Snow Properties Research Articles

A laser ultrasound system to non-invasively measure compression waves in granular ice mixes

Accurate knowledge of snow mechanical properties, including Young's modulus, shear modulus, Poisson's ratio, and density, is critical to many areas of snow science and to snow-related engineering problems. To facilitate the assessment of these properties, an innovative non-contacting laser ultrasound system (LUS) has been developed. This system acquires ultrasound waveform data at frequencies ranging from tens to hundreds of kHz in a controlled cold-lab environment. Two different LUS devices were compared in this study to determine which recorded more robust ultrasound in granular ice mix samples. We validated the ultrasound observations with poro-elastic traveltime modeling based on physical and empirical constitutive relationships, comparison to and replication of previous studies, and the use of other accredited snow property measurement systems, i.e., the SnowMicroPen. For ice mixes, we determined that the PSV-400 Scanning Vibrometer (Polytec GmbH) produces higher quality ultrasonic wavefield observations (i.e. has a better signal-to-noise ratio) than the VibroFlex Fiber Vibrometer (Polytec GmbH) in the lab conditions tested here. Using the PSV-400, we then demonstrated the utility of this new LUS to study the relationship between snow compression-wave speed and density during snow compaction experiments.

Impact of Land Surface Snow Processes on the Arctic Stable Boundary Layer

Abstract The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

Soot-on-snow experiment: artificial deposition of light-absorbing particles onto snow surfaces in 2018

The absorption of shortwave irradiance in snow depends on the physical properties of snow (e.g., snow grain size and shape, liquid water content, etc.) and light-absorbing particles (LAP). Originating from natural and anthropogenic sources, LAP has been reported to accelerate snowmelt significantly in different regions globally. Yet, our process-level understanding of LAP after deposition onto snow remains rather limited. Here we investigate the impacts of artificial deposition of different LAP onto snow surfaces in an outdoor environment of northern Finland. Following LAP dry deposition into a custom-made tent standing on top of the snowpack, the albedo was followed along with the properties of snow in snow pit measurements throughout the spring season. The results showed that the albedo decay at the end of the season for the different spots were linked to the initial amount and type of LAP that were deposited onto the snowpack. Measured snow temperature profiles from LAP doped snow versus natural reference snow illustrated that the LAP affected snow had higher temperatures in the subsurface snow layers. Collected snow samples analyzed for size distribution of soot particles revealed no apparent agglomeration of soot particles during thaw-freezing events taking place during the experiment. Despite the relatively large perturbation of the experimentally deposited LAP, their impact on the season length was only up to 3 days. Additional experiments are, nevertheless, needed to better constrain the effects of LAP on snow albedo, melt rate, and other associated processes.

Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site

Abstract. Estimates of winter (snow-covered non-growing season) CO2 fluxes across the Arctic region vary by a factor of 3.5, with considerable variation between measured and simulated fluxes. Measurements of snow properties, soil temperatures, and net ecosystem exchange (NEE) at Trail Valley Creek, NWT, Canada, allowed for the evaluation of simulated winter NEE in a tundra environment with the Community Land Model (CLM5.0). Default CLM5.0 parameterisations did not adequately simulate winter NEE in this tundra environment, with near-zero NEE (< 0.01 gCm-2d-1) simulated between November and mid-May. In contrast, measured NEE was broadly positive (indicating net CO2 release) from snow-cover onset until late April. Changes to the parameterisation of snow thermal conductivity, required to correct for a cold soil temperature bias, reduced the duration for which no NEE was simulated. Parameter sensitivity analysis revealed the critical role of the minimum soil moisture threshold of decomposition (Ψmin) in regulating winter soil respiration. The default value of this parameter (Ψmin) was too high, preventing simulation of soil respiration for the vast majority of the snow-covered season. In addition, the default rate of change of soil respiration with temperature (Q10) was too low, further contributing to poor model performance during winter. As Ψmin and Q10 had opposing effects on the magnitude of simulated winter soil respiration, larger negative values of Ψmin and larger positive values of Q10 are required to simulate wintertime NEE more adequately.

Freezing and Thawing of Enclosing Structures and Foundation Soils Taking into Account the Effects of Solar Energy: Part 1. Theoretical Foundations of Freezing Wet Soils

The temperature and humidity state of the foundations and foundations in the climatic conditions of the Russian Federation is decisive in their design,it affects the bearing capacity, operational reliability, and durability of the foundations and the entire building. When designing foundations and foundations, they solve a set of tasks that are subordinated to one idea, so that the soil (base) he carried the load from the building or structure proposed to him, and the foundation structure itself would ensure reliable operation of the building or structure for the entire life cycle, since the foundations belong to permanent structures, and their repair is associated with large material costs. Existing modern calculation methods assume that the heat transfer process is stationary and do not consider real processes – seasonal and daily fluctuations in outdoor air temperature, heating on and off modes, temperature changes in the depth of the soil, wet structures due to accidents of heating plants, the absence (or destruction) of a blind area, etc. Freezing of soils, as a rule, occurs in autumn-winter period, when the soil is saturated with moisture. Thawing of the soil occurs in the spring-summer period. These processes are non-stationary in nature, which is influenced by many factors, including solar radiation. The work is devoted to modelling and calculation of these real processes. The process of freezing and thawing of the foundation soils is influenced by many factors: the composition of the soil, water, ice, snow, solar radiation, fluctuations in outdoor temperature. The paper provides general information about some of the physical properties of water, ice, and snow, necessary for understanding the processes under consideration.

Material mapping strategy to identify the density-dependent properties of dry natural snow

The mechanical properties of natural snow play a crucial role in understanding glaciers, avalanches, polar regions, and snow-related constructions. Research has concentrated on how the mechanical properties of snow vary, primarily with its density; the integration of cutting-edge techniques like micro-tomography with traditional loading methods can enhance our comprehension of these properties in natural snow. This study employs μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}CT imaging and uniaxial compression tests, along with the Digital Volume Correlation (DVC) to investigate the density-dependent material properties of natural snow. The data from two snow samples, one initially non-compressed (test 1) and the other initially compressed (test 2), were fed into Burger’s viscoelastic model to estimate the material properties. μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}CT imaging with 801 projections captures the three-dimensional structure of the snow initially and after each loading step at -18∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${^\\circ }$$\\end{document}C, using a constant deformation rate (0.2 mm/min). The relative density of the snow, ranging from 0.175 to 0.39 (equivalent to 160–360 kg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}), is determined at each load step through binary image segmentation. Modulus and viscosity terms, estimated from Burger’s model, exhibit a density-dependent increase. Maxwell and Kelvin–Voigt moduli range from 0.5 to 14 MPa and 0.1 to 0.8 MPa, respectively. Viscosity values for the Maxwell and Kelvin–Voigt models vary from 0.2 to 2.9 GPa-s and 0.2 to 2.3 GPa-s within the considered density range, showing an exponent between 3 and 4 when represented as power functions. Initial grain characteristics for tests 1 and 2, obtained through image segmentation, reveal an average Specific Surface Area (SSA) of around 55 1/mm and 40 1/mm, respectively. The full-field strain distribution in the specimen at each load step is calculated using the DVC, highlighting strong strain localization indicative of non-homogeneous behavior in natural snow. These findings not only contribute to our understanding of natural snow mechanics but also hold implications for applications in fields such as glacier dynamics and avalanche prediction.

The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model

Abstract. Understanding the future evolution of permafrost requires a better understanding of its climatological past. This requires permafrost models to efficiently simulate the thermal dynamics of permafrost over the past centuries to millennia, taking into account highly uncertain soil and snow properties. In this study, we present a computationally efficient numerical permafrost model which satisfactorily reproduces the current ground temperatures and active layer thicknesses of permafrost in the Arctic and their trends over recent centuries. The performed simulations provide insights into the evolution of permafrost since the 18th century and show that permafrost on the North American continent is subject to early degradation, while permafrost on the Eurasian continent is relatively stable over the investigated 300-year period. Permafrost warming since industrialization has occurred primarily in three “hotspot” regions in northeastern Canada, northern Alaska, and, to a lesser extent, western Siberia. We find that the extent of areas with a high probability (p3 m>0.9) of near-surface permafrost (i.e., 3 m of permafrost within the upper 10 m of the subsurface) has declined substantially since the early 19th century, with loss accelerating during the last 50 years. Our simulations further indicate that short-term climate cooling due to large volcanic eruptions in the Northern Hemisphere in some cases favors permafrost aggradation within the uppermost 10 m of the ground, but the effect only lasts for a relatively short period of a few decades. Despite some limitations, e.g., with respect to the representation of vegetation, the presented model shows great potential for further investigation of the climatological past of permafrost, especially in conjunction with paleoclimate modeling.

An effect of a snow cover on solar heating and melting of lake or sea ice

Solar radiative heating and melting of lake and sea ice is a geophysical problem that has attracted the attention of researchers for many years. This problem is important in connection with the current global change of the climate. Physical and computational models of the process are suggested in the paper. Analytical solutions for the transfer of solar radiation in light-scattering snow cover and ice are combined with numerical calculations of heat transfer in a multilayer system. The thermal boundary conditions take into account convective heat losses to the ambient air and radiative cooling in the mid-infrared window of transparency of the cloudless atmosphere. The study begins with an anomalous spring melting of ice on the large high-mountain lakes of Tibet. It was found that a thick ice layer not covered with snow starts to melt at the ice-water interface due to volumetric solar heating of ice. The results of the calculations are in good agreement with the field observations. The computational analysis showed a dramatic change in the process when the ice is covered with snow. A qualitative change in the physical picture of the process occurs when the snow cover thickness increases to 20–30 cm. In this case, the snow melting precedes ice melting and water ponds are formed on the ice surface. This is typical for the Arctic Sea in polar summer. Known experimental data are used to estimate the melting of sea ice under the melt pond. Positive or negative feedback related to the specific optical and thermal properties of snow, ice, and water are discussed.

SNOWTRAN: A Fast Radiative Transfer Model for Polar Hyperspectral Remote Sensing Applications

In this work, we develop a software suite for studies of atmosphere–underlying SNOW-spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that the aerosol load is extremely low, the proposed theory does not require the numerical procedures for the solution of the radiative transfer equation and is based on analytical equations for the spectral nadir reflectance and simple approximations for the local optical properties of atmosphere and snow. The developed model is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. A new, fast technique for the determination of the snow grain size and assessment of the snowpack vertical inhomogeneity is then proposed and further demonstrated on EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The results revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 that was linked to a warming event and a four times larger grain size at Aviator Glacier compared with Dome C.

Combined effect of algae and dust on snow spectral and broadband albedo

The optical properties of snow can be strongly modified by the presence of a variety of impurities including mineral dust and snow algae. We made use of measured concentration of snow algae and mineral dust to parameterize the BioSNICAR radiative transfer model. Surficial snow samples were gathered during a field campaign on 7th July 2020 at the Presena glacier (Rhaetian Alps). We collected 18 samples of surface snow containing different amount of snow algae and mineral dust. Through radiative transfer simulations we estimated an average broadband albedo reduction of 7.4 ± 6.1 % and 35.3 ± 7.4 % compared to clean snow, caused by snow algae and mineral dust presence, respectively. When we considered the combined effect of snow algae and dust, we estimated a broadband albedo reduction equal to 40.8 ± 8.4 %. We estimated an average instantaneous radiative forcing induced by snow algae, mineral dust and both impurities equals to 42.3 (± 36.1) W/m2, 203.7 (±45.5) W/m2, and 211.8 (±45.9) W/m2, respectively.Using BioSNICAR simulations, we also tested a series of narrowband spectral indices to determine the concentration of mineral dust and snow algae from multi- and hyper-spectral data. Results showed that most spectral indices used for snow algae mapping are correlated also with mineral dust concentration. We found that only an index correlates uniquely with snow algae: the scaled band integral at 680 nm. A new spectral index, namely the Green Blue Normalized Index, is therefore proposed to discriminate mineral dust from snow algae when both impurities are present. The high spectral resolution of current (e.g. PRISMA, EnMAP) and future (e.g. CHIME, SBG) hyperspectral satellite missions will be fundamental to decouple the effect of mineral dust and snow algae on the optical properties of snow. In fact, from those data it is possible to calculate all narrowband indices presented in this study.

Influence of Surface Snow Properties on an 89-GHz Brightness Temperature Extreme Event at Dome Fuji, Antarctica

Influence of Surface Snow Properties on an 89-GHz Brightness Temperature Extreme Event at Dome Fuji, Antarctica

Quantifying short-term changes in snow strength due to increasing liquid water content above hydraulic barriers

The rapid weakening of snow layers that accumulate infiltrating liquid water is a well-known, but poorly quantified, mechanism for wet-snow slab avalanche formation. Therefore, quantifying this mechanical process is a crucial part of forecasting these snow avalanches accurately. Currently, studies do not agree on how snow strength should change as a function of volumetric liquid water content (θ) and whether this relationship differs between snow types. Furthermore, strength measurements taken at or above θ=7% in snow are rare, so there is limited understanding at this end of the θ continuum. These levels of saturation can occur in the snow immediately above hydraulic barriers and are considered important for wet-snow slab avalanches to initiate. To address this knowledge gap, a blade hardness gauge (BHG, a.k.a. thin blade penetrometer) and SLF snow sensor were used to take 349 targeted paired measurements of strength and θ, respectively, in the snow above 19 manually wetted hydraulic (capillary) barriers. Using a multiple regression analysis, we developed an expression for the blade hardness of manually wetted snow as a function of θ, crystal form, and blade hardness prior to wetting (R2=0.85). To understand these results in terms of a common measure of snow strength, we also performed a comparison of the BHG and a shear frame in dry snow. The two instruments are highly linearly correlated (R2=0.94), allowing us to interpret our regression in terms of shear strength when compressive stress is low. Our results demonstrate that short-term (< 2 h) changes in snow strength due to increasing θ can differ between snow layers. These differences can be empirically modelled using easily measured dry snow properties (i.e. crystal form and blade hardness), which could allow avalanche forecasters to be more selective when identifying failure layers in advance of a wetting event.

Evaluation of winter snow properties effects on spring soil moisture using satellite images in the Northwest of Iran

Evaluation of winter snow properties effects on spring soil moisture using satellite images in the Northwest of Iran

Динамика и оценка метеоусловий всесезонного туристско-рекреационного комплекса «Мамисон»

Introduction. By Decree of the Government of Russia dated September 25, 2019 No. 1248, a decision was made to create an all-season tourist and recreational complex “Mamison” on the territory of the Alagirsky district of the Republic of North Ossetia – Alania, in the upper reaches of the Ardon River, the valleys of the Mamikh-Don and Zemegon-Don rivers. One of the most favorable areas is the high-mountain gorge Mamison, where the Zemegon-Don and Kozydon-Don rivers merge, which, downstream, form the Mamikh-Don river. To date, the territory of the Mamison ATRC has been poorly studied in meteorological and hydrological terms. There are practically no studies of the physical properties of snow; the peculiarities of changes in the weight of the snow cover depending on the absolute height have not been identified, as, for example, at the Elbrus ATRC in Kabardino-Balkaria. The work is devoted to the analysis of the dynamics of the average seasonal values of outdoor air temperature, snow cover depth and the amount of precipitation during the cold period (October – May) from 1981 to 2022. On the territory of the Mamison All-Season Tourist and Recreational Complex. Research methods and materials. The work carried out a mathematical and statistical analysis of the meteorological parameters on which the snow avalanche regime depends to the greatest extent. To analyze the average seasonal values of snow depth, air temperatures and total (for the season) amount of precipitation, the work used weather data for the cold period (October – May) obtained at the snow avalanche station (SLS) Roki Pass (from 1981 to 2022). Research results. Mathematical analysis showed that the mid-season snow depth changes linearly, the trend is negative. Mid-season air temperature changes linearly, the trend is also positive. The trend in changes in the seasonal total values of the amount of solid precipitation is positive, the highest amount of precipitation occurs in the spring months – March, April, May (3396 mm, 4809 mm, 4910 mm, respectively). Discussion of research results. The observed changes were manifested in a decrease in the depth of snow cover, a change in precipitation patterns, and an increase in the average seasonal characteristics of the temperature regime. In view of these changes, we can say that the decrease in avalanche activity in the Roki Pass area is associated with interannual changes in the depth of snow cover, which is the main factor in avalanche formation. Conclusions. It was found that from 1981 to 2022, the average annual snow depth decreased by 0.56 cm per year. An expression for the time variation of snow cover height is obtained. Suggestions for practical applications and future research directions. The results obtained have practical significance in the development of mountainous territories and should be taken into account when designing infrastructure facilities of the Mamison ATRC.

Study on the Constitutive Equation and Mechanical Properties of Natural Snow under Step Loading

Snow, as an important component of the cryosphere, holds a crucial role in the construction of polar infrastructure. However, the current research on the mechanical properties of snow is not comprehensive. To contribute to our understanding of the mechanical behaviors of snow in cold regions, uniaxial compression tests under step loading were performed on the snow. With the Maxwell model as the basis, different temperatures, densities, and loading rates were set to establish constitutive equations of snow. The changes in the elastic modulus and viscosity coefficient of snow with respect to three variables were investigated. The results show that the loading rate has no obvious effect on the elastic modulus and viscosity coefficient of snow. Both the elastic modulus and viscosity coefficient of snow follow an exponential function with respect to density, with an increase in density, resulting in a higher value. As temperature decreases, the elastic modulus and viscosity coefficient initially decrease and then increase, whereas no specific functional relationship between them was observed. Additionally, a new constitutive equation considering snow density is derived based on the Maxwell model.

Snow Particle Analyzer for Simultaneous Measurements of Snow Density and Morphology

AbstractThe detailed characterization of snow particles is critical for understanding the snow settling behavior and modeling the ground snow accumulation for various applications such as prevention of avalanches and snowmelt‐caused floods, etc. In this study, we present a snow particle analyzer for simultaneous measurements of various properties of fresh falling snow, including their size, shape, type, and density. The analyzer consists of a digital inline holography module for imaging falling snow particles in a sample volume of 88 cm3 and a high‐precision scale to measure the weight of the same particles in a synchronized fashion. The holographic images are processed in real‐time using a machine learning model and post‐processing to determine snow particle size, shape, and type. Such information is used to obtain the estimated volume, which is subsequently correlated with the weight of snow particles to estimate their density. The performance of the analyzer is assessed using monodispersed spherical glass and foam beads, irregular salt crystals, and thin disks with various shapes with known density, which shows &lt;10% density measurement errors. In addition, the analyzer was tested in a number of field deployments under different snow and wind conditions. The system is able to achieve measurements of various snow properties at single particle resolution and statistical robustness. The analyzer was also deployed for 4 hr of operation during a snow event with changing snow and wind conditions, demonstrating its potential for long‐term and real‐time monitoring of the time‐varying snow properties in the field.

Diagnosis of Antarctic Blowing Snow Properties Using MERRA-2 Reanalysis with a Machine Learning Model

Abstract This paper presents work using a machine learning model to diagnose Antarctic blowing snow (BLSN) properties with the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), data. We adopt the random forest classifier for BLSN identification and the random forest regressor for BLSN optical depth and height diagnosis. BLSN properties observed from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used as the truth for training the model. Using MERRA-2 fields such as snow age, surface elevation and pressure, temperature, specific humidity, and temperature gradient at the 2-m level, and wind speed at the 10-m level as input, reasonable results are achieved. Hourly blowing snow property diagnostics are generated with the trained model. Using 2010 as an example, it is shown that the Antarctic BLSN frequency is much higher over East than West Antarctica. High-frequency months are from April to September, during which BLSN frequency exceeds 20% over East Antarctica. For May 2010, the BLSN snow frequency in the region is as high as 37%. Due to the suppression by strong surface-based inversions, larger values of BLSN height and optical depth are usually limited to the coastal regions, wherein the strength of surface-based inversions is weaker.

Stress measurements in the weak layer during snow stability tests

Abstract The snow compression test is a snow stability test where an isolated column of snow is progressively loaded by tapping on it to induce failure in a possible weak layer. The test result provides valuable information about the propensity of failure initiation within the snowpack. However, different persons might tap with different force and thus reduce the reproducibility of the test results. The aim of this work was to quantify the influence of different test persons and different snowpacks on snow compression test results. We therefore let 62 persons tap on a stress measurement plate during a workshop of the European Avalanche Warning Services. Moreover, in the field, we performed stress measurements during 116 snow compression tests with 13 persons at eight different locations in the Alps. Data on persons’ body features and snow properties were also collected. Our results show that the stresses that reach a weak snow layer due to tapping are influenced by both the snowpack as well as different persons. Still, the data's scattering is surprisingly small for lower loading steps and decreases with depth. Therefore, we can deduce that, especially when avalanche conditions are particularly dangerous, snow compression test results are quite reproducible.

Calorimetric determination of wet snow liquid water content: The effect of test conditions on the calorimeter constant and its impact on the measurement uncertainty

The liquid water content (lwc) of wet snow is a fundamental parameter in determining snow properties like its strength and adhesion force to surfaces. Among the different methods available for the measurement of the lwc, this paper focuses on melting calorimetry: known masses of hot water and snow are mixed into a thermally insulated container, and the lwc is obtained from the difference between the initial hot water temperature and the final mixing temperature; it is a fast and easy to implement method that requires little equipment, making it suitable for both laboratory and field measurements. Tests with “equivalent” wet snow samples whose liquid water content was known in advance were carried out to assess the method, showing that the liquid water content was overestimated by a variable amount that seemed to depend on the test parameters. To account for the heat capacity of the calorimeter, which was found not to be negligible, a constant in terms of equivalent hot water mass (E) was introduced. However, its value also depended on the test conditions. Hence, a correlation between E and the test parameters was found, and the measurements were repeated using a container of a different material, showing a similar behavior. Eventually, a discussion about the effects of choosing different hot water masses and hot water-to-snow mass ratios on measurement accuracy is provided.

A novel global snow depth retrieval method considering snow metamorphism and forest influence

Accurate snow depth (SD) information at global scales is highly important for validation and calibration of hydrological model, snowmelt runoff prediction, and global water supplies estimation. Passive microwave remote sensing has been proven to be an efficient method to detect SD at regional or global scales. However, numerous heterogeneous snow property parameters and forest parameters affect the microwave radiation characteristics, which is a great challenge for SD retrieval. In this study, a novel SD retrieval algorithm that comprehensively considered the multiple sensitive factors was developed at global scales by coupling the Microwave Emission Model of Layered Snowpacks (MEMLS) model and machine learning (ML) technique. The exponential correlation length (Pec), snow density (ρ), and forest cover fraction (fforest) are three sensitive parameters for retrieving SD from passive microwave observations based on the extended Fourier amplitude sensitivity test (EFAST) algorithm. The key parameter Pec, which characterizes the snow microstructure information inside snowpacks, was optimized by minimizing the brightness temperature between satellite observations and microwave radiative transfer model simulations by integrating the MEMLS, forest impact, and atmospheric radiation attenuation. The random forest (RF) ML technique was used, and the optimal combination of independent variables was selected by correlation heatmap and importance ranking of variables. Six selected essential parameters, including Pec, vertical polarized brightness temperature differences between 18.7 and 36.5 GHz, ρ from ERA5, fforest, elevation, and longitude together with target SD output were used to train the RF model. Seventy percent of the entire data at meteorological stations were randomly chosen to train the RF model, and the remaining 30% were adopted as the validation data from 2013 to 2020 to ensure the mutual independence of training data and validation data. Compared with independent in situ measurements, the overall RMSE of SD is 13.6 cm, which was noticeably lower than those of the international SD remote sensing products and reanalysis datasets, including AMSR2 (36.2 cm), GlobSnow 3.0 (25.3 cm), ERA5 (38.2 cm), and ERA5-land (29.9 cm). The results demonstrated that the new proposed algorithm, by coupling the MEMLS model with the ML technique, could significantly enhance the overall performance in SD retrieval at global scales.