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

Buisán, Samuel; Earle, Michael E.; Collado, José Luis; Kochendorfer, John; Alastrué, Javier; Wolff, Mareile; Smith, Charles W.; López‐Moreno, Juan I.

doi:10.5194/amt-10-1079-2017

Cited by 42 publications

(62 citation statements)

References 26 publications

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“…However, ρ HN values span a wide range, and values from 10 to 350 kg m −3 have been reported from American and European mountain ranges, with mean values between 70 and 110 kg m −3 (e.g. Diamond and Lowry, 1954;LaChapelle, 1962;Power et al, 1964;Judson, 1965;McKay et al, 1981;Meister, 1985;Judson and Doesken, 2000;Valt et al, 2014). Most of the ρ HN data analysed in these studies were observed using readings on a snow board.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies analysed ρ HN values on a daily basis and confirmed this 10 : 1 rule as applicable for a first estimate (e.g. Roebber et al, 2003;Egli et al, 2009;Teutsch, 2009). However, ρ HN values span a wide range, and values from 10 to 350 kg m −3 have been reported from American and European mountain ranges, with mean values between 70 and 110 kg m −3 (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Buisán et al, 2016;Pan et al, 2016). Many snow cover models calculate HN from HNW at sub-daily time intervals, although reliable HNW input data are difficult to obtain (Egli et al, 2009), and thus the new snow density is needed in equal temporal resolution to convert between HNW and HN (e.g. Lehning et al, 2002;Roebber et al, 2003;Olefs et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Beside snow depth (HS), the water equivalent of the snowpack (SWE) is observed operationally using weighing devices such as lysimetric snow pillows (e.g. Serreze et al, 1999;Egli et al, 2009;Lundberg et al, 2010;Krajči et al, 2017) and snow scales (e.g. http://www.…”

Section: Introductionmentioning

confidence: 99%

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Obtaining sub-daily new snow density from automated measurements in high mountain regions

Hartl

Koch

Marty³

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The density of new snow is operationally monitored by meteorological or hydrological services at daily time intervals, or occasionally measured in local field studies. However, meteorological conditions and thus settling of the freshly deposited snow rapidly alter the new snow density until measurement. Physically based snow models and nowcasting applications make use of hourly weather data to determine the water equivalent of the snowfall and snow depth. In previous studies, a number of empirical parameterizations were developed to approximate the new snow density by meteorological parameters. These parameterizations are largely based on new snow measurements derived from local in situ measurements. In this study a data set of automated snow measurements at four stations located in the European Alps is analysed for several winter seasons. Hourly new snow densities are calculated from the height of new snow and the water equivalent of snowfall. Considering the settling of the new snow and the old snowpack, the average hourly new snow density is 68 kg m −3 , with a standard deviation of 9 kg m −3 . Seven existing parameterizations for estimating new snow densities were tested against these data, and most calculations overestimate the hourly automated measurements. Two of the tested parameterizations were capable of simulating low new snow densities observed at sheltered inner-alpine stations. The observed variability in new snow density from the automated measurements could not be described with satisfactory statistical significance by any of the investigated parameterizations. Applying simple linear regressions between new snow density and wet bulb temperature based on the measurements' data resulted in significant relationships (r 2 > 0.5 and p ≤ 0.05) for single periods at individual stations only. Higher new snow density was calculated for the highest elevated and most wind-exposed station location. Whereas snow measurements using ultrasonic devices and snow pillows are appropriate for calculating station mean new snow densities, we recommend instruments with higher accuracy e.g. optical devices for more reliable investigations of the variability of new snow densities at sub-daily intervals.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Obtaining sub-daily new snow density from automated measurements in high mountain regions

Hartl

Koch

Marty³

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The collection efficiency of precipitation gauges is influenced by the wind speed and a bias adjustment for solid precipitation is needed under windy conditions Buisán et al, 2017;Smith et al, 2017;Pan et al, 2016). Measurement errors due to gauge undercatch frequently range between 20 and 50 % .…”

Section: Correcting For Geonor Undercatchmentioning

confidence: 99%

Snow observations in Mount Lebanon (2011–2016)

Fayad

Gascoin

Faour

et al. 2017

Earth Syst. Sci. Data

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Abstract. We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km 2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011)(2012)(2013)(2014)(2015)(2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m −3 . We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean.

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The potential for uncertainty in Numerical Weather Prediction model verification when using solid precipitation observations

Buisán

Smith

Ross

et al. 2020

Atmospheric Science Letters

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Precipitation forecasts made by Numerical Weather Prediction (NWP) models are typically verified using precipitation gauge observations that are often prone to the wind-induced undercatch of solid precipitation. Therefore, apparent model biases in solid precipitation forecasts may be due in part to the measurements and not the model. To reduce solid precipitation measurement biases, adjustments in the form of transfer functions were derived within the framework of the World Meteorological Organization Solid Precipitation Inter-Comparison Experiment (WMO-SPICE). These transfer functions were applied to single-Alter shielded gauge measurements at selected SPICE sites during two winter seasons (2015-2016 and 2016-2017). Along with measurements from the WMO automated field reference configuration at each of these SPICE sites, the adjusted and unadjusted gauge observations were used to analyze the bias in a Global NWP model precipitation forecast. The verification of NWP winter precipitation using operational gauges may be subject to verification uncertainty, the magnitude and sign of which varies with the gauge-shield configuration and the relation between model and site-specific local climatologies. The application of a transfer function to alter-shielded gauge measurements increases the amount of solid precipitation reported by the gauge and therefore reduces the NWP precipitation bias at sites where the model tends to overestimate precipitation, and increases the bias at sites where the model underestimates the precipitation. This complicates model verification when only operational (non-reference) gauge observations are available. Modelers, forecasters, and climatologists must consider this when comparing modeled and observed precipitation.

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

Cited by 42 publications

References 26 publications

Obtaining sub-daily new snow density from automated measurements in high mountain regions

Obtaining sub-daily new snow density from automated measurements in high mountain regions

Snow observations in Mount Lebanon (2011–2016)

The potential for uncertainty in Numerical Weather Prediction model verification when using solid precipitation observations

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