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The specific surface area (SSA) of snow is directly measured by X-ray computed tomography or indirectly using the reflectance of near-infrared light. The IceCube is a well-established spectroscopic instrument using a near-infrared wavelength of 1310 nm. We compared the SSA of six snow types measured with both instruments. The IceCube measured significantly higher values with a relative percentage difference between 20 to 52 % for snow types with an SSA between 5 to 25 m2 kg−1. There is no significant difference for snow with an SSA between 30 to 80 m2 kg−1. The difference is statistically significant between snow types but not uniquely related to the SSA. We suspected that artificially created particles were the source of the difference. These were sampled, measured and counted. Numerical simulations with radiation transfer solver TARTES confirm the observation. [ABSTRACT FROM AUTHOR]

Accurate remote sensing-based snow water equivalent (SWE) estimates have been elusive, particularly in mountain areas, however, there now appears to be some potential for direct satellite-based SWE observations along ground tracks that only cover a portion of a spatial domain (e.g., watershed). Fortunately, spatiotemporally continuous meteorological and surface variables could be leveraged to infer SWE in the gaps between satellite ground tracks. Here, we evaluate statistical and machine learning (ML) approaches to perform a track-to-area (TTA) transformation of synthetic SWE observations in California's Upper Tuolumne River Watershed. We construct relationships between multiple meteorological and surface variables and synthetic SWE observations along observation tracks, and we then extend this relationship to unobserved areas between ground tracks to estimate SWE over the entire watershed. Domain-wide April 1st SWE inferred using two satellite tracks (~4.5 % basin coverage) resulted in percent error of basin-averaged SWE of 24.5 %, 4.5 %, and 6.3 % in an extreme dry year (WY2015), a normal year (WY2008) and an extraordinarily wet year (WY2017), respectively. Assuming a 10-day overpass interval, percent errors in basin-averaged SWE in both snow accumulation and snowmelt seasons were mostly less than 10 %. We employ feature sensitivity analysis to overcome the black-box nature of ML methods and increase the explainability of the ML results. Our feature sensitivity analysis shows that precipitation is the dominant variable controlling the TTA SWE estimation, followed by net longwave radiation. We find a modest increase in SWE estimation accuracy when more than two ground tracks are leveraged. Accuracy of Apr 1st SWE estimation is only modestly improved for track repeats more often than about 15 days. [ABSTRACT FROM AUTHOR]

The spatial characteristics and patterns of snow accumulation and ablation inform the amount of water stored and subsequently available for runoff and the timing of snowmelt. This paper characterizes the snow accumulation phase to investigate the spatiotemporal snow water equivalent (SWE) distribution by fitting a function to the trajectory plot of the standard deviation versus mean SWE across a domain. Data were used from 90 snow stations for a 34-year period across the Southern Rocky Mountains in the western United States. The stations were divided into sub-sets based on elevation, latitude, and the mean annual maximum SWE. The best function was a linear fit, excluding the first 35 mm of SWE. There was less variability with SWE data compared to snow depth data. The trajectory of the accumulation phase was consistent for most years, with limited correlation to the amount of accumulation. These trajectories are more similar for the northern portion of the domain and for below average snow years. This work could inform where to locate new stations, or be applied to other earth system variables. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important abiotic variables related to plant growth. Using standardised measurements improves the comparability and quality of precipitation data as well as all other data within the Integrated Carbon Observation System network. Despite the spatial and temporal variation of some types of precipitation, a single point measurement satisfies the requirement as an ancillary variable for eddy covariance measurements. Here the term precipitation includes: rain, snowfall (liquid water equivalent) and snow depth, with the latter two being of interest only where occurring. Weighing gauges defined as Integrated Carbon Observation System standard with the capacity of continuously measuring liquid and solid precipitation are installed free-standing, away from obstacles obstructing rain or snowfall. In order to minimise wind-induced errors, gauges are shielded either naturally or artificially to reduce the adverse effect of wind speed on the measurements. Following standardised methods strengthens the compatibility and comparability of data with other standardised environmental observation networks while opening the possibility for synthesis studies of different precipitation measurement methodologies and types including a wide range of ecosystems and geolocations across Europe. [ABSTRACT FROM AUTHOR]

Abstract A coupled model resulting from the coupling of a sub-snowmelt model and a sub-snowdrift model has been proposed to simulate the snow distribution on roofs. Firstly, based on energy and mass balance, a multi-layer roof snowmelt model, in which the snow accumulation, compaction, melting, and refreezing processes can be considered, was developed in this paper. Then, the snowmelt model was coupled to a quasi-steady snowdrift model to predict the snow distribution on building roofs. With the coupled model, the convection and turbulent diffusion of snow particles caused by wind can be predicted through the sub-snowdrift model by Computational fluid dynamics (CFD) simulations. On the other hand, the variations of snow properties, especially the threshold friction velocity, are simulated by the sub-snowmelt model based on mass and energy balance. Finally, three models (the coupled model, the snowmelt model used individually and the snowdrift model used individually) were applied to simulate the snow distribution on a flat roof, respectively. The characteristics of snow distribution simulated by the coupled model was compared with those simulated when the snowdrift model or the snowmelt model was applied individually. Highlights • A coupled model resulting from coupling of a sub-snowmelt model and a sub-snowdrift model is proposed. • The coupling variables between two sub-models are mass flux of drifting snow and threshold friction velocity. • The snow distribution simulated by the coupled model is compared with that simulated by the two sub-models individually. [ABSTRACT FROM AUTHOR]

Precipitation represents a life‐critical energy and hydrologic exchange between the Earth's atmosphere and its surface. As such, knowledge of where, when and how much rain and snow falls is essential for scientific research and societal applications. Building on the 17‐year success of the Tropical Rainfall Measurement Mission (TRMM), the Global Precipitation Measurement (GPM) Core Observatory (GPM‐CO) is the first US National Aeronautical and Space Administration (NASA) satellite mission specifically designed with sensors to observe the structure and intensities of both rain and falling snow. The GPM‐CO has proved to be a worthy successor to TRMM, extending and improving high‐quality active and passive microwave observations across all times of day. The GPM‐CO launched in early 2014, is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA), with sensors that include the NASA‐provided GPM Microwave Imager and the JAXA‐provided Dual‐frequency Precipitation Radar. These sensors were devised with high accuracy standards enabling them to be used as a reference for inter‐calibrating a constellation of partner satellite data. These inter‐calibrated partner satellite retrievals are used with infrared data to produce merged precipitation estimates at temporal scales of 30 min and spatial scales of 0.1° × 0.1°. Precipitation estimates from the GPM‐CO and partner constellation satellites, provided in near real time and later reprocessed with all ancillary data, are an indispensable source of precipitation data for operational and scientific users. Advances have been made using GPM data, primarily in improving sensor calibration, retrieval algorithms, and ground validation measurements, and used to further our understanding of the characteristics of liquid and frozen precipitation and the science of water and hydrological cycles for climate/weather forecasting. These advances have extended to societal benefits related to water resources, operational numerical weather prediction, hurricane monitoring, prediction, and disaster response, extremes, and disease. The GPM mission has provided key new insights across the spectrum of liquid and frozen precipitation. GPM's precipitation data, especially near‐real‐time estimates, are indispensable for operational and scientific users and are used to further our understanding of liquid and frozen microphysics and the science of water and hydrological cycles for climate/weather forecasting. GPM's societal benefits are related to hurricane monitoring, floods, landslides, extremes, disease, and more. [ABSTRACT FROM AUTHOR]

Despite significant progress made in snowfall estimation from space, methods utilizing passive microwave measurements continue to be plagued by low detectability compared to those that estimate rainfall. This article presents a hybrid snowfall detection algorithm that combines the output from a statistical algorithm utilizing satellite passive microwave measurements with the output from a statistical algorithm trained with in situ data that uses meteorological variables derived from a global forecast model as predictors. The satellite algorithm computes the probability of snowfall over land using logistic regression and the principal components of the high‐frequency brightness‐temperature measurements at AMSU/MHS and ATMS channel frequencies 89 GHz and above. In a separate investigation, analysis of modelled data derived from NOAA's Global Forecast System (GFS) showed that cloud thickness and relative humidity at 1 to 3 km height were the best predictors of snowfall occurrence. A statistical logistical regression model that combined cloud thickness, relative humidity and vertical velocity was selected among statistically significant variants as the one with the highest overall classification accuracy. Next, the weather‐based and satellite model outputs were combined in a weighting scheme to produce a final probability of snowfall output, which was then used to classify a weather event as "snowing" or "not snowing" based on an a priori threshold probability. Statistical analysis indicated that a scheme with equal weights applied to the weather‐based and satellite model significantly improved satellite snowfall detection. Example applications of the hybrid algorithm over continental USA demonstrated the improvement for a major snowfall event and for an event dominated by lighter snowfall. The article presents a hybrid approach to satellite snowfall detection that can improve the performance of satellite‐based methods and increase their utility in operational weather and hydrological forecasting. It presents an analysis and new insights into modelled meteorological variables that are related to snowfall occurrence, and a weather‐based snowfall detection algorithm using modelled data from a global weather forecast system. [ABSTRACT FROM AUTHOR]

Snow has a very high reflection property when compared with all other natural surfaces on Earth; thus, a small amount of contamination (30 ng/g) can dramatically reduce the reflectance of snow. To quantify the effect of black carbon (BC) concentrations on the directional reflectance factors of snow, we deposited BC concentrations onto snow surfaces at natural levels (98–4095 ng/g) and compared the measurement results with those from a theoretical model. It was found that increasing BC concentrations decreased the reflectance factor of snow and changed its distribution pattern. Moreover, our data provided valuable verification of the snow reflection model, which has previously been used to characterize the reflectance of snow. The model did not well match our measurements; for example, the model found fewer anisotropic results than those from observations. Subsequently, a specular kernel combined with two free parameters was proposed to counter the forward and backward scattering of the optical property of snow. The improved model successfully characterized the observed variability in the reflection measurements of snow with different BC concentration levels under field conditions, and its inverted parameter (M) had the potential to estimate BC concentrations. The improved model also had the ability to simulate the spectral reflectance factor of snow with low BC concentrations (i.e., smaller than 118 ng/g) over a wide range of viewing zenith angles. This paper provides an additional and effective method for studying the angular and spectral reflection properties of snow. [ABSTRACT FROM AUTHOR]

As a zero-balance line, lying between snow-covered and snow-free land areas, the snowline reflects the melting and retreating of snow and ice, which also mirrors the basic situation of climate and environment on mountain plateaus and in polar regions where there is a lack of ground meteorological stations. Thus, it is important to accurately extract and assess the distribution characteristics of the snow-line from hydrological, climatological, and geological aspects. This study extracted summer snowline data from the Xinjiang region in China using the snow cover duration ratio (SCDR) method with MODIS daily snow cover products (MOD10A1 and MYD10A1), IMS and Landsat data, and temperature and precipitation observations from 2001-2015. The variation and distribution characteristics of the summer snowline elevation were analyzed comparatively for the Xinjiang region and its different subareas. The results showed that snowline elevation data extracted using the SCDR method passed the accuracy test and therefore, it was considered suitable for the Xinjiang region. During 2001-2015, the average snowline elevation remained at about 3950 m within a 250-m range of fluctuation. However, linear trend analysis revealed an annual increasing trend of average snowline elevation with a tendency rate of 160.38 m/decade. The minimum snowline elevation exhibited a fluctuating upward trend, whereas the maximum snowline elevation showed a slight downward trend. Comparative analysis of snowline elevation in different areas of the Xinjiang region revealed a fluctuating upward trend in the Kunlun, Tianshan, and Altai Mountains. The Altai Mountains showed the most stable variation of snowline elevation, followed by the Kunlun Mountains and then the Tianshan Mountains. Spatially, the distribution of snowline elevation in the Xinjiang region was characterized by a decline from south to north with longitudinal zonation. [ABSTRACT FROM AUTHOR]

An automatic snow depth probe (magnaprobe) patented in 1999 (United States Patent 5,864,059, 1999) and produced commercially by Snow‐Hydro LLC has now been used to obtain more than a million simultaneous snow depths (up to 140 cm) and GPS measurements during a wide range of field validation campaigns. The magnaprobe consists of a ski pole‐like rod housing a magnetostrictive device along which a basket and magnet assembly slides. The rod is inserted to the base of the snow, the basket floats on the snow, and when a button is pushed, the distance between rod tip and basket is measured while a position is acquired. The nature of the substrate beneath the snow controls the snow depth accuracy with errors ranging from near zero for hard bases to +5 cm in soft vegetation. The Wide Area Augmentation System‐enabled GPS provides a position accurate to ±2.5 m. The probe increases the speed with which a depth and position measurement can be obtained by a factor of 10 compared to measuring with a traditional ruler probe and writing down the results. The highest boost in snow depth measurement efficiency occurs when the distance between measuring locations is kept relatively small (<10 m). Magnaprobes have materially improved our ability to evaluate airborne and satellite‐based snow depth products and are likely to see continued heavy use over the next decade as efforts to develop satellite systems for monitoring snow remotely are tested in various field settings. Plain Language Summary: We developed an automatic snow depth probe that can measure both depth and position simultaneously. These probes have been used extensively in snow field campaigns. They are accurate to better than 5 cm in measuring depth, with the poorer accuracy coming when soft ground under the snow is encountered; they are accurate to about ±5 m in measuring position. We expect probes like these will continue to be used in field validation campaigns for the foreseeable future. Key Points: Since 1999 a magnetostrictive device called a magnaprobe has been used to measure simultaneous snow depths and positions (GPS) during dozens of snow research field campaigns worldwide, with more than a million data points collected in this mannerDepths measured in this manner are accurate to better than 5 cm, with the ease penetration of the substrate below the snow determining the over‐probe error; position errors are ±2.5 mThe device, or something similar, is likely to see continued use until airborne and satellite products reach a stand‐alone level of accuracy and reliability [ABSTRACT FROM AUTHOR]

Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (TB) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer - Multi-Layer model, DMRT-ML), the vegetation transmissivity model (ϖ-гopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation, and the soil, respectively, at the top of the atmosphere. DA experiments were performed for 12 stations where daily continuous SWE measurements were acquired over 4 winters (2012-2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19, and 37 GHz in vertical polarizations. The overall SWE bias is reduced by 68% compared to the original SWE simulations, from 23.7 kgm-2 without assimilation to 7.5 kgm-2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.1% (19% without assimilation) for sites with a fraction of forest cover below 75 %, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50% of the PMW signal. [ABSTRACT FROM AUTHOR]

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k -nearest neighbors ( k -NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k -NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k -NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k -NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k -NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k -NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions. [ABSTRACT FROM AUTHOR]

Abstract: Accurate snow accumulation and melt simulations are crucial for understanding and predicting hydrological dynamics in mountainous settings. As snow models require temporally varying meteorological inputs, time resolution of these inputs is likely to play an important role on the model accuracy. Because meteorological data at a fine temporal resolution (~1 hr) are generally not available in many snow‐dominated settings, it is important to evaluate the role of meteorological inputs temporal resolution on the performance of process‐based snow models. The objective of this work is to assess the loss in model accuracy with temporal resolution of meteorological inputs, for a range of climatic conditions and topographic elevations. To this end, a process‐based snow model was run using 1‐, 3‐, and 6‐hourly inputs for wet, average, and dry years over Boise River Basin (6,963 km2), which spans rain dominated (≤1,400 m), rain–snow transition (>1,400 and ≤1,900 m), snow dominated below tree line (>1,900 and ≤2,400 m), and above tree line (>2,400 m) elevations. The results show that sensitivity of the model accuracy to the inputs time step generally decreases with increasing elevation from rain dominated to snow dominated above tree line. Using longer than hourly inputs causes substantial underestimation of snow cover area (SCA) and snow water equivalent (SWE) in rain‐dominated and rain–snow transition elevations, due to the precipitation phase mischaracterization. In snow‐dominated elevations, the melt rate is underestimated due to errors in estimation of net snow cover energy input. In addition, the errors in SCA and SWE estimates generally decrease toward years with low snow mass, that is, dry years. The results indicate significant increases in errors in estimates of SCA and SWE as the temporal resolution of meteorological inputs becomes coarser than an hour. However, use of 3‐hourly inputs can provide accurate estimates at snow‐dominated elevations. The study underscores the need to record meteorological variables at an hourly time step for accurate process‐based snow modelling. [ABSTRACT FROM AUTHOR]

Snow saltation is governed by the shear stress from wind on the snow surface, in form of the friction velocity. In most previous studies, the friction velocity was measured or estimated by wind velocity at a certain height using the log law, which is suitable for the fully developed wind field of an open area. However, the applicability of such method to roofs has not been tested. In this study, Large Eddy Simulation (LES) of wind flow around a flat roof was performed to study the flow field characteristics above a flat roof and the results were compared with those from the wind tunnel measurement. Owing to the flow separation and the reversed flow above the roof, the log law is observed to exist only on the leeward part of the roof; therefore, measurement based on the log law is not universally suitable to estimate the friction velocity on roofs. The comparison of the simulation results against experimental data shows that LES reasonably reproduces the flow field around the roof and can be an alternative approach to estimate the friction velocity on building roofs. The fluctuating friction velocity is found to be unneglectable compared with the mean friction velocity and should be considered in the estimation of the snow transport on roofs. [ABSTRACT FROM AUTHOR]

A multiresolution (MR) approach was successfully implemented in the context of a data assimilation (DA) framework to efficiently estimate snow water equivalent (SWE) over a large head water catchment in the Colorado River basin (CRB), while decreasing computational constraints by 60 %. A total of 31 years of fractional snow cover area (fSCA) images derived from Landsat TM, ETMC, and OLI sensor measurements were assimilated to generate two SWE reanalysis datasets, a baseline case at a uniform 90mspatial resolution and another using theMRapproach. A comparison of the two showed negligible differences in terms of snow accumulation, melt, and timing for the posterior estimates (in terms of both ensemble median and coefficient of variation). The MR approach underestimated the baseline peak SWE by less than 2% and underestimated day of peak and duration of the accumulation season by a day on average. The largest differences were, by construct, limited primarily to areas of low complexity, where shallow snowpacks tend to exist. The MR approach should allow for more computationally efficient implementations of snow data assimilation applications over large-scale mountain ranges, with accuracies similar to those that would be obtained using ~100m simulations. Such uniform resolution applications are generally infeasible due to the computationally expensive nature of ensemble-based DA frameworks. [ABSTRACT FROM AUTHOR]

This paper describes the methodology for deriving yearly pixel-wise snow melt-off day maps from optical data-based FSC (Fractional Snow Cover) without conducting any interpolation for cloud-obscured pixels or otherwise missing data. The Copernicus CryoLand Pan-European FSC time series for 2001–2016 re-gridded to 0.1° serves as input for the production of 16 years of melt-off day maps for Europe. These maps are compared with passive microwave radiometer (MWR) melt retrievals, to compare the performance of these two independent datasets, particularly concerning the effect of physiographic and snow conditions on the accuracy of the melt-off day estimates. Both these datasets are evaluated against melt-off day derived from in situ snow depth (SD) time series observed at European weather stations. We also present the relationship of these snow melt-off day products to a passive microwave radiometer-derived landscape freeze/thaw product. Our results show that the melt-off day derived from optical springtime FSC time series provides the strongest correlation with the snow melt-off day with respect to the in situ data. Overall the deviation of CryoLand FSC data derived melt-off day to that indicated by in situ observations is quite small, with positive bias of 0.9 days, and RMSE of 13.1 days. For 85% of the analyzed cases the differences are between ±10 days. Across Europe the MWR-based detection of melt-off day is less accurate; the investigated method performs the best for areas with sustained seasonal snow cover. Based on the time series for MWR-based melt-off day (1980–2016) and FT-ESDR (1980–2014), separately for boreal forests and tundra, we also found a clear trend towards earlier snow clearance: a decrease of melt-off day by as much as ~5 days per decade in boreal forest region was observed. [ABSTRACT FROM AUTHOR]

The snow data from the Soviet airborne expeditions Sever in the Arctic collected over several decades in March, April and May have been analyzed in this study. The Sever data included more measurements and covered a much wider area, particularly in the Eurasian marginal seas (Kara Sea, Laptev Sea, East Siberian Sea and Chukchi Sea), compared to the Soviet North Pole drifting stations. The latter collected data mainly in the central part of the Arctic Basin. The following snow parameters have been analyzed: average snow depth on the level ice (undisturbed snow) height and area of sastrugi, depth of snow dunes attached to ice ridges and depth of snow on hummocks. In the 1970s-1980s, in the central Arctic, the average depth of undisturbed snow was 21.2 cm, the depth of sastrugi (that occupied about 30% of the ice surface) was 36.2 cm and the average depth of snow near hummocks and ridges was about 65 cm. For the marginal seas, the average depth of undisturbed snow on the level ice varied from 9.8 cm in the Laptev Sea to 15.3 cm in the East Siberian Sea, which had a larger fraction of multiyear ice. In the marginal seas the spatial variability of snow depth was characterized by standard deviation varying between 66 and 100 %. The average height of sastrugi varied from 23 cm to about 32 cm with standard deviation between 50 and 56 %. The average area covered by sastrugi in the marginal seas was estimated to be 36.5% of the total ice area where sastrugi were observed. The main result of the study is a new snow depth climatology for the late winter using data from both the Sever expeditions and the North Pole drifting stations. The snow load on the ice observed by Sever expeditions has been described as a combination of the depth of undisturbed snow on the level ice and snow depth of sastrugi weighted in proportion to the sastrugi area. The height of snow accumulated near the ice ridges was not included in the calculations because there are no estimates of the area covered by those features from the Sever expeditions. The effect of not including that data can lead to some underestimation of the average snow depth. The new climatology refines the description of snow depth in the central Arctic compared to the results by Warren et al. (1999) and provides additional detailed data in the marginal seas. The snow depth climatology is based on 94% Sever data and 6% North Pole data. The new climatology shows lower snow depth in the central Arctic comparing to Warren climatology and more detailed data in the Eurasian seas. [ABSTRACT FROM AUTHOR]

The Soil and Water Assessment Tool (SWAT) model is generally applied in alpine catchments using a unique set of snow parameters for the entire basin, and calibration is based on discharge records only. This technical note presents three calibration procedures for snow parameters of SWAT considering snow water equivalent (SWE) values computed using a dense network of snow depth measurement stations available in the Upper Adige River basin, Italy. The first two procedures calibrate snow parameters according to the average sub-basin SWE: the first one defines a unique set of parameters for the entire basin, while the second allows for sub-basin variability. The last approach includes the elevation band SWE output in the calibration for each sub-basin and qualitatively compares it to the SWE computed from the available snow depth monitoring stations. This last method provides the best agreement between SWAT model results and SWE data. [ABSTRACT FROM AUTHOR]

We have developed an algorithm that retrieves the microphysical properties of falling snow from multi-frequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multi-frequency airborne radar observations from the OLYMPEX/RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar, and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and test the sensitivity of the algorithm to the prior assumptions. The results suggest that multi-frequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars, and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. [ABSTRACT FROM AUTHOR]

A novel microwave radiometric technique, wideband autocorrelation radiometry (WiBAR), is introduced. The radiometer offers a direct method to remotely measure the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces, such as a dry snowpack and a freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent; thus, this technique provides a direct measurement of depth. It is also a low-power sensing method, since there is no transmitter. We present a simple geophysical forward model for the multipath interference phenomenon and derive the system requirements needed to design a WiBAR instrument. An X-band instrument fabricated from commercial-off-the-shelf (COTS) components measured the thickness of the freshwater lake ice at the University of Michigan Biological Station. Ice thickness retrieval is demonstrated from nadir to 59°. The WiBAR was able to directly measure the lake icepack thickness of about 36 cm with an accuracy of 2 cm over this range of incidence angles. [ABSTRACT FROM AUTHOR]

Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure. [ABSTRACT FROM AUTHOR]

Automated in-situ snow measurements allow for continuous observations and extensive measurement networks into unpopulated areas. The collected information of essential snow parameters (e.g. snow water equivalent (SWE) and snow depth) is important for weather prediction, climate modelling and interpretation of remote sensing observations. The Arctic Space Centre of Finnish Meteorological Institute in Sodankylä has several field experiment sites, where automated and manual snow observations are performed e.g. for the purposes of operational weather services and development of remote sensing applications. The first automated snow measurements in Sodankylä were snow depth and snow temperature profile installed in 2000. The major snow observations were established in 2006, when tower-based optical radiance measurements and manual snow pit measurements started. Automated reference measurements including snow on the ground observations were installed following this. Tower-based microwave observations have been made since 2009 for remote sensing related purposes. The three measurement sites, the Intensive Observation Area, the bog site and the Integrated Carbon Observation System (ICOS) tower site, have hosted the tower-based observations, and most of the automated in-situ snow measurements. In addition, the WMO Solid Precipitation Intercomparison Experiment (SPICE) had a measurement site in Sodankylä in 2013-2015, involving several automatic snow on the ground instruments measuring snow depth and SWE. In this study, the automated snow depth, SWE and snow temperature profile measurements are compared with the manual observations to study the accuracy of the automated measurements. For the examined periods, the correlation of observations is strong with correlation coefficients between 0.93 and 0.96, p-values of <0.001, and average bias around 10 % for the snow depth (absolute bias of 7.9 cm) and snow temperature (absolute bias of 0.3 °C) and approximately 2 % for SWE (absolute bias of 1.0 mm). [ABSTRACT FROM AUTHOR]

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability; however, the high reflectance of snow is advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (> 50 %) of the TEMPO field of regard can be snow covered in January, and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy = 93 %, precision = 87 %, recall = 83 %, F = 85 %). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS observed radiances had high precision (90 % for Aqua and Terra), but underestimated the presence of snow (recall = 74 % for Aqua, 75 % for Terra). MAIAC generally outperforms the standard MODIS products (precision = 51 %, recall = 43 % for Aqua; precision = 69 %, recall = 45 % for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83 %) but missed a significant number of snow covered pixels (recall = 45 %). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy = 91 %, precision = 79 %, recall = 82 %, F = 81 %). We use the F score, which balances precision and recall, to determine overall product performance (F = 85 %, 82(82) %, 81 %, 58 %, 46(54) % for IMS, MAIAC Aqua(Terra), CMC, NISE, MODIS Aqua(Terra) respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. [ABSTRACT FROM AUTHOR]

Radar-based snowfall intensity retrieval is investigated at centimeter and millimeter wavelengths using high-quality collocated ground-based multi-frequency radar and video-disdrometer observations. Using data from four snowfall events, recorded during the Biogenic Aerosols Effects on Clouds and Climate (BAECC) campaign in Finland, measurements of liquid-water-equivalent snowfall rate S are correlated to radar equivalent reflectivity factors Ze, measured by the Atmospheric Radiation Measurement (ARM) cloud radars operating at X, Ka and W frequency bands. From these coupled observations power-law Ze-S relationships are derived for all considered frequencies and distinguishing fluffy from rimed snowfall. Interestingly fluffy-snow events show a spectrally distinct signature of Ze-S with respect to rimed-snow cases. In order to understand the connection between snowflake microphysical and multi-frequency backscattering properties, numerical simulations are also performed by using the particle size distribution provided by the in-situ video-disdrometer. The latter are carried out by using both the T-matrix method (TMM) for soft-spheroids with different aspect ratios and exploiting a pre-computed discrete dipole approximation (DDA) database for complex-shape snowflakes. Based on the presented results, it is concluded that the soft-spheroid approximation can be adopted to explain the observed multi-frequency Ze-S relations if a proper spheroid aspect ratio is selected. The latter may depend on the snowfall type. A further analysis of the backscattering simulations reveals that TMM cross-sections are higher than the DDA ones for small ice particles, but lower for larger particles. These differences may explain why the soft-spheroid approximation is satisfactory for radar reflectivity simulations, the errors of computed cross-sections for larger and smaller particles compensating each other. [ABSTRACT FROM AUTHOR]

This study presents a new instrument called a low-cost albedometer (LCA) composed of two illuminance sensors that are used to measure in-situ incident and reflected illuminance values on a daily timescale. The ratio between reflected vs. incident illuminances is called the albedo index and can be compared with actual albedo values. Due to the shape of the sensor, the direct radiation for zenith angles ranging from 55° to 90° is not measured. The spectral response of the LCA varies with the solar irradiance wavelengths within the range 0.26 to 1.195μm, and the LCA detects 85% of the total spectral solar irradiance for clear sky conditions. We first consider the theoretical results obtained for 10 different ice and snow surfaces with clear sky and cloudy sky incident solar irradiance that show that the LCA spectral response may be responsible for an overestimation of the theoretical albedo values by roughly 9% at most. Then, the LCA values are compared with two classical albedometers over a one-year measurement period (2013) for two sites in a tropical mountainous catchment in Bolivia. One site is located on the Zongo Glacier (i.e. snow and ice surfaces) and the second one is found on the right-hand side lateral moraine (bare soil and snow surfaces). The results, at daily time steps (256 days), given by the LCA are in good agreement with the classic albedo measurements taken with pyranometers with R² = 0.83 (RMSD = 0.10) and R² = 0.92 (RMSD = 0.08) for the Zongo Glacier and the right-hand side lateral moraine, respectively. This demonstrates that our system performs well and thus provides relevant opportunities to document spatio-temporal changes in the surface albedo from direct observations at the scale of an entire catchment at a low cost. Finally, during the period from September 2015 to June 2016, direct observations were collected with 15 LCAs on the Zongo Glacier and successfully compared with LANDSAT images showing the surface state of the glacier (i.e. snow or ice). This comparison illustrates the efficiency of this system to monitor the daily time step changes in the snow/ice coverage distributed on the glacier. [ABSTRACT FROM AUTHOR]

Summer storage of snow for tourism has seen an increasing interest in the last years. Covering large snow piles with materials such as sawdust enables more than two-thirds of the initial snow volume to be conserved. We present detailed mass balance measurements of two sawdust-covered snow piles obtained by terrestrial laser scanning during summer 2015. Results indicate that 74 and 63% of the snow volume remained over the summer for piles in Davos, Switzerland and Martell, Italy. If snow mass is considered instead of volume, the values increase to 83 and 72 %. The difference is attributed to settling and densification of the snow. Additionally, we adapted the one-dimensional, physically based snow cover model SNOWPACK to perform simulations of the sawdust-covered snow piles. Model results and measurements agreed extremely well at the point scale. Moreover, we analysed the contribution of the different terms of the surface energy balance to snow ablation for a pile covered with a 40 cm thick sawdust layer and a pile without insulation. Short-wave radiation was the dominant source of energy for both scenarios, but the moist sawdust caused strong cooling by long-wave emission and negative sensible and latent heat fluxes. This cooling effect reduces the energy available for melt by up to a factor of 12. As a result only 9% of the net short-wave energy remained available for melt. Finally, sensitivity studies of the parameters "thickness of the sawdust layer", "air temperature", "precipitation" and "wind speed" were performed. We show that sawdust thickness has a tremendous effect on snow loss. Higher air temperatures and wind speeds increase snow ablation but less significantly. No significant effect of additional precipitation could be found as the sawdust remained wet during the entire summer with the measured quantity of rain. Setting precipitation amounts to zero, however, strongly increased melt. Overall, the 40 cm sawdust provides sufficient protection for mid-elevation (approx. 1500ma.s.l.) Alpine climates and can be managed with reasonable effort. [ABSTRACT FROM AUTHOR]

Cold glaciers at the highest locations of the European Alps have been investigated by drilling ice cores to retrieve their stratigraphic climate records. Findings like the Oetztal ice man have demonstrated that small ice bodies at summit locations of comparatively lower altitudes may also contain old ice if locally frozen to the underlying bedrock. In this case, constraining the maximum age of their lower-most ice part may help to identify past periods with minimum ice extent in the Alps. However, with recent warming and consequent glacier mass loss, these sites may not preserve their unique climate information for much longer. Here we utilized an existing ice cave at Chli Titlis (3030 m), central Switzerland, to perform a case study for investigating the maximum age of cold-based summit glaciers in the Alps. The cave offers direct access to the glacier stratigraphy without the logistical effort required in ice core drilling. In addition, a pioneering exploration had already demonstrated stagnant cold ice conditions at Chli Titlis, albeit more than 25 years ago. Our englacial temperature measurements and the analysis of the isotopic and physical properties of ice blocks sampled at three locations within the ice cave show that cold ice still exists fairly unchanged today. State-of-the-art micro-radiocarbon analysis constrains the maximum age of the ice at Chli Titlis to about 5000 years before present. By this means, the approach presented here will contribute to a future systematic investigation of cold-based summit glaciers, also in the Eastern Alps. [ABSTRACT FROM AUTHOR]

In many mountainous regions around the world, snow and soil moisture are key components of the hydrologic cycle. Preferential flow paths of snowmelt water through snow have been known to occur for years with few studies observing the effect on soil moisture. In this study, statistical analysis of the topographical and hydrological controls on the spatiotemporal variability of snow water equivalent (SWE) and soil moisture during snowmelt was undertaken at a subalpine forested setting with north, south, and flat aspects as a seasonally persistent snowpack melts. We investigated if evidence of preferential flow paths in snow can be observed and the effect on soil moisture through measurements of snow water equivalent and near-surface soil moisture, observing how SWE and near-surface soil moisture vary on hillslopes relative to the toes of hillslopes and flat areas. We then compared snowmelt infiltration beyond the near-surface soil between flat and sloping terrain during the entire snowmelt season using soil moisture sensor profiles. This study was conducted during varying snowmelt seasons representing above-normal, relatively normal, and below-normal snow seasons in northern Colorado. Evidence is presented of preferential meltwater flow paths at the snow-soil interface on the north-facing slope causing increases in SWE downslope and less infiltration into the soil at 20 cm depth; less association is observed in the near-surface soil moisture (top 7 cm). We present a conceptualization of the meltwater flow paths that develop based on slope aspect and soil properties. The resulting flow paths are shown to divert at least 4% of snowmelt laterally, accumulating along the length of the slope, to increase the snow water equivalent by as much as 170% at the base of a north-facing hillslope. Results from this study show that snow acts as an extension of the vadose zone during spring snowmelt and future hydrologic investigations will benefit from studying the snow and soil together. [ABSTRACT FROM AUTHOR]

For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81% in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean. [ABSTRACT FROM AUTHOR]

Observations at the West Antarctic Ice Sheet (WAIS) Divide site show that near-surface snow is strongly altered by weather-related processes such as strong winds and temperature fluctuations, producing features that are recognizable in the deep ice core. Prominent "glazed" surface crusts develop frequently at the site during summer seasons. Surface, snow pit, and ice core observations made in this study during summer field seasons from 2008-2009 to 2012-2013, supplemented by automated weather station (AWS) data with short- and longwave radiation sensors, revealed that such crusts formed during relatively low-wind, low-humidity, clear-sky periods with intense daytime sunshine. After formation, such glazed surfaces typically developed cracks in a polygonal pattern likely from thermal contraction at night. Cracking was commonest when several clear days occurred in succession and was generally followed by surface hoar growth; vapor escaping through the cracks during sunny days may have contributed to the high humidity that favored nighttime formation of surface hoar. Temperature and radiation observations show that daytime solar heating often warmed the near-surface snow above the air temperature, contributing to upward mass transfer, favoring crust formation from below, and then surface hoar formation. A simple surface energy calculation supports this observation. Subsequent examination of the WDC06A deep ice core revealed that crusts are preserved through the bubbly ice, and some occur in snow accumulated during winters, although not as commonly as in summertime deposits. Although no one has been on site to observe crust formation during winter, it may be favored by greater wintertime wind packing from stronger peak winds, high temperatures and steep temperature gradients from rapid midwinter warmings reaching as high as -15 °C, and perhaps longer intervals of surface stability. Time variations in crust occurrence in the core may provide paleoclimatic information, although additional studies are required. Discontinuity and cracking of crusts likely explain why crusts do not produce significant anomalies in other paleoclimatic records. [ABSTRACT FROM AUTHOR]

Two hundred marine-terminating Greenland outlet glaciers deliver more than half of the annually accumulated ice into the ocean and have played an important role in the Greenland ice sheet mass loss observed since the mid-1990s. Submarine melt may play a crucial role in the mass balance and position of the grounding line of these outlet glaciers. As the ocean warms, it is expected that submarine melt will increase, potentially driving outlet glaciers retreat and contributing to sea level rise. Projections of the future contribution of outlet glaciers to sea level rise are hampered by the necessity to use models with extremely high resolution of the order of a few hundred meters. That requirement in not only demanded when modeling outlet glaciers as a stand alone model but also when coupling them with high-resolution 3-D ocean models. In addition, fjord bathymetry data are mostly missing or inaccurate (errors of several hundreds of meters), which questions the benefit of using computationally expensive 3-D models for future predictions. Here we propose an alternative approach built on the use of a computationally efficient simple model of submarine melt based on turbulent plume theory. We show that such a simple model is in reasonable agreement with several available modeling studies. We performed a suite of experiments to analyze sensitivity of these simple models to model parameters and climate characteristics. We found that the computationally cheap plume model demonstrates qualitatively similar behavior as 3-D general circulation models. To match results of the 3-D models in a quantitative manner, a scaling factor of the order of 1 is needed for the plume models. We applied this approach to model submarine melt for six representative Greenland glaciers and found that the application of a line plume can produce submarine melt compatible with observational data. Our results show that the line plume model is more appropriate than the cone plume model for simulating the average submarine melting of real glaciers in Greenland. [ABSTRACT FROM AUTHOR]

With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be log-normally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses. [ABSTRACT FROM AUTHOR]

Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966-2012) ground-based measurements from 1814 stations. Spatially, long-term (1971-2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade-1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50° N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales. [ABSTRACT FROM AUTHOR]

Twenty-first century snowfall changes over the European Alps are assessed based on high-resolution regional climate model (RCM) data made available through the EURO-CORDEX initiative. Fourteen different combinations of global and regional climate models with a target resolution of 12 km and two different emission scenarios are considered. As raw snowfall amounts are not provided by all RCMs, a newly developed method to separate snowfall from total precipitation based on near-surface temperature conditions and accounting for subgrid-scale topographic variability is employed. The evaluation of the simulated snowfall amounts against an observation-based reference indicates the ability of RCMs to capture the main characteristics of the snowfall seasonal cycle and its elevation dependency but also reveals considerable positive biases especially at high elevations. These biases can partly be removed by the application of a dedicated RCM bias adjustment that separately considers temperature and precipitation biases. Snowfall projections reveal a robust signal of decreasing snowfall amounts over most parts of the Alps for both emission scenarios. Domain and multi-model mean decreases in mean September-May snowfall by the end of the century amount to -25 and -45% for representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, respectively. Snowfall in low-lying areas in the Alpine forelands could be reduced by more than -80 %. These decreases are driven by the projected warming and are strongly connected to an important decrease in snowfall frequency and snowfall fraction and are also apparent for heavy snowfall events. In contrast, high-elevation regions could experience slight snowfall increases in midwinter for both emission scenarios despite the general decrease in the snowfall fraction. These increases in mean and heavy snowfall can be explained by a general increase in winter precipitation and by the fact that, with increasing temperatures, climatologically cold areas are shifted into a temperature interval which favours higher snowfall intensities. In general, percentage changes in snowfall indices are robust with respect to the RCM postprocessing strategy employed: similar results are obtained for raw, separated, and separated-bias-adjusted snowfall amounts. Absolute changes, however, can differ among these three methods. [ABSTRACT FROM AUTHOR]

The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional "normality" tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as "early-spring snow". [ABSTRACT FROM AUTHOR]

The article describes conditions during the snow season in the U.S. from July 2016 to June 2017. The first traces of snow in Alaska were observed at Barrow and Glenallen in July 2016. Record amounts of snow fell at several locations in January 2017, including Alamosa, Colorado and Pocatello, Idaho. The Northeast experienced blizzard conditions with thunder snow in February 2017.

The partitioning of precipitation into frozen and liquid components influences snow-derived water resources and flood hazards in mountain environments. We used a 915-MHz Doppler radar wind profiler upstream of the northern Sierra Nevada to estimate the hourly elevation where snow melts to rain, or the snow level, during winter (December-February) precipitation events spanning water years (WY) 2008-2017. During this ten-year period, a Mann-Kendall test indicated a significant (p < 0.001) positive trend in snow level with a Thiel-Sen slope of 72 m year-1. We estimated total precipitation falling as snow (snow fraction) between WY1951 and 2017 using nine daily mid-elevation (1200-2000 m) climate stations and two hourly stations spanning WY2008-2017. The climate-station-based snow fraction estimates agreed well with snow-level radar values (R² = 0.95, p < 0.01), indicating that snow fractions represent a reasonable method to estimate changes in frozen precipitation. Snow fraction significantly (p < 0.001) declined during WY2008-2017 at a rate of 0.035 (3.5%) year-1. Single-point correlations between detrended snow fraction and sea-surface temperatures (SST) suggested that positive SST anomalies along the California coast favor liquid phase precipitation during winter. Reanalysis-derived integrated moisture transported upstream of the northern Sierra Nevada was negatively correlated with snow fraction (R² = 0.90, p < 0.01), with atmospheric rivers representing the likely circulation mechanism producing low-snow-fraction storms. [ABSTRACT FROM AUTHOR]

We present a novel method for the direct determination of the snowmelt coefficient of widely used degree-day models, using only cumulated temperature and precipitation over the days of snow cover. We develop a proof of concept using (1) local measurements of precipitation, temperature and snow water equivalent (SWE) at a set of well-monitored sites in the US, and (2) available time series of snow cover from satellite and gridded daily precipitation and daily average temperature for the study region of South Tyrol, in the Italian Alps. We demonstrate how the method can reproduce the snow water balance to an acceptable extent, critically depending on the accuracy of input precipitation and temperature, highlighting the importance of a reliable representation of weather forcing if the estimate has to be robust and representative. Although not always accurate at a point, our approach yields a SWE reasonably consistent with observations, and snowmelt flows compatible with measured streamflow. At the same time, the model allows an interpretation of discrepancies between observations and simulations to detect inconsistencies between snow cover and weather forcing. This method is in principle applicable for large-scale hydrological assessments thanks to the increasing global coverage of snow cover, precipitation and temperature data. As the only other type of observation available to calibrate models is often streamflow, the direct calibration of the snow component of a model using snow cover and weather forcing reduces the number of model processes and parameters to be calibrated with streamflow, and is expected to increase model robustness. [ABSTRACT FROM AUTHOR]

This study investigates the synoptic/mesoscale dynamics responsible for an unusually heavy southern US snowstorm that occurred on February 11-12, 2010, using reanalysis, observations, and numerical simulations. This record breaking snowfall event represents an example of multiple upper level and low-level jets (LLJs) and their accompanying baroclinic zones. The analysis reveals the following synoptic scale processes as significant contributors: (1) upper level jet splitting and merging, (2) advection of cold arctic air at low levels by a large anticyclone, and (3) an incoming upper level shortwave trough. In addition to the synoptic scale processes, the following mesoscale features played a major role in this snowstorm event: coexisting potential (convective) instability and conditional symmetric instability, terrain blocking, and a double LLJ development process. Sensitivity experiments including (1) limiting the orographic effects of elevated plateau in Texas and the Sierra Madre Mountains in Mexico by reducing the terrain height to 225 meters, (2) the microphysics/latent heating effects, and (3) surface fluxes on the development and intensity of the snowstorm were also conducted by turning these options off in the numerical model. Of all three experiments, the surface flux experiment displays the least amount of influence on the developing frozen precipitation bands. [ABSTRACT FROM AUTHOR]

This paper addresses the problem related to the interpretation of the results of actual snowfall measurements performed with two different configurations of the compensation methods for changeable climatic conditions. The paper also presents the operating principle of an ultrasound sensor for monitoring snow layer (on the air-side). In particular, the paper includes a graphic representation of the results of measurements performed in measurement stations located in Poland and at Spitsbergen. The two compensation methods (temperature and parametric) were tested for their effectiveness in different, changeable climatic conditions. The measurement results confirm that the applied compensation methods are useful in minimizing the measurement error for snow layer thickness measured on the air-side. [ABSTRACT FROM AUTHOR]

Seasonal snow plays an important part in Earth's climate system. Snow cover regulates the land surface energy balance through altering the albedo of the land surface. To utilize the satellite-retrieved snow cover fraction (SCF) and snow depth (SD) data sufficiently and avoid inconsistency, this paper developed a very simple but robust quality control method to assimilate Fengyun satellite-retrieved SCF and SD simultaneously. The results show that the assimilation method which this paper implemented can not only utilize the satellite-retrieved SCF and SD data sufficiently but also avoid the inconsistency of them. Two experiments were designed and performed to quantify the impacts of snow on land surface energy balance using the integrated urban land model. With the increase of the SCF and SD, the net radiation decreased significantly during the day and increased a little at night; the sensible heat flux decreased significantly during the day; the evapotranspiration and ground heat flux decreased during the day too. [ABSTRACT FROM AUTHOR]

Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971-2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015-2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature. [ABSTRACT FROM AUTHOR]

The article presents comprehensive data from the U.S. Geological Survey database about flood events that were recorded during a 12-year period from 2002 to 2013. Details related to the use of the Characteristics points method to estimate water flow records, along with the computational steps that were followed are included.

The conventional climate datasets based on observations only are a widely used source of information for climate and hydrology. On the Norwegian mainland, the seNorge datasets of daily mean temperature and total precipitation amount constitute a valuable meteorological input for snow- and hydrological simulations which are routinely conducted over such a complex and heterogeneous terrain. A new seNorge version (seNorge2) has been released recently and to support operational applications for civil protection purposes, it must be updated daily and presented on a high-resolution grid (1 km of grid spacing). The archive goes back to 1957. The seNorge2 statistical interpolation schemes can provide high-resolution fields for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The statistical schemes build upon classical spatial interpolation methods, such as Optimal Interpolation and successive-correction schemes, and introduce original approaches. For both temperature and precipitation, the spatial interpolation exploits the concept of (spatial) scale-separation and the first-guess field is derived from the observed data. Furthermore, the geographical coordinates and the elevation are used as complementary information. The evaluation of the seNorge2 products is presented both from a general point of view, through systematic cross-validations, and specifically as the meteorological input in the operational model chains used for snow- and hydrological simulations. The seNorge snow model is used for simulation of snow fields and the DDD (Distance Distribution Dynamics) rainfall-runoff model is the hydrological model used. The evaluation points out important information for the future seNorge2 developments: the daily mean temperature fields constitute an accurate and precise dataset, on average the predicted temperature is an unbiased estimate of the actual temperature and its precision (at grid points) varies between 0.8 °C and 2.4 °C; the daily precipitation fields provide a reasonable estimate of the actual precipitation, the cross-validation shows that on average the precision of the estimates (at grid points) is about &pm;20 %, though a systematic underestimation of precipitation occurs in data-sparse areas and for intense precipitation. Both the seNorge snow and the DDD models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The dataset described in this article is available for public download at http://doi.org/10.5281/zenodo.845733. [ABSTRACT FROM AUTHOR]

Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on "rock debris" type and the shortest on "lichen-herb-heath tundra", resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials. [ABSTRACT FROM AUTHOR]

Abstract. We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November–June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014–2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011–2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m−3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200–2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean. [ABSTRACT FROM AUTHOR]

The article presents information regarding the weather and the environmental changes of Nebraska. The article also reports the states snowfall, wind flow, and temperature during different seasons. Particular focus is presented to various natural calamities including Blizzard of 1888, tornado in Omaha, Nebraska on March 23, 1913, and Hallam tornado of June 25, 2004.