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Journal articles on the topic 'Snow observations'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zhang, Wei, Jianqiao He, An’an Chen, Xuejiao Wu, and Yongping Shen. "Observations of Drifting Snow Using FlowCapt Sensors in the Southern Altai Mountains, Central Asia." Water 14, no. 6 (2022): 845. http://dx.doi.org/10.3390/w14060845.

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Drifting snow is a significant factor in snow redistribution and cascading snow incidents. However, field observations of drifting snow are relatively difficult due to limitations in observation technology, and drifting snow observation data are scarce. The FlowCapt sensor is a relatively stable sensor that has been widely used in recent years to obtain drifting snow observations. This study presents the results from two FlowCapt sensors that were employed to obtain field observations of drifting snow during the 2017–2018 snow season in the southern Altai Mountains, Central Asia, where the sno

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Arsenault, Kristi, and Paul Houser. "Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation." Geosciences 8, no. 12 (2018): 484. http://dx.doi.org/10.3390/geosciences8120484.

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Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to snow cover fraction (SCF). In this study, a new approach is introduced that uses snow water equivalent (SWE) observations and satellite-based SCF retrievals to derive SDC relationships for use in an Ensemble Kalman filter (EnKF) to assimilate snow cover estimates. A histogram analysis is used to bin the SWE observations, whic

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Horton, Simon, and Pascal Haegeli. "Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting." Cryosphere 16, no. 8 (2022): 3393–411. http://dx.doi.org/10.5194/tc-16-3393-2022.

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Abstract. The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather–snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–2021 winter were compiled for 21 avalanche

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Lv, Zhibang, and John W. Pomeroy. "Assimilating snow observations to snow interception process simulations." Hydrological Processes 34, no. 10 (2020): 2229–46. http://dx.doi.org/10.1002/hyp.13720.

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5

Fayad, Abbas, Simon Gascoin, Ghaleb Faour, et al. "Snow observations in Mount Lebanon (2011–2016)." Earth System Science Data 9, no. 2 (2017): 573–87. http://dx.doi.org/10.5194/essd-9-573-2017.

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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 km2 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

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Gafurov, A., D. Kriegel, S. Vorogushyn, and B. Merz. "Evaluation of remotely sensed snow cover product in Central Asia." Hydrology Research 44, no. 3 (2012): 506–22. http://dx.doi.org/10.2166/nh.2012.094.

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Central Asian countries depend highly on water resources from snow and glacier melt, which has to be studied thoroughly to estimate water availability. However, the observation network in Central Asia is poor to carry out such studies in detail. Observations from space using remote sensing techniques might fill this observation gap, which needs to be validated. Therefore, this study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product in Central Asia. For the evaluation, in situ snow depth data from 30 meteorological stations and higher resolution Landsa

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Riboust, Philippe, Guillaume Thirel, Nicolas Le Moine, and Pierre Ribstein. "Revisiting a Simple Degree-Day Model for Integrating Satellite Data: Implementation of Swe-Sca Hystereses." Journal of Hydrology and Hydromechanics 67, no. 1 (2019): 70–81. http://dx.doi.org/10.2478/johh-2018-0004.

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Abstract Conceptual degree-day snow models are often calibrated using runoff observations. This makes the snow models dependent on the rainfall-runoff model they are coupled with. Numerous studies have shown that using Snow Cover Area (SCA) remote sensing observation from MODIS satellites helps to better constrain parameters. The objective of this study was to calibrate the CemaNeige degree-day snow model with SCA and runoff observations. In order to calibrate the snow model with SCA observations, the original CemaNeige SCA formulation was revisited to take into account the hysteresis that exi

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Fletcher, Steven J., Glen E. Liston, Christopher A. Hiemstra, and Steven D. Miller. "Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model." Journal of Hydrometeorology 13, no. 5 (2012): 1475–92. http://dx.doi.org/10.1175/jhm-d-11-082.1.

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Abstract In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in

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Zlydneva, L. A., and E. V. Pischalnikova. "THE INFLUENCE OF LANDSCAPE ELEMENTS ON THE DISTRIBUTION OF SNOW COVER ACCORDING TO FIELD MEASUREMENTS AND ANALYSIS (ON THE EXAMPLE OF PERM)." Bulletin of Udmurt University. Series Biology. Earth Sciences 31, no. 3 (2021): 301–10. http://dx.doi.org/10.35634/2412-9518-2021-31-3-301-310.

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The scientific interest in snow dynamics is growing steadily, especially in relation to climate variability. The ability to monitor snow cover is being improved, which makes it possible to take into account the characteristics of snow cover from hard-to-reach places in hydrodynamic models of the atmosphere. As a result, the quality of climate and weather forecasting improves. This paper presents an analysis of field observation data in small relief forms with observation data from a stationary network of ground-based observations and ERA5-Land reanalysis. It was found that the temporal variabi

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Uematsu, Takahiko. "Ablation Rates on the Ceiling of a Snow Tunnel Over a Stream." Annals of Glaciology 6 (1985): 316–17. http://dx.doi.org/10.3189/1985aog6-1-316-317.

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A snow tunnel is often formed between the water surface of a small stream and the base of a snow cover in snowy areas. Observations made in northern Japan revealed that the amount of snow ablated at the ceiling of the snow tunnel ranged widely from 1.3 - 50 kg/m2d. This amount was calculated using heat transfer method; assuming that the ceiling was ablated only by the flux of heat from stream water, the amounts observed and calculated were found consistent.

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Uematsu, Takahiko. "Ablation Rates on the Ceiling of a Snow Tunnel Over a Stream." Annals of Glaciology 6 (1985): 316–17. http://dx.doi.org/10.1017/s0260305500010776.

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A snow tunnel is often formed between the water surface of a small stream and the base of a snow cover in snowy areas. Observations made in northern Japan revealed that the amount of snow ablated at the ceiling of the snow tunnel ranged widely from 1.3 - 50 kg/m2d. This amount was calculated using heat transfer method; assuming that the ceiling was ablated only by the flux of heat from stream water, the amounts observed and calculated were found consistent.

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12

Riedel, Christopher, and Jeffrey Anderson. "Exploring non-Gaussian sea ice characteristics via observing system simulation experiments." Cryosphere 18, no. 6 (2024): 2875–96. http://dx.doi.org/10.5194/tc-18-2875-2024.

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Abstract. The Arctic is warming at a faster rate compared to the globe on average, a phenomenon commonly referred to as Arctic amplification. Sea ice has been linked to Arctic amplification and has gathered attention recently due to the decline in summer sea ice extent. Data assimilation (DA) is the act of combining observations with prior forecasts to obtain a more accurate model state. Sea ice poses a unique challenge for DA because sea ice variables have bounded distributions, leading to non-Gaussian distributions. The non-Gaussian nature violates the Gaussian assumptions built into DA algo

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Liston, Glen E., and Christopher A. Hiemstra. "A Simple Data Assimilation System for Complex Snow Distributions (SnowAssim)." Journal of Hydrometeorology 9, no. 5 (2008): 989–1004. http://dx.doi.org/10.1175/2008jhm871.1.

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Abstract A methodology for assimilating ground-based and remotely sensed snow data within a snow-evolution modeling system (SnowModel) is presented. The data assimilation scheme (SnowAssim) is consistent with optimal interpolation approaches in which the differences between the observed and modeled snow values are used to constrain modeled outputs. The calculated corrections are applied retroactively to create improved fields prior to the assimilated observations. Thus, one of the values of this scheme is the improved simulation of snow-related distributions throughout the entire snow season,

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Rodell, M., and P. R. Houser. "Updating a Land Surface Model with MODIS-Derived Snow Cover." Journal of Hydrometeorology 5, no. 6 (2004): 1064–75. http://dx.doi.org/10.1175/jhm-395.1.

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Abstract A simple scheme for updating snow-water storage in a land surface model using snow cover observations is presented. The scheme makes use of snow cover observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites. Simulated snow-water equivalent is adjusted when and where the model and MODIS observation differ, following an internal accounting of the observation quality, by either removing the simulated snow or adding a thin layer. The scheme is tested in a 101-day global simulation of the Mosaic land surface model driven

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15

Lim, Sujeong, Hyeon-Ju Gim, Ebony Lee, et al. "Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)." Geoscientific Model Development 15, no. 22 (2022): 8541–59. http://dx.doi.org/10.5194/gmd-15-8541-2022.

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Abstract. Snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas LSMs evaluate the snow cover, snow albedo, and snow depth through interaction with atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. When the initially developed empirical parameters ar

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Pershin, Dmitry Konstantinovich, Liliya Fedorovna Lubenets, Dmitry Vladimirovich Chernykh, Roman Yur'evich Biryukov, and Dmitrii Vladimirovich Zolotov. "Open database of snow-measuring observations in the south of Western Siberia (2011-2021) and its comparison with data from stationary meteorological observations and satellite monitoring." Арктика и Антарктика, no. 3 (March 2021): 1–18. http://dx.doi.org/10.7256/2453-8922.2021.3.36262.

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This article provides a database of the local snow-measuring observations for three river basins in the south of Western Siberia, reviews the methodological peculiarities of the conduct of measurements, and compares the acquired data with the observations at weather stations and available satellite data (CGLS SWE). Observations were carried out in several stages over the period of ten years (2011-2021) in small river basins of Kuchuk, Kasmala, and Mayma Rivers, and reflect the transition from the West Siberian Plain to the Altai lowlands. Total of 25,000 measurements of the parameters of snow

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17

Hu, Yanxing, Tao Che, Liyun Dai, and Lin Xiao. "Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere." Remote Sensing 13, no. 7 (2021): 1250. http://dx.doi.org/10.3390/rs13071250.

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In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series

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18

Zaitchik, Benjamin F., and Matthew Rodell. "Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model." Journal of Hydrometeorology 10, no. 1 (2009): 130–48. http://dx.doi.org/10.1175/2008jhm1042.1.

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Abstract Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equival

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Luijting, Hanneke, Dagrun Vikhamar-Schuler, Trygve Aspelien, Åsmund Bakketun, and Mariken Homleid. "Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway." Cryosphere 12, no. 6 (2018): 2123–45. http://dx.doi.org/10.5194/tc-12-2123-2018.

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Abstract. In Norway, 30 % of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1 km over an area in southern Norway for 2 years (1 September 2014–31 August 2016). Experiments were carried out using two different forcing data sets: (1) hourly forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing) including post-processed temperature (500 m gri

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Helmert, Jürgen, Aynur Şensoy Şorman, Rodolfo Alvarado Montero, et al. "Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey." Geosciences 8, no. 12 (2018): 489. http://dx.doi.org/10.3390/geosciences8120489.

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The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its b

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Dyrrdal, Anita Verpe. "An evaluation of Norwegian snow maps: simulation results versus observations." Hydrology Research 41, no. 1 (2009): 27–37. http://dx.doi.org/10.2166/nh.2010.019.

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The snow map service introduced by the Norwegian Meteorological Institute and Norwegian Water Resources and Energy Directorate in 2004 is evaluated at eleven meteorological stations situated in three regions in Norway. The focus is on the start and end of the snow season and the total number of snow days. In addition, accumulated snow depth throughout the winter season, along with snow depth on four selected dates, is examined. In the evaluation, simulations by a precipitation/degree-day snow model are compared to observations. The approach used to calculate snow depth tends to compact the sno

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Cluzet, Bertrand, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont. "Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network." Cryosphere 16, no. 4 (2022): 1281–98. http://dx.doi.org/10.5194/tc-16-1281-2022.

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Abstract. The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterizations, and unresolved terrain features. In situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large-scale modeling errors by means of data assimilation. In this work, we assimilate HS observations from an in situ network of 295 stations covering the French Alps, Pyrenees, and Andorra, over the period 2009–

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Shao, Donghang, Wenbo Xu, Hongyi Li, Jian Wang, and Xiaohua Hao. "Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations." Remote Sensing 12, no. 18 (2020): 3101. http://dx.doi.org/10.3390/rs12183101.

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Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high s

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Lee, J. E., G. W. Lee, M. Earle, and R. Nitu. "Uncertainty analysis for evaluating the accuracy of snow depth measurements." Hydrology and Earth System Sciences Discussions 12, no. 4 (2015): 4157–90. http://dx.doi.org/10.5194/hessd-12-4157-2015.

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Abstract. A methodology for quantifying the accuracy of snow depth measurement are demonstrated in this study by using the equation of error propagation for the same type sensors and by compariong autimatic measurement with manual observation. Snow depth was measured at the Centre for Atmospheric Research Experiments (CARE) site of the Environment Canada (EC) during the 2013–2014 winter experiment. The snow depth measurement system at the CARE site was comprised of three bases. Three ultrasonic and one laser snow depth sensors and twelve snow stakes were placed on each base. Data from snow dep

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Lenaerts, J. T. M., C. J. P. P. Smeets, K. Nishimura, et al. "Drifting snow measurements on the Greenland Ice Sheet and their application for model evaluation." Cryosphere 8, no. 2 (2014): 801–14. http://dx.doi.org/10.5194/tc-8-801-2014.

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Abstract. This paper presents autonomous drifting snow observations performed on the Greenland Ice Sheet in the fall of 2012. High-frequency snow particle counter (SPC) observations at ~ 1 m above the surface provided drifting snow number fluxes and size distributions; these were combined with meteorological observations at six levels. We identify two types of drifting snow events: katabatic events are relatively cold and dry, with prevalent winds from the southeast, whereas synoptic events are short lived, warm and wet. Precipitating snow during synoptic events disturbs the drifting snow meas

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Lenaerts, J. T. M., C. J. P. P. Smeets, K. Nishimura, et al. "Drifting snow measurements on the Greenland Ice Sheet and their application for model evaluation." Cryosphere Discussions 8, no. 1 (2014): 21–53. http://dx.doi.org/10.5194/tcd-8-21-2014.

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Abstract. This paper presents autonomous drifting snow observations performed on the Greenland Ice Sheet in the fall of 2012. High-frequency Snow Particle Counter (SPC) observations at ~1 m above the surface provided drifting snow number fluxes and size distributions; these were combined with meteorological observations at six levels. We identify two types of drifting snow events: katabatic events are relatively cold and dry, with prevalent winds from the southeast, whereas synoptic events are short-lived, warm and wet. Precipitating snow during synoptic events disturbs the drifting snow measu

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Tian, Y., S. Zhang, W. Du, et al. "SURFACE SNOW DENSITY OF EAST ANTARCTICA DERIVED FROM IN-SITU OBSERVATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1657–60. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1657-2018.

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Models based on physical principles or semi-empirical parameterizations have used to compute the firn density, which is essential for the study of surface processes in the Antarctic ice sheet. However, parameterization of surface snow density is often challenged by the description of detailed local characterization. In this study we propose to generate a surface density map for East Antarctica from all the filed observations that are available. Considering that the observations are non-uniformly distributed around East Antarctica, obtained by different methods, and temporally inhomogeneous, th

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Wang, Jiatong, Yufeng Hu, Zhenhong Li, Chenglong Zhang, Lei Lei, and Ji Wang. "Improving GPS-IR Snow Depth Estimation by Considering the Snow Surface Roughness." Journal of Physics: Conference Series 2356, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1742-6596/2356/1/012048.

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Snow is an important environmental variable influencing weather and climate. The GPS-IR technique is a very effective technique for monitoring snow depth. A GPS-IR snow depth estimation corrected model is proposed to address the impact of the Signal to Noise Ratio (SNR) amplitude attenuation and snow surface roughness variation that are not considered in the standard model of GPS-IR. In this study, the snow depth of the P351 GPS site of the Plate Boundary Observatory (PBO) was obtained using the standard model and the corrected model, and the snow depth observations of the nearby SNOTEL statio

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Karbou, Fatima, Gaëlle Veyssière, Cécile Coleou, et al. "Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations." Remote Sensing 13, no. 3 (2021): 381. http://dx.doi.org/10.3390/rs13030381.

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The main objective of this study was to monitor wet snow conditions from Sentinel-1 over a season, to examine its variation over time by cross-checking wet snow with independent snow and weather estimates, and to study its distribution taking into account terrain characteristics such as elevation, orientation, and slope. One of our motivations was to derive useful representations of daily or seasonal snow changes that would help to easily identify wet snow elevations and determine melt-out days in an area of interest. In this work, a well-known approach in the literature is used to estimate th

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Sugiura, Konosuke, Daqing Yang, and Tetsuo Ohata. "Rapid change of tundra snow hardness in Alaska." Annals of Glaciology 52, no. 58 (2011): 97–101. http://dx.doi.org/10.3189/172756411797252040.

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AbstarctTo understand the formation of hard snow in the field and its relationship to the physical parameters of snow surfaces, we carried out snowpack observations at a flat and snow-covered field in Barrow, Alaska. These observations were performed in the middle of winter and included two drifting snow events. After the end of each drifting snow event, the minimum snow surface density increased slightly with time and there was a rapid increase in the minimum snow surface hardness. There was no rain during our observations. Therefore, the strong bonds among the snow surface particles were not

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Gerland, Sebastian, and Christian Haas. "Snow-depth observations by adventurers traveling on Arctic sea ice." Annals of Glaciology 52, no. 57 (2011): 369–76. http://dx.doi.org/10.3189/172756411795931552.

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AbstractSnow depth is a key parameter for assessing the sea-ice mass budget in the Arctic and for the surface energy balance at the atmosphere–snow–ice–ocean interfaces. However, scientific expeditions to the high Arctic Ocean are rare, and for large parts of the year no snow and ice data are collected in situ in most regions. Therefore any additional in situ observations of snow depth are of interest to the scientific community. Arctic adventurers and tourists are among the most frequent visitors to the Arctic Ocean and North Pole. If properly trained and carefully adhering to standard protoc

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Hou, Yingxu, Xiaodong Huang, and Lin Zhao. "Point-to-Surface Upscaling Algorithms for Snow Depth Ground Observations." Remote Sensing 14, no. 19 (2022): 4840. http://dx.doi.org/10.3390/rs14194840.

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To validate the accuracy of snow depth products retrieved from passive microwave remote sensing data with a high confidence level, the verification method based on points of ground observation is subject to great uncertainty, due to the scale effect. Thus, it is necessary to use a point-to-surface scale transformation method to obtain the relative ground truth at the remote sensing pixel scale. In this study, by using the snow depth ground observations at different observation scales, the upscaling methods are conducted based on simple average (SA), geostatistical, Bayes maximum entropy (BME),

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Gao, Xiaowen, Jinmei Pan, Zhiqing Peng, et al. "Snow Density Retrieval in Quebec Using Space-Borne SMOS Observations." Remote Sensing 15, no. 8 (2023): 2065. http://dx.doi.org/10.3390/rs15082065.

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Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a

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Venäläinen, Pinja, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala. "Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy." Cryosphere 15, no. 6 (2021): 2969–81. http://dx.doi.org/10.5194/tc-15-2969-2021.

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Abstract. Snow water equivalent (SWE) is an important variable in describing global seasonal snow cover. Traditionally, SWE has been measured manually at snow transects or using observations from weather stations. However, these measurements have a poor spatial coverage, and a good alternative to in situ measurements is to use spaceborne passive microwave observations, which can provide global coverage at daily timescales. The reliability and accuracy of SWE estimates made using spaceborne microwave radiometer data can be improved by assimilating radiometer observations with weather station sn

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Nishimura, Kouichi, Masaki Nemoto, Yoichi Ito, Satoru Omiya, Kou Shimoyama, and Hirofumi Niiya. "Elucidation of spatiotemporal structures from high-resolution blowing-snow observations." Cryosphere 18, no. 10 (2024): 4775–86. http://dx.doi.org/10.5194/tc-18-4775-2024.

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Abstract. Systematic observations were conducted to investigate the spatiotemporal structures of blowing snow. Along a line perpendicular to the dominant wind direction on the lee side of a flat field, 15 snow particle counters (SPCs) and ultrasonic anemometers (USAs) were placed 1.5 m apart. Data were recorded at high frequencies of 100 kHz for SPCs and 1 kHz for USAs. The horizontal mass flux distributions, representing the spatiotemporal variability of blowing snow, exhibited non-uniformity in both time and space and manifested periodic changes akin to snow waves. Additionally, the presence

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Ishizaka, Masaaki. "Climatic response of snow depth to recent warmer winter seasons in heavy-snowfall areas in Japan." Annals of Glaciology 38 (2004): 299–304. http://dx.doi.org/10.3189/172756404781815248.

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AbstractIn heavy-snowfall areas facing the Sea of Japan, winter seasons with lower volumes of snow occurred from 1986/87 to 1999/2000. In this paper, the changes induced by these warmer winters in snowy areas in Japan are investigated using two datasets. One set was normalized for the period 1971–2000 from manned surface meteorological observations by the Japan Meteorological Agency, and the other set was for 1961– 90. Winter climatic monthly values for the first dataset were thought to be affected by the warmer winter seasons since almost half the relevant period coincides with them. By compa

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Dai, Liyun, Tao Che, Yang Zhang, et al. "Microwave radiometry experiment for snow in Altay, China: time series of in situ data for electromagnetic and physical features of snowpack." Earth System Science Data 14, no. 8 (2022): 3509–30. http://dx.doi.org/10.5194/essd-14-3509-2022.

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Abstract. In this paper, we present a comprehensive experiment, namely, an Integrated Microwave Radiometry Campaign for snow (IMCS), in Xinjiang, China, during the snow season of 2015–2016. The campaign hosted a dual-polarized microwave radiometer operating at L, K, and Ka bands to provide minutely passive microwave observations of snow cover at a fixed site, along with daily manual snow pit observations of snow physical parameters, automatic observations of 10 min four-component radiation and layered snow temperatures, and meteorological observations of hourly weather data and soil data. To t

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Lakhankar, T., J. Muñoz, P. Romanov, et al. "CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data." Hydrology and Earth System Sciences Discussions 9, no. 7 (2012): 8105–36. http://dx.doi.org/10.5194/hessd-9-8105-2012.

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Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during winter 2011 at the research site of the National Weather Service office, Caribou ME, USA. In this ground experiment, dual polarized microwave (37 and 89 GHz) observations are conducted along with detailed synchronous observations of snowpack properties. The objective of this long term field experiment is to improve our understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to impro

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Zhao, Jiawei, Yang Lu, Haibo Zhao, Xiaochun Wang, and Jiping Liu. "Influence of Snow Redistribution and Melt Pond Schemes on Simulated Sea Ice Thickness During the MOSAiC Expedition." Journal of Marine Science and Engineering 13, no. 7 (2025): 1317. https://doi.org/10.3390/jmse13071317.

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The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistr

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Yamaguchi, Satoru, Masaki Nemoto, Takahiro Tanabe, et al. "Overview: Results of Snow and Ice Disaster Mitigation Conducted by the National Research Institute for Earth Science and Disaster Resilience." Journal of Disaster Research 19, no. 5 (2024): 733–40. http://dx.doi.org/10.20965/jdr.2024.p0733.

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More than half of Japan’s land area experiences significant snowfall during winter, and the damage caused by various snow and ice disasters remains a dire issue, which also leads to decreased living standards. Simultaneously, the nature of snow and ice disasters has been transformed due to climate change and the increasing occurrence of extreme weather conditions. The National Research Institute for Earth Science and Disaster Resilience (NIED) has been continuously conducting research to address these problems in relation to snow and ice disasters. This study presents the results of the projec

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Järvi, L., C. S. B. Grimmond, M. Taka, A. Nordbo, H. Setälä, and I. B. Strachan. "Development of the Surface Urban Energy and Water Balance Scheme (SUEWS) for cold climate cities." Geoscientific Model Development 7, no. 4 (2014): 1691–711. http://dx.doi.org/10.5194/gmd-7-1691-2014.

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Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) is developed to include snow. The processes addressed include accumulation of snow on the different urban surface types: snow albedo and density aging, snow melting and re-freezing of meltwater. Individual model parameters are assessed and independently evaluated using long-term observations in the two cold climate cities of Helsinki and Montreal. Eddy covariance sensible and latent heat fluxes and snow depth observations are available for two sites in Montreal and one in Helsinki. Surface runoff from two catchments (24 and 45

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Järvi, L., C. S. B. Grimmond, M. Taka, A. Nordbo, H. Setälä, and I. B. Strachan. "Development of the Surface Urban Energy and Water balance Scheme (SUEWS) for cold climate cities." Geoscientific Model Development Discussions 7, no. 1 (2014): 1063–114. http://dx.doi.org/10.5194/gmdd-7-1063-2014.

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Abstract. The Surface Urban Energy and Water balance Scheme (SUEWS) is developed to include snow. The processes addressed include accumulation of snow on the different urban surface types; snow albedo and density aging; snow melting and re-freezing of melt water. Individual model parameters are assessed and independently evaluated using long-term observations in two cold climate cities, Helsinki and Montreal. Eddy covariance sensible and latent heat fluxes and snow depth observations are available for two sites in Montreal and one in Helsinki. Surface runoff from two catchments (24 and 45 ha)

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Lakhankar, T. Y., J. Muñoz, P. Romanov, et al. "CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data." Hydrology and Earth System Sciences 17, no. 2 (2013): 783–93. http://dx.doi.org/10.5194/hess-17-783-2013.

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Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during January–March 2011 at the research site of the National Weather Service office, Caribou, ME, USA. In this experiment dual-polarized microwave (37 and 89 GHz) observations were accompanied by detailed synchronous observations of meteorology and snowpack physical properties. The objective of this long-term field experiment was to improve understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow a

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Zhang, Wengang, Guirong Xu, Yuanyuan Liu, Guopao Yan, Dejun Li, and Shengbo Wang. "Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions." Atmospheric Measurement Techniques 10, no. 1 (2017): 155–65. http://dx.doi.org/10.5194/amt-10-155-2017.

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Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith observations. The MWR retrievals were averaged in a ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith observations than in zenith observations, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB ar

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Wayand, Nicholas E., Martyn P. Clark, and Jessica D. Lundquist. "Diagnosing snow accumulation errors in a rain-snow transitional environment with snow board observations." Hydrological Processes 31, no. 2 (2016): 349–63. http://dx.doi.org/10.1002/hyp.11002.

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Liston, Glen E., Christopher A. Hiemstra, Kelly Elder, and Donald W. Cline. "Mesocell Study Area Snow Distributions for the Cold Land Processes Experiment (CLPX)." Journal of Hydrometeorology 9, no. 5 (2008): 957–76. http://dx.doi.org/10.1175/2008jhm869.1.

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Abstract The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can distribute meteorological forcings, simulate snowpack accumulation and ablation processes, and assimilate snow-related observations. A SnowModel was developed and used to simulate winter snow accumulation across three 30 km ×

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Zhou, Lu, Julienne Stroeve, Shiming Xu, et al. "Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval." Cryosphere 15, no. 1 (2021): 345–67. http://dx.doi.org/10.5194/tc-15-345-2021.

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Abstract. In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that r

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Luus, Kristina A., John C. Lin, Richard E. J. Kelly, and Claude R. Duguay. "Subnivean Arctic and sub-Arctic net ecosystem exchange (NEE)." Progress in Physical Geography: Earth and Environment 37, no. 4 (2013): 484–515. http://dx.doi.org/10.1177/0309133313491130.

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In the Arctic and sub-Arctic, up to half of annual net ecosystem exchange (NEE) occurs during the snow season. Subnivean soil respiration can persist at a greater rate when the overlying snowpack has a lower thermal conductivity, and the rate of photosynthetic uptake at the start and end of the snow season can be diminished by fractional snow cover. Although recent studies have indicated that uncertainty in model estimates of NEE can be reduced by representing the influence of a modeled snowpack on soil respiration, models of NEE have not represented the influence of snowpack dynamics on proce

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Higashiura, Masao, Takeshi Sato, Atsushi Sato, et al. "Areal investigation of drifting snow on Tsugaru Plain, Japan." Annals of Glaciology 18 (1993): 155–60. http://dx.doi.org/10.3189/s0260305500011423.

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This research investigates mechanisms of drifting snow accumulation, and of strong wind associated with snow clouds (developed cumulus). Detailed structure of snow drifting close to the ground was observed at several sites by use of snow particle counters (SPC), visibility meters and other meteorological instruments, simultaneously with observations of wind structure in the lower atmosphere using Doppler radar and radiosonde. Areal distributions of drifting snow were also observed as a function of time. Primary results include the following. (1) The intensity of drifting snow was found to have

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Higashiura, Masao, Takeshi Sato, Atsushi Sato, et al. "Areal investigation of drifting snow on Tsugaru Plain, Japan." Annals of Glaciology 18 (1993): 155–60. http://dx.doi.org/10.1017/s0260305500011423.

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This research investigates mechanisms of drifting snow accumulation, and of strong wind associated with snow clouds (developed cumulus). Detailed structure of snow drifting close to the ground was observed at several sites by use of snow particle counters (SPC), visibility meters and other meteorological instruments, simultaneously with observations of wind structure in the lower atmosphere using Doppler radar and radiosonde. Areal distributions of drifting snow were also observed as a function of time. Primary results include the following. (1) The intensity of drifting snow was found to have

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