Bias corrections of precipitation measurements across experimental sites in different ecoclimatic regions of western Canada

Xiong, Pan; Yang, Daqing; Li, Yanping; Barr, Alan G.; Helgason, Warren; Hayashi, Masaki; Marsh, Philip; Pomeroy, John W.; Janowicz, R.

doi:10.5194/tc-10-2347-2016

Cited by 65 publications

(83 citation statements)

References 30 publications

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“…Three types of biases were found in the Geonor observations, similarly to previous studies (Harder and Pomeroy, 2013;and Pan et al, 2016): (1) field servicing (i.e., emptying and/or adding of antifreeze and oil to the Geonor bucket); (2) jitters and diurnal noise due to wind speed (e.g., MZA) and changes in temperature are similar to those found in sites with strong diurnal changes in temperature, radiation, and wind speed (e.g., Harder and Pomeroy 2013;Pan et al, 2016); and (3) long-term drift results from evaporation within the bucket, which occurs at the end of snow season, when air temperature is high. We post-processed the raw precipitation data using a supervised correction similar to the one described in Harder and Pomeroy (2013) by performing the following steps.…”

Section: Correcting For Geonor Undercatchsupporting

confidence: 71%

“…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%

“…Over-correction is possible for snowfall events and occurs under the impact of blowing snow at high wind speeds (Pan et al, 2016). To limit overcorrection, we used a threshold for maximum wind speed.…”

Section: Correcting For Geonor Undercatchmentioning

confidence: 99%

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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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Section: Correcting For Geonor Undercatchsupporting

confidence: 71%

Section: Correcting For Geonor Undercatchmentioning

confidence: 99%

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Snow observations in Mount Lebanon (2011–2016)

Fayad

Gascoin

Faour

et al. 2017

Earth Syst. Sci. Data

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“…Recent advances in remotely sensed soil moisture, such as the ground-based cosmic ray neutron probe (Zreda et al, 2008) or satellite-based sensors such as those used by the Soil Moisture and Ocean Salinity (SMOS) mission (Kerr et al, 2010) or the Soil Moisture Active Passive (SMAP) mission (Entekhabi et al, 2010), can retrieve soil moisture estimates over hundreds of metres to tens of kilometres. However, these observations are limited to the near surface, and need to be depth-scaled to the root zone to be suitable for water balance studies (Peterson et al, 2016). Adequately capturing field-scale variability using point-scale measurement techniques requires a large number of samples (e.g.…”

Section: Introductionmentioning

confidence: 99%

Field-scale water balance closure in seasonally frozen conditions

Pan¹,

Helgason²,

Ireson³

et al. 2016

Preprint

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Hydrological water balance closure is a simple concept, yet in practice it is uncommon to measure every significant term independently in the field. Here we demonstrate the degree to which the field-scale water balance can be closed using only routine field observations in a seasonally frozen prairie pasture field site in Saskatchewan, Canada. Arrays of snow and soil moisture measurements were combined with a precipitation gauge and flux tower evapotranspiration estimates. We consider three hydrologically distinct periods: the snow accumulation period over the winter, the snowmelt period in spring, and the summer growing season. In each period, we attempt to quantify the residual between net precipitation (precipitation minus evaporation) and the change in field-scale storage (snow and soil moisture), while accounting for measurement uncertainties. When the residual is negligible, a simple 1-D water balance with no net drainage is adequate. When the residual is non-negligible, we must find additional processes to explain the result. We identify the hydrological fluxes which confound the 1-D water balance assumptions during different periods of the year, notably blowing snow and frozen soil moisture redistribution during the snow accumulation period, and snowmelt runoff and soil drainage during the melt period. Challenges associated with quantifying these processes, as well as uncertainties in the measurable quantities, caution against the common use of water balance residuals to estimate fluxes and constrain models in such a complex environment.

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“…Recent results of the Solid Precipitation Intercomparison Experiment (SPICE; Nitu et al, 2012) reveal that these errors still exist in standard meteorological measurements (e.g. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Bias corrections of precipitation measurements across experimental sites in different ecoclimatic regions of western Canada

Cited by 65 publications

References 30 publications

Snow observations in Mount Lebanon (2011–2016)

Snow observations in Mount Lebanon (2011–2016)

Field-scale water balance closure in seasonally frozen conditions

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

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