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

Hartl, Lea; Koch, Roland; Marty, Christoph; Olefs, Marc

doi:10.5194/hess-22-2655-2018

Cited by 37 publications

(33 citation statements)

References 33 publications

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“…Falling snow density was empirically parameterized as a function of air temperature and wind speed (Pahaut, 1975). This parameterization is associated with significant uncertainties (Lafaysse et al, 2017;Helfricht et al, 2018). Snow compaction is modelled with a visco-elastic scheme in which the 10 snow viscosity of each layer is parameterized depending mainly on the layer density and temperature.…”

Section: Crocus Snowpack Modelmentioning

confidence: 99%

Statistical post-processing of ensemble forecasts of the height of new snow

Nousu¹,

Lafaysse²,

Vernay³

et al. 2019

Preprint

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Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, roads viability, ski resorts management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool is refining the elevation resolution, and the Crocus snowpack model is representing the main physical processes in the snowpack. It provides better HN forecasts 5 than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on Nonhomogeneous Regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24-hour HN in French Alps and Pyrenees. The calibration is tested at the station-scale and the massif-scale (i.e. aggregating different stations over areas of 1000 km 2 ). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two 10 training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs 15 long reforecasts as homogeneous as possible with the operational systems.30 2018) are considerably lower than errors in solid precipitation measurements.The goal of this study is to test the ability of a nonhomogeneous regression method to improve the ensemble forecasts of HN from the PEARP-S2M ensemble snowpack modelling system. More precisely, the regression method of Scheuerer andHamill (2015) based on the Censored Shifted Gamma Distribution was chosen in this work for the advantages identified by the authors in the case of precipitation forecasts. In particular, this method allows to extrapolate the statistical relationship 35 3 https://doi.between predictors and predictands from common events to more unusual events. Considering the specificities of the available datasets in terms of predictands and predictors, two other scientific questions are considered: (1) can statistical postprocessing be applied at a larger spatial scale than the observation points? (2) what are the requirements of a robust training forecast dataset for statistical postprocessing?The structure of the paper is as follows. Section 2 describes the model components of the PEARP-S2M system, the obser-5 vation and forecast datasets used in this study, the nonhomogeneous regression method chosen for post-processing and the evaluation me...

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Section: Crocus Snowpack Modelmentioning

confidence: 99%

Statistical post-processing of ensemble forecasts of the height of new snow

Nousu¹,

Lafaysse²,

Vernay³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Nevertheless, automatic, ultrasonic snow depth sensors are usually included in automated meteorological stations. Snow depth measurements like this have been used to derive solid precipitation (Mair et al, ), snow density (Helfricht, Hartl, Koch, Marty, & Olefs, ), and snow accumulation as snow water equivalent or SWE (Egli, Jonas, & Meister, ), and have been used to compare water balance between forests and clearings (Bales et al, ). However, their use to quantitatively determine snow interception needs to be further explored.…”

Section: Introductionmentioning

confidence: 99%

Assimilating snow observations to snow interception process simulations

Pomeroy

2020

Hydrological Processes

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Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post-storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611-1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785-7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time-lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule-based direct insertion method. Interception determined using density estimates from the Hedstrom-Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data.Assimilation of tree-measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time-lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.

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“…The 5 following elements, averaged only over the period of snowfall (see Section 3.2), were used-air temperature (Ta), relative humidity (RH), wind speed (WS), air pressure (Press), and wet-bulb temperature (Tw) calculated using Ta, RH, and Press. The reasons for selecting these meteorological elements are that Ta, RH, WS, and Tw are often used for the parameterization of new snow density, as summarized by Helfricht et al (2018). Although WS is also strongly affected by the local topography and roughness, WS and Press should share a relation with the synoptic scale condition.…”

Section: Relationship Between Ssa Of Fresh Pp and Meteorological Datamentioning

confidence: 99%

Measurement of specific surface area of fresh solid precipitation particles in heavy snowfall regions of Japan

Yamaguchi¹,

Ishizaka²,

Motoyoshi³

et al. 2019

Preprint

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In countries such as Japan, particular solid precipitation particles (PP) like unrimed PP and graupel sometimes form a weak layer in snow that subsequently triggers slab avalanches. It is therefore essential for avalanche prevention authorities 20 to design a predictive model for slab avalanches triggered by weak PP layers. The specific surface area (SSA) is a parameter that could characterize the physical properties of PP. The SSAs of solid PP were measured for four winters (from 2013/2014 -2016/2017) in Nagaoka, one of the heaviest snowfall-experiencing cities in Japan. More than 100 SSAs of PP were measured during the study period using the gas absorption method. The measured SSA values range from 42 to 153 m 2 kg -1 . PP under the melting condition show smaller values than that without melting. Unrimed and slightly rimed PP exhibited low SSA, 25 whereas heavily rimed PP and graupel exhibited high SSA. The degree of riming depends on the synoptic meteorological conditions. Based on the potential of weak PP layer formation with respect to the degree of riming of PP, the results indicate that SSA is a useful parameter for describing the characteristics of PP to predict avalanches triggered by weak PP layers. The study found that the values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. SSA increases with increase in WS, and decreases with increase in Tw. Using WS and Tw, an equation to empirically estimate the 30 SSA of fresh PP in Nagaoka was established. The equation successfully reproduced the fluctuation of SSA. The SSA equation, with the meteorological data, is an efficient first step toward describing the development of weak PP layers in the snow cover models.The Cryosphere Discuss., https://doi.

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

Cited by 37 publications

References 33 publications

Statistical post-processing of ensemble forecasts of the height of new snow

Statistical post-processing of ensemble forecasts of the height of new snow

Assimilating snow observations to snow interception process simulations

Measurement of specific surface area of fresh solid precipitation particles in heavy snowfall regions of Japan

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