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In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use random forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents.

Volatile evolution in protoplanetary discs determines the compositional evolution of forming planets. Below their sublimation temperatures, volatiles freeze out from the vapour phase onto dust grains in the disc and transition to being dynamically-coupled to the dust component as opposed to the gas. The boundary between the ice and vapour phases is referred to as the snow line, when thought of as the mid-plane radius at which the phase transition occurs, or the snow surface, when viewed as a 2D (radial and vertical) structure in the disc. We investigate whether the CO snow line (and therefore snow surface) is thermally unstable and therefore liable to changes in its location during disc evolution using the disc evolution code cuDisc, to which we have added an ice-vapour chemistry solver. We find that the instability does lead to there being two steady-state stable equilibrium solutions for the snow surface when including the vertical structure. However, in dynamically-evolving simulations, the disc does not enter a limit-cycle - as seen in previous 1D models - due to the shape of the 2D snow surface and the vertical transport of volatiles. We therefore expect that dynamically evolu

Snow is a crucial element of the sea ice system, affecting sea ice growth and decay due to its low thermal conductivity and high albedo. Despite its importance, present-day climate models have an idealized representation of snow, often including only single-layer thermodynamics and omitting several processes that shape its properties. Although advanced snow process models like SnowModel exist, they are often excluded from climate modeling due to their high computational costs. SnowModel simulates snow depth, density, blowing-snow redistribution, sublimation, grain size, and thermal conductivity in a multi-layer snowpack. It operates with high spatial (1 meter) and temporal (1 hour) resolution. However, for large regions like the Arctic Ocean, these high-resolution simulations face challenges such as slow processing and large resource requirements. Data-driven emulators are used to address these issues, but they often lack generalizability and consistency with physical laws. In our study, we address these challenges by developing a physics-guided emulator that incorporates physical laws governing changes in snow density due to compaction. We evaluated three machine learning models:

Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely common atmospheric phenomenon that will seriously affect the performance of high-level computer vision tasks, such as object detection and semantic segmentation. Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object. However, the distribution of snow location and shape is complex. Therefore, failure to detect snowflakes / snow streak effectively will affect snow removing and limit the model performance. To solve these issues, we propose a Snow Mask Guided Adaptive Residual Network (SMGARN). Specifically, SMGARN consists of three parts, Mask-Net, Guidance-Fusion Network (GF-Net), and Reconstruct-Net. Firstly, we build a Mask-Net with Self-pixel Attention (SA) and Cross-pixel Attention (CA) to capture the features of snowflakes and accurately localized the location of the snow, thus predicting an accurate snow mask. Secondly, the predicted snow mask is sent into the specially designed GF-Net to adaptively guide the model to remove

Understanding and modeling snow particle dynamics in the atmosphere remains a significant challenge for atmospheric scientists, hydrologists, and glaciologists. Temporally and spatially varying rates of snow transport, deposition, and erosion are driven by atmospheric turbulence and further complicated by inertial particle dynamics. Even with perfectly resolved wind fields, accurately predicting the fate of mobile snow particles in wind relies on semi-empirical assumptions embedded in diffeo-integro equations that contain numerical instabilities. The present research couples a modern approach to snow particle drag with model order reduction tools from nonlinear dynamical systems. Coupled with novel accumulation diagnostics, we provide a simplified framework of snow transport with well-defined simplification errors and rigorous physical meaning.

This paper presents a snow accretion test conducted in a climate wind tunnel to investigate the icing process on a model train. The model used within this experiment was the cleaned-up and 2/3-scaled version of EMU-320, which is a high-speed train in Korea. The model was designed without an electronic power source or heat source so that the wheels did not rotate and snow accretion on the model did not occur due to heat sources. To investigate snow accretion, four cases with different ambient temperatures were considered in the climate wind tunnel on Rail Tec Arsenal. Before analyzing the snow accretion on the train, the snow flux and liquid water content of snow were measured so that they could be used as the input conditions for the simulation and to ensure the analysis of the icing process was based on the characteristics of the snow. Both qualitative and quantitative data were obtained, whereby photographs was used for qualitative analysis, and the density of the snow sample and the thickness of snow accreted on the model were used for quantitative analysis. Based on the visual observations, it was deduced that as the ambient temperature increased, the range of the snow accreted

The microstructure of snow determines its fundamental properties such as the mechanical strength, reflectivity, or the thermo-hydraulic properties. Snow undergoes continuous microstructural changes due to local gradients in temperature, humidity or curvature, in a process known as snow metamorphism. In this work, we focus on wet snow metamorphism, which occurs when temperature is close to the melting point and involves phase transitions amongst liquid water, water vapor, and solid ice. We propose a pore-scale phase-field model that simultaneously captures the three relevant phase-change phenomena: sublimation (deposition), evaporation (condensation), and melting (solidification). The phase-field formulation allows one to track the temperature evolution amongst the three phases and the water vapor concentration in the air. Our three-phase model recovers the corresponding two-phase transition model when one phase is not present in the system. 2D simulations of the model unveils the impact of humidity and temperature on the dynamics of wet snow metamorphism at the pore scale. We also explore the role of liquid melt content in controlling the dynamics of snow metamorphism in contrast t

This study presents a computer vision approach aimed at detecting snow on sidewalks and pavements to reduce winter-related fall injuries, especially among elderly and visually impaired individuals. Leveraging fine-tuned VGG-19 and ResNet50 convolutional neural networks (CNNs), the research focuses on identifying snow presence in pavement images. The dataset comprises 98 images evenly split between snowy and snow-free conditions, evaluated with a separate test set using the F1 score and accuracy metrics. This work builds upon existing research by employing fine-tuned CNN architectures to accurately detect snow on pavements from smartphone-captured images. The methodology incorporates transfer learning and model ensembling techniques to integrate the best predictions from both the VGG19 and ResNet50 architectures. The study yields accuracy and F1 scores of 81.8% and 81.7%, respectively, showcasing the potential of computer vision in addressing winter-related hazards for vulnerable populations.

Snow density estimates below the surface, used with airplane-acquired ice-penetrating radar measurements, give a site-specific history of snow water accumulation. Because it is infeasible to drill snow cores across all of Antarctica to measure snow density and because it is critical to understand how climatic changes are affecting the world's largest freshwater reservoir, we develop methods that enable snow density estimation with uncertainty in regions where snow cores have not been drilled. In inland West Antarctica, snow density increases monotonically as a function of depth, except for possible micro-scale variability or measurement error, and it cannot exceed the density of ice. We present a novel class of integrated spatial process models that allow interpolation of monotone snow density curves. For computational feasibility, we construct the space-depth process through kernel convolutions of log-Gaussian spatial processes. We discuss model comparison, model fitting, and prediction. Using this model, we extend estimates of snow density beyond the depth of the original core and estimate snow density curves where snow cores have not been drilled. Along flight lines with ice-pen

Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a Laplace-VQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the

The 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 concentration, 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 concentration, 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 beads and irregular salt crystals with known density, which shows <5% density measurement errors. In addition, the analyzer was tested in a number of f

Marine snow, the floating particles in underwater images, severely degrades the visibility and performance of human and machine vision systems. This paper proposes a novel method to reduce the marine snow interference using deep learning techniques. We first synthesize realistic marine snow samples by training a Generative Adversarial Network (GAN) model and combine them with natural underwater images to create a paired dataset. We then train a U-Net model to perform marine snow removal as an image to image translation task. Our experiments show that the U-Net model can effectively remove both synthetic and natural marine snow with high accuracy, outperforming state-of-the-art methods such as the Median filter and its adaptive variant. We also demonstrate the robustness of our method by testing it on the MSRB dataset, which contains synthetic artifacts that our model has not seen during training. Our method is a practical and efficient solution for enhancing underwater images affected by marine snow.

Understanding the structure, quantity, and type of snow in mountain landscapes is crucial for assessing avalanche safety, interpreting satellite imagery, building accurate hydrology models, and choosing the right pair of skis for your weekend trip. Currently, such characteristics of snowpack are measured using a combination of remote satellite imagery, weather stations, and laborious point measurements and descriptions provided by local forecasters, guides, and backcountry users. Here, we explore how characteristics of the top layer of snowpack could be estimated while skiing using strain sensors mounted to the top surface of an alpine ski. We show that with two strain gauges and an inertial measurement unit it is feasible to correctly assign one of three qualitative labels (powder, slushy, or icy/groomed snow) to each 10 second segment of a trajectory with 97% accuracy, independent of skiing style. Our algorithm uses a combination of a data-driven linear model of the ski-snow interaction, dimensionality reduction, and a Naive Bayes classifier. Comparisons of classifier performance between strain gauges suggest that the optimal placement of strain gauges is halfway between the bind

Wind-blown snow particles often contaminate Terrestrial Laser Scanning (TLS) data of snow covered terrain. However, common filtering techniques fail to filter wind-blown snow and incorrectly filter data from the true surface due to the spatial distribution of wind-blown snow and the TLS scanning geometry. We present FlakeOut, a filter designed specifically to filter wind-blown snowflakes from TLS data. A key aspect of FlakeOut is a low false positive rate of um{2.8e-4} -- an order of magnitude lower than standard filtering techniques -- which greatly reduces the number of true ground points that are incorrectly removed. This low false positive rate makes FlakeOut appropriate for applications requiring quantitative measurements of the snow surface in light to moderate blowing snow conditions. Additionally, we provide mathematical and software tools to efficiently estimate the false positive rate of filters applied for the purpose of removing erroneous data points that occur very infrequently in a dataset.

The accurate prediction and estimation of annual snow accumulation has grown in importance as we deal with the effects of climate change and the increase of global atmospheric temperatures. Airborne radar sensors, such as the Snow Radar, are able to measure accumulation rate patterns at a large-scale and monitor the effects of ongoing climate change on Greenland's precipitation and run-off. The Snow Radar's use of an ultra-wide bandwidth enables a fine vertical resolution that helps in capturing internal ice layers. Given the amount of snow accumulation in previous years using the radar data, in this paper, we propose a machine learning model based on recurrent graph convolutional networks to predict the snow accumulation in recent consecutive years at a certain location. We found that the model performs better and with more consistency than equivalent nongeometric and nontemporal models.

Existing learning-based atmospheric particle-removal approaches such as those used for rainy and hazy images are designed with strong assumptions regarding spatial frequency, trajectory, and translucency. However, the removal of snow particles is more complicated because it possess the additional attributes of particle size and shape, and these attributes may vary within a single image. Currently, hand-crafted features are still the mainstream for snow removal, making significant generalization difficult to achieve. In response, we have designed a multistage network codenamed DesnowNet to in turn deal with the removal of translucent and opaque snow particles. We also differentiate snow into attributes of translucency and chromatic aberration for accurate estimation. Moreover, our approach individually estimates residual complements of the snow-free images to recover details obscured by opaque snow. Additionally, a multi-scale design is utilized throughout the entire network to model the diversity of snow. As demonstrated in experimental results, our approach outperforms state-of-the-art learning-based atmospheric phenomena removal methods and one semantic segmentation baseline on t

Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity (albedo), and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. In this paper, we develop a simple model of pre-melt snow topography that accurately describes snow cover of flat, undeformed Arctic sea ice on several study sites for which data was available. The model considers a surface that is a sum of randomly sized and placed "snow dunes" represented as Gaussian mounds. This model generalizes the "void model" of Popović et al. (2018) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the pre-melt snow topography. We show that the model snow-depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage ev

In 2020, there was a record heavy snowfall owing to climate change. In reality, 2,000 vehicles were stuck on the highway for three days. Because of the freezing of the road surface, 10 vehicles had a billiard accident. Road managers are required to provide indicators to alert drivers regarding snow cover at hazardous locations. This study proposes a deep learning application with live image post-processing to automatically calculate a snow hazard ratio indicator. First, the road surface hidden under snow is translated using a generative adversarial network, pix2pix. Second, snow-covered and road surface classes are detected by semantic segmentation using DeepLabv3+ with MobileNet as a backbone. Based on these trained networks, we automatically compute the road to snow rate hazard index, indicating the amount of snow covered on the road surface. We demonstrate the applied results to 1,155 live snow images of the cold region in Japan. We mention the usefulness and the practical robustness of our study.

In engineering applications snow often undergoes large and fast deformations. During these deformations the snow transforms from a sintered porous material into a granular material. In order to capture the fundamental mechanical behavior of this process a discrete element (DE) model is the physically most appropriate. It explicitly includes all the relevant components: the snow microstructure, consisting of bonded grains, the breaking of the bonds and the following rearrangement and interaction of the loose grains. We developed and calibrated a DE snow model based on the open source DE code liggghts. In the model snow grains are represented by randomly distributed elastic spheres connected by elastic-brittle bonds. This bonded structure corresponds to sintered snow. After applying external forces, the stresses in the bonds might exceed their strength, the bonds break, and we obtain loose particles, corresponding to granular snow. Model parameters can be divided into temperature dependent material parameters and snow type dependent microstructure parameters. The model was calibrated by angle of repose experiments and several high strain rate mechanical tests, performed in a cold lab