Search

Background: Snow forms when tiny ice crystals in clouds stick together to become snowflakes. If enough crystals stick together, they become heavy enough to fall to the ground. Where background includes Precipitation falls as snow when the air temperature is below 2 °C (275.15 K). The falling snow does begin to melt as soon as the temperature rises above freezing, but as the melting process begins, the air around the snowflake is cold. Objective: It is a myth that it needs to be below 0 °C (273.15) K to snow. In Iraq, the heaviest snowfalls tend to occur when the air temperature is between (273.15-275.15) K (0-2) °C. Methods: The data for this study, which includes Temperature (T), Snow Albedo (SA), and Snow Density (SD) as monthly-daily mean, taken from the European Center for Medium-Range Weather Forecasts (ECMWF) for fifteen years from 2008 to 2022 for several selected stations over northern Iraq. The method was to take the monthly rates of snow density, snow albedo, and temperature for the stations of Erbil, Sulaymaniyah, Zakho, Dohuk, and Amadiyah, and the type of relationship and strength of the connection between them was also known. Results: The study found an inverse relationship between snow albedo and snow density across the selected stations, indicating that an increase in snow density leads to a decrease in snow albedo. Notably, Duhok City exhibited the strongest relationship between snow albedo and density, with a regression coefficient of 0.9699 compared to other regions. Conclusions: This study highlights the complex relationship between snow albedo and density in northern Iraq. The strong correlation observed in Duhok City suggests the importance of further research to understand the factors influencing snow properties in this region.

In this study, we analyzed the accuracy of snow observations at weather stations compared to the data of snow measurements in the vicinity of these stations. Also, the variations of measurement errors were estimated considering the inter-annual snowpack variability and landscape heterogeneity of the river basins. The studies were conducted in three catchments in the south of Western Siberia: forest-steppe the Kasmala River (2011–2020), low mountain the Mayma River (2015–2020), and steppe the Kuchuk River (2019–2020). The results showed that the accuracy of snow measurements at the weather stations was higher in the low mountain catchment than in the plain basins. Interannual differences in precipitation combined with wind transport influenced the most significant errors in the Kasmala catchment (relative error of snow depth on the snow gauge – 46,3%, and SWE on the permanent course – 17,3%). However, in the Mayma catchment, the snow depth measurements on the snow gauge agreed well with the catchment means in all years (mean relative error 7,7%). The relative error of snow depth measurements on the snow gauge in the Kuchuk catchment was 7,5%, and of SWE on the permanent snow course was 19,1%. The small snow depth error occurred due to the composition of the error distribution and large differences between open and forested areas.

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.

The results of processing of a profile snow-measuring survey of snow cover in the Khibiny Mountains are presented. The survey was performed during the period of maximum snow accumulation (March of 2020) on the main elements of the landscape: mixed forest on the plain, open woodlands at the bottom of valleys, plateaus, wooded slopes, and upper slopes without woody vegetation. The averaged values of snow storage for different types of the landscapes were obtained for the period of the maximum snow accumulation in the snowy winter of 2019/20. The maximum snow storage (> 700 mm w.e.) was determined for areas on the high plateaus and open woodlands at the bottom of valleys. Minimum snow storage (> 400 mm w.e.) was recorded in areas of mixed forest on the plain and on an ice cover of lakes. Measurements of snow depth were carried out by the standard method (a handspike) and the ground-based radio-echo sounding using georadar with the frequency of 1600 MHz. The accuracy of this method allows measuring of the snow depth with accuracy of 1 cm for a dense snow and 2 cm for a loose one. Thus, the accuracy of measuring the snow depth with the radar is comparable to the accuracy of a handspike. A large number of radar measurements of snow depth on the profiles makes possible to determine the spatial variability of this value and its statistical characteristics. As a result, a vertical gradient of snow accumulation was defined as 25 mm w.e. per 100 m. The smallest spatial variability of snow depth was observed on profiles in the forests on the plain, in woodlands, and on the upper slopes. On profiles with complex relief (plateau, lower slopes), the spatial variability of snow depth is significant – the standard deviation was within limits of 30%. Based on the results of processing the field data, a map of snow storage over the studying area during the period of maximum snow accumulation was constructed. When constructing the map, we took into account the averaged data of the measurements for each type of landscape, the boundaries of woody vegetation, the height, steepness of slopes, and the high-altitude gradient of snow accumulation. It was found that features of the spatial distribution of snow cover were primarily due to the location of natural landscape complexes. The role of changes in snow storages with altitude was found to be insignificant.

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 τ-ω vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters (τ, ω) and soil roughness (SD) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m3 for all stations, with a mean bias of 9.4 kg/m3. The RMSE can be further reduced if an artificial tuning of three predetermined parameters (τ, ω, and SD) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics.

This study aims to present and analyze the time series of the snow parameters focusing on representative geographical areas of the Eastern Mediterranean (i.e., Greece and Italy) and to examine their seasonal variability, in terms of region and geography. The satellite retrievals were firstly validated against in-situ retrievals for 67 common days, with a mean bias equal to −0.018 cm, with a near-Gaussian distribution, showing the good performance of the satellite snow detection. The satellite-based analysis resulted in increasing trends of snow water equivalent, attributed to the enhanced values between 2000 and 2009; however, decreasing trends are found starting from 2010 until now of −1.79 × 10−17 and −2.31 × 10−18 over the two representative areas of Greece (e.g., Thessaloniki and Kozani). A similar pattern is found for the snow water equivalent in the Italian study area, with a decreasing trend of −4.45 × 10−18. The presented results contribute to a better understanding of the spatial snow distribution and the snow coverage seasonality that could be crucial for the long-term groundwater management, by combining snow data trends from in-situ data and satellite statistics.

The relationship between the results of calculations of the dynamics of the temperature regime of the in freezing and thawing soil profile with the heating effect of the snow cover is considered. To analyze this connection, two coupled models are used: the model of formation and degradation of snow cover in winter and the model of heat transfer and soil moisture transport in underlying vadoze zone profile. Parametrization of the influence of the snow cover, which at each calculated moment of time has the current average density and depth, on the dynamics of the temperatures of the soil profile is due to the use of its specific thermal resistance, which depends on its current depth and the thermal conductivity coefficient. The coefficient of thermal conductivity of the snow cover is related with its density using six different published empirical relationships. Modeling of heat transfer in freezing and thawing soil is carried out on the example of the field site for monitoring the thermal regime located on the territory of the Zvenigorod Biological Station of Moscow State University. It is shown that the well-known relationships give similar curves for the dynamics of the depth of seasonal freezing, including the degradation of the seasonal freezing layer in the spring period, with the same dynamics of the snow cover. However, the maximum penetration depth of the zero isotherm differs significantly for different snow conductivity-snow density relationships. The tested six relationships were divided into three groups. Minimal freezing is provided by the Sturm model and the effective medium model. The average and rather poorly differentiating freezing from each other is given by the Pavlov, Osokin et al. and Jordan relationships. The greatest value of the freezing depth is obtained with using Pavlov’s relationship with a temperature correction.

Accurate snowfall forecasting and quantitative snowfall estimation remain challenging due to the complexity and variability of snow microphysical properties. In this paper, the microphysical characteristics of snowfall in the Yanqing mountainous area of Beijing are investigated by using a Particle Size and Velocity (PARSIVEL) disdrometer. Results show that the high snowfall intensity process has large particle-size distribution (PSD) peak concentration, but the distribution of its spectrum width is much smaller than that of moderate or low snowfall intensity. When the snowfall intensity is high, the corresponding Dm value is smaller and the Nw value is larger. Comparison between the fitted μ−Λ relationship and the relationships of different locations show that there are regional differences. Based on dry snow samples, the Ze−SR relationship fitted in this paper is more consistent with the Ze−SR relationship of dry snow in Nanjing, China. The fitted ρs−Dm relationship of dry snow is close to the relationship in Pyeongchang, Republic of Korea, but the relationship of wet snow shows greatly difference. At last, the paper analyzes the statistics on velocity and diameter distribution of snow particles according to different snowfall intensities.

The results of experimental investigation of thermal conductivity of snow on the Svalbard archipelago in the conditions of natural occurrence are considered. The observations were carried out in the spring of 2013–2015 in the vicinity of the meteorological station «Barentsburg». The obtained data were processed using the Fourier equation of thermal conductivity that allowed determination of the coefficient t of thermal conductivity of the snow with different structure and density. The thermal conductivity of snow depends on the contacts between ice crystals. The larger the contact area, the better the heat transfer from one layer to another. But the strength characteristics of snow, and especially its hardness, depend on the bonds between ice crystals, so the thermal conductivity and hardness of snow depend on the structure of snow. Note, that measurements of snow hardness are less laborious than measurements of its thermal conductivity. For layers of snow cover of different hardness the relationship between snow thermal conductivity and its density has been established. To verify the reliability of the approach to the determination of snow thermal conductivity, numerical experiments were performed on a mathematical model, which did show good convergence of the results. The obtained formulas for the coefficient of thermal conductivity of very loose, loose, medium and hard snow (according to the international classification of seasonal snow falls) are compared with the data of other studies. It was found that when the snow density is within the range 0.15–0.40 g/cm3 these formulas cover the main variety of thermal conductivity of snow. This allows estimating the coefficient of thermal conductivity and to determine the thermal resistance of snow cover in the field by measuring the density and hardness of different layers of snow.

Improving the estimation of snow density is a key task in current snow research. Characterization of the variability of density in time and space is essential for the estimation of water equivalent, hydroelectric power production, assessment of natural hazards (avalanches, floods, etc.). Hyperspectral imaging is proving to be a promising and reliable tool for monitoring and estimating this physical property. Indeed, the spectral reflectance of snow is partly controlled by changes in its physical properties, particularly in the near-infrared (NIR) part of the spectrum. For this purpose, several models have been designed to estimate snow density from spectral information. However, none has yet achieved significant performance. One of the major difficulties is that the relationship between snow density and spectral reflectance is non-bijective (surjective). Indeed, several reflectance amplitudes can be associated with the same density and vice versa, so the correlation between density and spectral reflectance can be very poor. To resolve this issue, a hybrid snow density estimation model based on spectral data is proposed in this work. The principle behind this model is to classify the snow density prior to its estimation by means of a specific estimator corresponding to a predetermined snow density class. These additional steps eliminate the surjective relation by converting it into three bijective relations between density and spectral reflectance. The calibration step showed that the densities included within the three classes are sensitive to different spectral regions, with R2 > 0.80. The results of the cross-validation for the specific estimators were also satisfactory with R2 > 0.78 and RMSE < 36.36 kg m−3. The overall performance of the hybrid model (HM), when tested with independent data, demonstrated the effectiveness of using proximal NIR hyperspectral imagery to estimate snow density (R2 = NASH = 0.93).

The paper presents characteristics of a snow cover observed on the Lagonaky high plateau (the Western Caucasus) in the interfluve of the rivers Belaya and Pshekha along two snow-measuring courses. The period of observations is 1973–2015, and the altitude interval is 460–2020 m. The characteristics measured are the following: depths of snow cover, the snow density, and temperature of different snow layers in the test pits. The stratigraphy of the snow thickness is described. In different years, observations did show changes in the depths which felt within a wide range of values from a few centimeters up to 5–6 m at the same points. The observations allowed determination of dates of snow appearance (setting-up of snow cover) as well as the disappearance in different parts of the Lagonaky high plateau. Durations of the snow cover in different altitudinal belts were also determined. By the end of March the snow cover disappears to altitudes of 700 m, and after 20th of April the slopes become free of snow to 1000 m. The snow cover duration in the low-altitude zone (500–1000 m) is 70–80 days, while in the middle altitudes (1,000–1,500 m) it is 130 days. In the highlands (above 2000 m), the snow cover lasts for 215 days. It must be emphasized that during the last five years the duration of snow cover has changed significantly. Materials of snow surveys showed that snow depths and water contents (water equivalents of snow cover) significantly differ from year to year. In seasons 1975/76, 1978/79, 19861/87, 1989/90 the winters were extremely snowy, while winters in 1976/77, 1979/80, 1983/84, 1990/91, 2013–2015 had small amount of snow. According to data of distant snow-measuring sticks, the maximum values of the snow depth (610 cm) have been recorded in March of 1976 near the tourist shelter Fisht (1596 м). Significant changes in the distribution of snow cover in recent years were probably related to the global climate changes.

In recent years, ground-penetrating radars are widely used for measuring thickness and liquid water content in snow cover on land and glaciers. The measurement accuracy depends on radio wave velocity (RWV) adopted for calculations. The RWV depends mainly on density, water content and structure of the snow cover and ice layers in it. The density and wetness of snow, and its structure can be estimated from data on RWV, using the available experimental and theoretical relations. Satisfactory results can be obtained using the Looyenga’s (1965) equations to estimate the density and wetness of snow cover, and equations of van Beek’s (1967) showing the distinction between RWV speeds velocities in snow cover and ice layers with different prevailing orientation and sizes of air or water inclusions.RWV in dry snow with density 300 kg/m3 may vary by 32 m/µs, depending on whether the vertical or horizontal orientation of the air inclusions prevails therein. In ice with density 700 kg/m3 effect of air inclusions orientation on differences in RWV is reduced to 5 m/µs. If the inclusions are not filled with air but with water, the difference in RWV in snow is 21 m/µs, and in ice is 24 m/µs. The RWV is affected not only by orientation of the inclusions, but their elongation. Twofold elongation of ellipsoidal air and water inclusions increases the difference in RWV in snow (with a density 300 kg/m3 ) to 23 m/µs and 22 m/µs.These estimates show a noticeable influence of snow structure on RWV in snow cover. The reliability of the above RWV estimates depends significantly on a thermal state of the snow cover, and decreases during snowmelt and increases in the cold period. It strongly depends on accuracy of measurements of the RWV in snow cover and its separate layers. With sufficiently high accuracy of the measurements this makes possible to detect and identify loose layers of deep hoar and compact layers of infiltration and superimposed ice, which is important for studying the liquid water storage of snow cover and a glacier mass balance. Therefore, considerable attention should be given to accuracy of the RWV measurements in dry and wet snow cover and its individual layers. With sufficiently high accuracy of measurements of the RWV, this should allow revealing such layers and estimating their thickness and average density.

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R > 0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm3. This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique.

During the 45th–57th Russian Antarctic Expeditions (1999–2012) an intensive program of glaciological and geophysical observations have been carried out in the region between the subglacial Lake Vostok and the Ridge B, Сentral Antarctica. In this work we present the instrumentally obtained snow accumulation data along the northern (NVFL) and southern (VFL) ice flow lines passing through the Lake Vostok. The mean accumulation rate at the NVFL and VFL profiles is 29–37 kg m-2 yr-1 and 21–24 kg m-2 yr-1, respectively. Thus, this region is characterized by a strong latitudinal gradient of the snow accumulation rate, while the longitudinal one is an order of magnitude less. The spatial distribution of the accumulation rate along the VFL is characterized by a sharp variability attributed to the snow re-deposition due to the wind interplay with the local glacier surface slope anomalies. The new data on the stable water isotope concentration (18O) along the VFL profile are also presented.

Spatial variations of avalanche formation parameters in the relation with the explosive mass and its location have been experimentally studied. The charges were installed in five locations: on the ground, in the snow layer at a height of 0.6 m above the ground, on the snow surface, at a height of 1 m and 2 m above the snow surface. Mass of explosive was 0.5, 1, 2 and 4 kg. The experiments were in March 2012 on the Suoluayv plateau (600 m a.s.l.) in Khibiny Mountains. Studies have shown that at explosion sites the disturbance of snow structure, its density and shear strength occurs throughout the whole depth of snow cover, depending on the structure and density of snow layers as well as the explosive mass and its location.

This paper presents results of field investigations of coefficient of efficient thermal conductivity of snow with different structures and densities. Observations were performed in spring of 2013 in the vicinity of meteorological station Barentsburg. The data obtained were processed by means of the Fourier technique that allowed deriving relationship between thermal conductivity and snow temperature in regimes of cooling and warming of the snow cover surface. It was found that coefficient of efficient thermal conductivity increases with rising of the snow temperature in the regime of the snow surface cooling, and it does decrease under regime of warming. This can be possibly caused by the following: under the snow surface warming a water vapor flux moves inward, and when the snow temperature drops the water vapor condensation grows. That results in additional temperature rise and creates effect of growth of thermal conductivity at the lower temperature. When the snow surface cools down this effect is absent, and when the snow temperature drops a contribution of water vapor diffusion into thermal conductivity also decreases, and as a result, the coefficient drops too. Average value of coefficient of efficient thermal conductivity of the depth hoar with density of 280 kg/m3 is 0.12 W/(mK) that in 3–4 times smaller than the same coefficient of granular and frozen together snow with density of 370–390 kg/m3.

A new snow layer probing device with a rectangular section is described in the paper. This device allows receiving snow samples with the undisturbed structures of snow layers, and more accurately reflects the actual value of the snow water equivalent. Application of a new snow probing device in snow measurements provides a new way to organize the monitoring dynamics of snow density layer variability, to estimate density of certain snow types in the snow cover, including the total or vertically averaged one, and to visualize the features of the snow stratigraphy structure. Snow layer probing device with a rectangular section as opposed to the cylindrical weight snow devices has constructive advantages. It also can be used in the calibration weight snow measurement devices that will ensure the comparability of the snow shooting results made in different climatic zones.

A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The input to this algorithm is a simple ray model that includes a speculary reflected signal along with a direct signal. These signals are transmitted from the global positioning system satellites at 1.57542 GHz with right-hand circularly polarization. The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. The results from this nonlinear algorithm show potential for inferring snow water equivalent using GPS multipath signals.

Ground permittivity and snow density retrievals for the “snow-free period”, “cold winter period”, and “early spring period” are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and multi-angle two-parameter retrieval algorithms employed during each of the aforementioned three periods is assessed using in-situ measured ground permittivity and snow density. Additionally, a synthetic sensitivity analysis is conducted that studies melting effects on the retrievals in the form of two types of “geophysical noise” (snow liquid water and footprint-dependent ground permittivity). Experimental and synthetic analyses show that both types of investigated “geophysical noise” noticeably disturb the retrievals and result in an increased correlation between them. The strength of this correlation is successfully used as a quality-indicator flag for the purpose of filtering out highly correlated ground permittivity and snow density retrievals. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Experimental and synthetic retrievals are performed in retrieval modes RM = “H”, “V”, and “HV”, where brightness temperatures from polarizations p = H, p = V, or both p = H and V are used, respectively, in the retrieval procedure. Our analysis shows that retrievals for RM = “V” are predominantly least prone to the investigated “geophysical noise”. The presented experimental results indicate that retrievals match in-situ observations best for the “snow-free period” and the “cold winter period” when “geophysical noise” is at minimum.

Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15–50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014–2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (TB) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, TB was negatively correlated with soil permittivity (εG). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects TB. However, because of the effect of snow on L-Band emission, the correlation between TB and εG decreased with snow accumulation. When compared to satellite measurements, the average TB of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) TB with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., TB increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms.