Dominance of grain size impacts on seasonal snow albedo at deforested sites in New Hampshire

Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate

Simulations of snow distribution and hydrology in a mountain basin

We applied a version of the Regional Hydro‐Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind‐driven sublimation to Loch Vale Watershed (LVWS), an alpine‐subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind‐driven sublimation was necessary to predict moisture losses

SNOWMIP2: An evaluation of forest snow process simulation

The Northern Hemisphere has large areas that are forested and seasonally snow covered. Compared with open areas, forest canopies strongly influence interactions between the atmosphere and snow on the ground by sheltering the snow from wind and solar radiation and by intercepting falling snow, and these influences have important consequences for the meteorology, hydrology and ecology of forests. Many of the land surface models used in meteorological and hydrological forecasting now include representations of canopy snow processes, but these have not been widely tested in comparison with observations. Phase 2 of the Snow Model Intercomparison Project (SnowMIP2) was therefore designed as an intercomparison of surface mass and energy balance simulations for snow in forested areas. Model forcing and calibration data for sites with paired forested and open plots were supplied to modelling groups. Participants in 11 countries contributed outputs from 33 models, and results are published here for sites in Canada, the USA and Switzerland. On average, the models perform fairly well in simulating snow accumulation and ablation, although there is a wide inter-model spread and a tendency to underestimate differences in snow mass between open and forested areas. Most models capture the large differences in surface albedos and temperatures between forest canopies and open snow well. There is, however, a strong tendency for models to underestimate soil temperatures under snow, particularly for forest sites, and this would have large consequences for simulations of runoff and biological processes in the soil

Plant phenology and seasonal nitrogen availability in Arctic snowbed communities

Thesis (M.S.) University of Alaska Fairbanks, 2006This study was part of the International Tundra Experiment (ITEX) and examined the effects of increased winter snow depth and decreased growing season length on the phenology of four arctic plant species (Betula nana, Salix pulchra, Eriophorum vaginatum, and Vaccinium vitis-idaea) and seasonal nitrogen availability in arctic snowbed communities. Increased snow depth had a large effect on the temporal pattern of first date snow-free in spring, bud break, and flowering, but did not affect the rate of plant development. By contrast, snow depth had a large qualitative effect on N mineralization in deep snow zones, causing a shift in the timing and amount of N mineralized compared to ambient snow zones. Nitrogen mineralization in deep snow zones occurred mainly overwinter, whereas N mineralization in ambient snow zones occurred mainly in spring. Concentrations of soil dissolved organic nitrogen (DON) were approximately 5 times greater than concentrations of inorganic nitrogen (DIN) and did not vary significantly over the season. Projected increases in the depth and duration of snow cover in arctic plant communities will likely have minor effects on plant phenology, but potentially large effects on patterns of N cycling

A Digital Archive of Northern Hemisphere Snow Cover, November 1966 through December 1980

The purpose of this article is to acquaint the research community with a new data base—a digitized archive of Northern Hemisphere snow cover. Historically, those researchers who needed snow cover data for climatic and atmospheric boundary layer studies have had to rely on the irregularly spaced (and in some regions, sparse) grid of point observations. Northern Hemisphere Weekly Snow and Ice Cover Charts, which are created from analyzed satellite imagery at the National Earth Satellite Service (NESS), have been available on an operational basis since late 1966. Each of these weekly charts for the period November 1966 through December 1980 was digitized and stored in a new data archive. Snow cover area and snow cover frequency climatologies were created and examples are presented. The significance of this unique data archive is examined by comparing the 14-year mean annual snow cover frequency climatology with several published snow cover climatologies. The potential uses for this data archive in meteorological and climatological studies also are reviewed

Snow metamorphism: a fractal approach

Snow is a porous disordered medium consisting of air and three water phases: ice, vapour and liquid. The ice phase consists of an assemblage of grains, ice matrix, initially arranged over a random load bearing skeleton. The quantitative relationship between density and morphological characteristics of different snow microstructures is still an open issue. In this work, a three-dimensional fractal description of density corresponding to different snow microstructure is put forward. First, snow density is simulated in terms of a generalized Menger sponge model. Then, a fully three-dimensional compact stochastic fractal model is adopted. The latter approach yields a quantitative map of the randomness of the snow texture, which is described as a three-dimensional fractional Brownian field with the Hurst exponent H varying as continuous parameter. The Hurst exponent is found to be strongly dependent on snow morphology and density. The approach might be applied to all those cases where the morphological evolution of snow cover or ice sheets should be conveniently described at a quantitative level

The effects of variation in snow properties on passive microwave snow mass estimation

Estimating snow mass at continental scales is difficult, but important for understanding land-atmosphere interactions, biogeochemical cycles and the hydrology of the Northern latitudes. Remote sensing provides the only consistent global observations, butwith unknown errors. Wetest the theoretical performance of the Chang algorithm for estimating snow mass from passive microwave measurements using the Helsinki University of Technology (HUT) snow microwave emission model. The algorithm's dependence upon assumptions of fixed and uniform snow density and grainsize is determined, and measurements of these properties made at the Cold Land Processes Experiment (CLPX) Colorado field site in 2002–2003 used to quantify the retrieval errors caused by differences between the algorithm assumptions and measurements. Deviation from the Chang algorithm snow density and grainsize assumptions gives rise to an error of a factor of between two and three in calculating snow mass. The possibility that the algorithm performsmore accurately over large areas than at points is tested by simulating emission from a 25 km diameter area of snow with a distribution of properties derived from the snow pitmeasurements, using the Chang algorithm to calculate mean snow-mass from the simulated emission. The snowmass estimation froma site exhibiting the heterogeneity of the CLPX Colorado site proves onlymarginally different than that from a similarly-simulated homogeneous site. The estimation accuracy predictions are tested using the CLPX field measurements of snow mass, and simultaneous SSM/I and AMSR-E measurements

DesnowNet: Context-Aware Deep Network for Snow Removal

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 the proposed Snow100K dataset in both qualitative and quantitative comparisons. The results indicate our network would benefit applications involving computer vision and graphics

Studies on Physical Properties of Snow Based on Multi Channel Microwave Radiometer

The analysis of the data observed over a snow field with a breadboard model of MSR (microwave scanning radiometer) to be installed in MOS-1 (Marine Observation Satellite-1) indicates that: (1) the influence of incident angle on brightness temperature is larger in horizontal polarization component than in vertical polarization component. The effect of incident angle depends upon the property of snow with larger value for dry snow; (2) the difference of snow surface configuration consisting of artifically made parallel ditches of 5 cm depth and 5 cm width with spacing of 10 and 30 cm respectively which are oriented normal to electrical axis do not affect brightness temperature significantly; and (3) there is high negative correlation between brightness temperature and snow depth up to the depth of 70 cm which suggests that the snow depth can be measured with a two channel microwave radiometer up to this depth