments located inside the DFIR and the UN and
TPB were less than 20 %, while the difference with the SA was
approximately 10 %. Figure 3b shows that under colder temperatures and
stronger winds (up to 10 m s
Figure 4 shows the number of cases classified by type of precipitation at 1 min resolution as detected by the disdrometer during the 2014–2015 winter
season. Results showed that for precipitation events at temperatures colder
than 0
Contingency tables of cases detected by each instrument over 1 (top table) and 3 h (bottom table) accumulation periods. The sum of total accumulation measured by each instrument is provided in parentheses. Note that the amount provided for the yes/yes case represents the precipitation amount measured by the reference.
Frequency distribution of precipitation type binned by temperature using 1 min data derived from the disdrometer.
The accumulated precipitation was calculated for the DFAR and tipping bucket
for each 1 h period, provided that the average temperature was colder than 0
When the TPB accumulated more precipitation in a 1 h period than the DFAR, a catch ratio (TPB/DFAR) > 1 resulted. These catch ratios > 1 likely occurred due to the delay in the melting of the snow caught by the TPB. For example, if the DFAR reports accumulation during a given 1 h period, this delay can cause the TPB to report precipitation during a subsequent 1 h period, potentially resulting in catch ratios > 1 for the subsequent 1 h period. Figure 5b shows that in 13 % of the cases, the catch ratio (TPB/DFAR) was > 1 and that these cases accounted for 9.5 % of the total precipitation recorded by the tipping bucket. Therefore, based on the hypothesis that these ratios > 1 are not likely physically realistic, the differences can be attributed to a time delay in the melting process within the bucket. This result could be considered as the percentage, on average, that is melted in the next hour. However, it is a low correction value in comparison with the differences due to the wind effect over the catch efficiency of the bucket.
To derive a suitable transfer function, only those events for which both the
TPB and the DFAR detected precipitation and the TPB/DFAR catch ratio was
lower than 1 were considered. Figure 6 shows the relationship between this
catch ratio and wind speed. At wind speeds lower than 2 m s
Derived transfer functions in which each step adds a new variable.
Top table is for 1 h period and bottom table is for 3 h period. The value of
the coefficient of determination (
Given the non-linear dependence of catch ratio on wind speed and, following
a similar procedure from recent studies (Goodison et al., 1998; Rasmussen et al., 2012; Theriault et al., 2012; Wolff et al., 2015), an exponential curve
was fit to the snow event data. Wind speed was found to explain more than
50 % of the variance. However, as shown in Fig. 6, at temperatures
colder than
Since it is not possible to know operationally how much snow is melted from the previous hour of precipitation, and in order to derive an operational transfer function, we propose the following approach: implement a “melting factor” of 0.095 to correct for the average amount of snowfall that falls in the current hour but is not melted until the next hour. This value was determined by calculating the correlation between the hourly TPB measurements and the DFAR measurements for all “melting factors” between 0 and 30 %, and then creating a correlogram (Fig. 7). A peak in the correlation was associated with a melting factor of 9.5 %, where 9.5 % of the Thies precipitation from a given hour was assumed to have melted in the following hour. The proposed equation to derive the “true” snowfall amount in the operational network for 1 h time periods is given in Table 3, Eq. (4). This simple equation can easily be implemented operationally and can improve the estimation of snowfall accumulation measured with the TPB. It is important to remember that analysis has shown that the error associated with this melting factor only accounts for, on average, less than 10 % of the true accumulation, and that the undercatch of precipitation due to other factors is the main source of error.
Following the same methodology, we considered snowfall during 3 h time
periods, and only including events with a maximum temperature colder than 0
To estimate the uncertainty of the proposed equations we split the 1 and 3 h data sets each into two equal and independent data sets. One data set was
used to calculate the regression equations (114 events for 1 h period, 45
events for 3 h period). The resulting equations were similar to those
obtained using the entire data set. The accuracy of the resultant regressions
was then independently evaluated using the second subsample of each data set
(100 events for 1 h period, 42 events for 3 h period). For the 1 h data set
the resultant RMSE was 0.13, and for the 3 h data set it was 0.11. These
values are acceptable given that the
After demonstrating the magnitude of TPB snowfall measurement errors and
developing methodologies to address these errors, the areas within Spain
where the impact of these adjustments will be most significant can be
identified. Hereafter, we will use the units of km h
The relationship between catch ratio (TPB/DFAR) and wind speed for accumulation periods of
Correlation between the hourly TPB measurements and the DFAR measurements for different melting factors, where melting factor is the percentage of the Thies tipping bucket precipitation from a given hour melted in the following hour.
Percentage of 1 h snowfall events per station at different wind speed intervals.
Figure 8 shows frequency distributions of 1 h average wind speeds during
snowfall at sites in northern Spain. In the Cantabrian and Pyrenees ranges,
most stations show that 60 % of the events occur during light winds or
between 0 and 10 km h
Average station wind speed during 1 h snowfall events.
Figure 10 shows the average temperature during snowfall events. The stations located in the Pyrenees and in some areas of the Iberian range are located at higher elevation and, for this reason, the temperature is on average lower during snowfall. As demonstrated previously in Sect. 3.1, the catch ratio decreases more rapidly with increasing wind speed at lower temperatures.
Using the derived transfer function (Eq. 2, Table 3), the average catch
ratio for each station was calculated for all 1 h snowfall events (Fig. 11). The snowfall accumulation for stations located in the Pyrenees and
Cantabrian ranges was underestimated by less than 50 %, whereas for
stations located in the most elevated areas of the plateau and in the
Iberian range, the underestimation ranged from 50 to 70 %. It is
noteworthy that at stations characterized by light winds, the undercatch at
sites with lower average temperatures was higher than that for stations with
higher average temperatures. This was the case for some stations in the
Pyrenees range in comparison with stations in the Cantabrian Range that are
located at a lower elevation, and have more snowfall events at temperatures
near 0
Average station temperature during 1 h snowfall events. The colours represent temperature ranges with different (minimum, maximum) temperatures as indicated in the legend.
The Formigal–Sarrios test site provided a unique opportunity to test the performance of the AEMET operational tipping bucket gauge as well as other gauges within the framework of the WMO-SPICE project (Buisan et al., 2016b; Nitu et al., 2015). The large number of snowfall events during the 2014/2015 winter season provided a data set encompassing a wide range of temperature and wind speed conditions. Intercomparison with the DFAR showed that in snow, the performance of the TPB was similar for accumulation periods of 1 and 3 h, with similar catch ratio relationships for both accumulation periods.
The main factor affecting the underestimation of precipitation was the wind
speed, especially for cold events. At wind speeds below 4 m s
One factor that was not included in the analysis was the impact of heating on the evaporation or sublimation of incident precipitation producing losses in the TPB accumulation, especially at low intensities (Zweifel and Sevruk, 2002; Savina et al., 2012). The heating power of the model of tipping bucket used operationally and tested in Formigal was only 49 W (in comparison with other models of tipping bucket, which have different heating configurations, some with power greater than 100 W) and snowfall events at Formigal are usually characterized by high intensities. Based on this, we could consider that, in addition to the impact on catch efficiency already included in transfer functions, longer delays on the melting process could be expected. For this reason, the choice of 1 and 3 h time periods was considered a good option.
Average undercatch of precipitation for 1 h snowfall events at each station.
Despite the difficulty of discriminating rain from snow (Harder and Pomeroy,
2014), the upper threshold temperature of 0
Wind speed was measured at the standard operational height of 10 m, instead of at gauge height. The main advantages are that measurements are less affected by obstacles, and that all stations in the operational network measure wind at this height. This allows for broader applicability of the derived transfer functions within the network. Previous work has shown only small improvements in the accuracy of results using the gauge height wind speed relative to using the 10 m wind speed (Kochendorfer et al., 2017).
From a national perspective, it is crucial to identify areas where the
underestimation of precipitation can potentially have significant impact. A
study of the climatic data set from the national archive for 2010 revealed
two areas in northern Spain that exhibit different levels of underestimation
during snowfall events. The Pyrenees and the north side of the Cantabrian
Range were characterized by higher catch ratios than the elevated areas
of the Iberian plateau. It therefore appears that the undercatch of snow was
more significant at higher elevations (i.e. on the slopes). However, in
terms of the total water equivalent that is not accounted for, it is likely
the northern areas and the Pyrenees range experience more total undercatch
because of the relatively large portion of winter precipitation occurring in
these mountains as snowfall (Pons et al., 2010; Bootey et al., 2013; Buisan et al., 2014). It is also important to note that, in general, snowfall in Spain
occurs very infrequently at temperatures colder than
These adjustment functions will also help forecasters to infer, in near real time, the degree of danger during a snowfall event that could otherwise be significantly underestimated by the uncorrected TPB measurement. This in turn will result in more accurate warnings. An accurate assessment of the available snow water equivalent is critical to activate mechanisms to reduce the impact of the risk of floods associated with the rapid melting of snow at lower elevations (below 1500 m a.s.l.); for example, after a heavy snowfall event followed by an increase of temperature or rainfall episode. These results are therefore a first step forward in improving the precipitation input for hydrological models.
Within the Spanish climate record, winter precipitation is persistently underestimated, especially in areas subject to frequent snowfall (Pons et al., 2010; Buisan et al., 2014). This underestimation could affect previous studies of solid precipitation, especially if the period of time considered was associated with significant winter precipitation extremes (López-Moreno et al., 2011; Vicente-Serrano et al., 2011; Añel et al., 2014; Cortesi et al., 2014; Buisan et al., 2016a). Adjustment functions for the Hellman gauges (Goodison et al., 1998) traditionally used by AEMET and the transfer functions obtained in this study for the gauges used currently should be used to assess the actual precipitation trends in Spain.
This is the first study describing the underestimation of winter precipitation in Spain, and as such, it is a first step that has important applications in many different research (i.e. climatology, numerical modelling) and operational (i.e. nowcasting, hydrology) fields. Further research is needed, however, to obtain better corrections, to more accurately describe correction uncertainty using in situ validation and to define temperature thresholds that can be used to identify snowfall events for different locations. Preliminary tests of the transfer functions determined in this study were performed by the Spanish hydrological service. The response of hydrological models was significantly improved when initialized using the adjusted precipitation measurements.
Furthermore, the observed measurements of snow depth and liquid water equivalent recorded by observers in selected AEMET stations (i.e. Cubillo de Ebro, Cantabria; Mosqueruela, Teruel; Lalastra, Alava; and Sargentes de Lora, Burgos) during snowfall episodes agreed well with the derived precipitation when the transfer functions were applied. For example, based on manual snow depth measurements at the Lalastra station, the total liquid water equivalent for the 3–6 February 2015 blizzard was estimated to be between 150 and 250 mm. The gauge only measured 81.8 mm at this station, but after adjustment the corrected precipitation was 233.4 mm.
Finally, and perhaps most importantly, most countries use tipping buckets without shields in their operational networks (Nitu and Wong, 2010), and for this reason the underestimation of snowfall precipitation is a ubiquitous problem. The methodology presented here can be used by other national weather and hydrological services to test precipitation bias corrections and to identify regions where errors affecting snowfall accumulation are most significant.
The results of this study demonstrate that a transfer function between the
Thies tipping bucket precipitation gauge and the SPICE reference can be
derived for accumulated precipitation amounts over 1 and 3 h time intervals.
Wind is the most dominant environmental variable affecting the gauge catch
efficiency, especially at temperatures colder than
The authors declare that they have no conflict of interest.
Thanks to all members of the SPICE team for their continuous advice and contributions to this challenging project. Special thanks to the Formigal Ski Resort for its support in the installation of the site and in situ help and maintenance. Thanks to all colleagues of AEMET, especially to those who helped us from the beginning with this project, both in the Aragon Regional Office and at the headquarters, and to collaborators for their work in operating our observing network. We would also like to acknowledge the AEMET infrastructure division in Castilla-León, Cantabria and País Vasco for providing contacts of collaborators who were able to provide us with information on snowfall events. Thanks to the Ebro basin hydrological service for performing the requested simulation. Thanks to Javier Zabalza for his support plotting maps. Edited by: S. Morin Reviewed by: S. R. Fassnacht and E. Lanzinger