scholarly journals Evaluating continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on a Swiss glacier

2019 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mean Absolute Error ◽  
Cosmic Ray ◽  
Absolute Error ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be directly derived from neutron counts. In this study, we present the analyses of temporally continuous SWE measurements by a CRS on a Swiss glacier (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which were markedly different in terms of amount and timing of snow accumulation. By combining the SWE values with snow depth measurements, we calculate the daily mean density of the snowpack. The autonomous measurements overestimate SWE by +2 % ± 12 % compared to manual field observations (snow pits). Snow depth and mean density agree with manual in situ measurements with a standard deviation of ±6 % and ±8 %, respectively. In general, the cosmic ray sensor measured with high reliability during these two winter seasons and is, thus, considered an effective method to measure SWE at remote high alpine sites. We use the daily observations to break down the winter season into days either dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, melt) or snow densification. The prevailing meteorological conditions of these periods are clearly distinct for each of the classified processes. Moreover, we compare daily SWE amounts to precipitation sums from three nearby weather stations located at lower elevations, and to a gridded precipitation dataset. We determine the best-possible scaling factor for these precipitation estimates in order to reproduce the measured accumulation on the glacier. Using only one scaling factor for the whole time series, we find a mean absolute error of less than 8 cm w.e. for the reproduced snow accumulation. By applying temperature-specific scaling factors, this mean absolute error can be reduced to less than 6 cm w.e. for all stations.

scholarly journals Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

2019 ◽  
Vol 13 (12) ◽  
pp. 3413-3434 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
High Mountain ◽  
Alpine Glacier ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

Development of a Snow Load Alert System, “YukioroSignal” for Aiding Roof Snow Removal Decisions in Snowy Areas in Japan

2020 ◽  
Vol 15 (6) ◽  
pp. 688-697
Author(s):  
Hiroyuki Hirashima ◽  
Tsutomu Iyobe ◽  
Katsuhisa Kawashima ◽  
Hiroaki Sano ◽  
◽  
...  
Keyword(s):  
Snow Depth ◽  
Load Distribution ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Risk Level ◽  
Residential Areas ◽  
Alert System ◽  
Snow Removal ◽  
Snow Load ◽  
Snow Water

This study developed a snow load alert system, known as the “YukioroSignal”; this system aims to provide a widespread area for assessing snow load distribution and the information necessary for aiding house roof snow removal decisions in snowy areas of Japan. The system was released in January 2018 in Niigata Prefecture, Japan, and later, it was expanded to Yamagata and Toyama prefectures in January 2019. The YukioroSignal contains two elements: the “Quasi-Real-Time Snow Depth Monitoring System,” which collects snow depth data, and the numerical model known as SNOWPACK, which can calculate the snow water equivalent (SWE). The snow load per unit area is estimated to be equivalent to SWE. Based on the house damage risk level, snow load distribution was indicated by colors following the ISO 22324. The system can also calculate post-snow removal snow loads. The calculated snow load was validated by using the data collected through snow pillows. The simulated snow load had a root mean square error (RMSE) of 21.3%, which was relative to the observed snow load. With regard to residential areas during the snow accumulation period, the RMSE was 13.2%. YukioroSignal received more than 56,000 pageviews in the snowheavy 2018 period and 26,000 pageviews in the less snow-heavy 2019 period.

scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2017 ◽  
Author(s):  
Esteban Alonso-González ◽  
J.¬Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management. The data presented here are available for free download from Zenodo (DOI: https://doi.org/10.5281/zenodo.854618).This paper fully describes the work flow, data validation, uncertainty assessment and possible applications and limitations of the database.

scholarly journals Snowfall and snow accumulation processes during the MOSAiC winter and spring season

2021 ◽  
Author(s):  
David N. Wagner ◽  
Matthew D. Shupe ◽  
Ola G. Persson ◽  
Taneil Uttal ◽  
Markus M. Frey ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Drifting Snow ◽  
Snow Water ◽  
Abstract Data ◽  
Good Agreement

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation, and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), installed on a railing on the top deck of research vessel Polarstern was least affected by blowing snow and showed good agreements with SWE retrievals along the transect, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.

scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2018 ◽  
Vol 10 (1) ◽  
pp. 303-315 ◽  
Author(s):  
Esteban Alonso-González ◽  
J. Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km  ×  10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km  ×  10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

scholarly journals GNSS-R-Based Snow Water Equivalent Estimation with Empirical Modeling and Enhanced SNR-Based Snow Depth Estimation

2020 ◽  
Vol 12 (23) ◽  
pp. 3905
Author(s):  
Kegen Yu ◽  
Yunwei Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
Empirical Models ◽  
Empirical Modeling ◽  
Fusion Model ◽  
Snow Water

Snow depth and snow water equivalent (SWE) are two parameters for measuring snowfall. By exploiting the Global Navigation Satellite System reflectometry (GNSS-R) technique and thousands of existing GNSS Continuous Operating Reference Stations (CORS) deployed in the cryosphere, it is possible to improve the temporal and spatial resolutions of the SWE measurement. In this paper, a fusion model for combining multi-satellite SNR (Signal to Noise Ratio) snow depth estimations is proposed, which uses peak spectral powers associated with each of the snow depth estimations. To simplify the estimation of SWE, the complete snowfall period over a winter season is split into snow accumulation, transition, and melting period in accordance with the variation characteristics of snow depth and SWE. By extensively using in situ snow depth and SWE observations recorded by snow telemetry network (SNOTEL) and regression analysis, three empirical models are developed to describe the relationship between snow depth and SWE for the three periods, respectively. Based on the snow depth fusion model and the SWE empirical models, an SWE estimation algorithm is proposed. Three data sets recorded in different environments are used to test the proposed method. The results demonstrate that there exists good agreement between the in situ SWE measurements and the SWE estimates produced by the proposed method; the root-mean-square error of SWE estimations is smaller than 6 cm when the SWE is up to 80 cm.

scholarly journals Analysis of the Snow Water Equivalent at the AEMet-Formigal Field Laboratory (Spanish Pyrenees) During the 2019/2020 Winter Season Using a Stepped-Frequency Continuous Wave Radar (SFCW)

2021 ◽  
Vol 13 (4) ◽  
pp. 616
Author(s):  
Rafael Alonso ◽  
José María García del Pozo ◽  
Samuel T. Buisán ◽  
José Adolfo Álvarez
Keyword(s):  
Software Defined Radio ◽  
Continuous Wave ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Relative Dielectric Permittivity ◽  
Near Surface ◽  
Wave Radar ◽  
Spanish Pyrenees ◽  
Snow Water ◽  
Stepped Frequency

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available the water from the snow cover is the well-known snow water equivalent (SWE). This paper presents a near-surface-based radar for determining the SWE from the measured complex spectral reflectance of the snowpack. The method is based in a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150 MHz to 6 GHz. An electromagnetic model to solve the electromagnetic reflectance of a snowpack, including the frequency and wetness dependence of the complex relative dielectric permittivity of snow layers, is shown. Using the previous model, an approximated method to calculate the SWE is proposed. The results are presented and compared with those provided by a cosmic-ray neutron SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. This experimental field is located in the Spanish Pyrenees at an elevation of 1800 m a.s.l. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow height measurement sensor based on a 120 GHz frequency modulated continuous wave (FMCW) radar sensor, is shown.

scholarly journals Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 363
Author(s):  
George Duffy ◽  
Fraser King ◽  
Ralf Bennartz ◽  
Christopher G. Fletcher
Keyword(s):  
Time Scales ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snow Accumulation ◽  
High Latitudes ◽  
Percentage Points ◽  
Snow Water ◽  
Time Periods ◽  
Predicted Values ◽  
Good Agreement

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

scholarly journals Estimating snow water equivalent using cosmic‐ray neutron sensors from the COSMOS‐UK network

2021 ◽  
Author(s):  
J. R. Wallbank ◽  
S. J. Cole ◽  
R. J. Moore ◽  
S. R. Anderson ◽  
E. J. Mellor
Keyword(s):  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Snow Water ◽  
Neutron Sensors
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