Blowing Snow Events Research Articles

Representation of Blowing Snow and Associated Visibility Reduction in an Operational High-Resolution Weather Model

Abstract Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the Rapid Update Cycle land surface model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. To improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions. Significance Statement Blowing snow presents a significant hazard to motorists and airport operations through sometimes very rapid and intense reductions in visibility, yet little predictive guidance exists for blowing snow. This study aims to improve the prediction of blowing snow occurrence and associated surface visibility reduction using diagnostics from an operational high-resolution weather model. One key challenge regards the question of driftability of the snowpack. This study evaluates two approaches to quantify driftability in terms of visibility reduction due to blowing snow and acknowledges that more measurements are needed to improve the representation of blowing snow physics in NWP models.

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

Ion geochemistry of a coastal ice wedge in northwestern Canada: Contributions from marine aerosols and implications for ice‐wedge paleoclimate interpretations

AbstractIce wedges are a characteristic ground ice feature in permafrost regions that form primarily from the meltwater of the seasonal snowpack. Ice‐wedge oxygen and hydrogen stable isotopes have been used in winter paleotemperature reconstructions; however, until recently, the ion geochemistry of ice wedges has rarely been analyzed as a potential paleoclimate proxy. This potential is greatest for ice wedges located in coastal regions, where marine aerosols are the dominant contributor to snowpack impurities. Here, we evaluate the source and integrity of ionic concentrations of a coastal ice wedge in the northwestern Canadian Arctic (Beaufort Sea coast) to evaluate the use of ice wedges as a marine aerosol archive. Comparison to a regionally comparable snowpack reveals remarkably similar ionic concentrations for Cl−, Na+, Br−, SO42−, Ca2+, and Mg2+, with a Cl−/Na+ ratio similar to bulk seawater (1.80 vs. 1.79 in seawater), suggesting that marine aerosols, probably from sea salt aerosol production during blowing snow events over sea ice as indicated by depleted SO42− values relative to Na+, are probably the dominant contributor to ion concentrations. A previously established linear age model for the ice wedge is used to develop a continuous ion record spanning ~4,600 to ~700 yr b2k. Cl− and Na+ concentrations reveal a strong and continuous increase in concentrations over the late Holocene, thought to be driven by reduced distance‐to‐coast of up to 1 km as a result of coastal erosion. This study presents a novel interpretation of ice‐wedge geochemical data and represents the first Holocene ice‐wedge ion record.

Comparison of varied complexity parameterizations in estimating blowing snow occurrences

Blowing snow has a major impact on the spatial–temporal evolution of snow cover. An accurate prediction of the initiation of snow particle movement is one of the prerequisites for blowing snow modeling. Previous studies have proposed varied complexity parameterizations to estimate the occurrence of blowing snow. However, a quantitative evaluation of the performance of different schemes in blowing snow identification remains lacking, particularly outside the regions where empirical schemes were proposed. This study details a comparative study of the performance of five distinct parameterizations with varying degrees of complexity in estimating the occurrence of blowing snow over the central French Alps. Our results show that less than half of blowing snow events can be accurately detected at most stations by traditional schemes, for example, the constant and temperature-based empirical threshold wind speed schemes, probability estimation, and physically based methods. In addition, the spatial variability of wind speed can result in considerable spatial variability in the occurrence of blowing snow, which cannot be captured by traditional methods. Decision tree-based models can detect blowing snow occurrences with higher accuracy, and the spatial variation in snow transport can be captured by including information on blowing snow occurrences at low wind speed stations. However, regardless of the training strategies employed, the decision tree-based model employed here estimated far too many false alarms. This study contributes to blowing snow modeling by presenting the current state-of-the-art and limitations of various schemes for detecting blowing snow occurrences, and highlights the importance of parameterization modification, key parameter calibration, and optimization.

Analysis of Multiyear Blowing Snow Occurrences in the French Alps

Abstract Wind-driven snow transport has important implications for spatial–temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the French Alps. Due to the paucity of near-surface observations, our knowledge on the spatiotemporal variability of blowing snow occurrences is rather limited. Based on multiyear in situ observations, the spatial–temporal variability in the occurrence of blowing snow events in the French Alps was presented to investigate potential links with ambient meteorological conditions. Statistical analysis of the observations demonstrates that blowing snow events are frequently observed with substantial spatiotemporal variability. Most stations experienced snow transport one out of every five days throughout winter, and the corresponding cumulative hours with blowing snow occurrence accounted for 8% of the month in winter. Blowing snow events generally last 4–8 h in winter and early spring. The likelihood of blowing snow occurrences increases with wind speed but with divergent patterns across snow types. The frequency of blowing snow occurrences with concurrent snowfall is substantially higher than that without concurrent snowfall, although high spatiotemporal variability was found. The considerable variation in snow transport frequency can be explained by contrasting meteorological conditions, local climate, snowpack properties, and topography (elevation and aspect). The temperature-based empirical scheme failed to recognize individual occurrence of blowing snow events because of the significantly overestimated threshold wind speeds, highlighting the importance of validation using in situ observations. Our results contribute to the understanding of spatiotemporal occurrence of blowing snow events and facilitate the development of blowing snow models.

Impact of Clouds and Blowing Snow on Surface and Atmospheric Boundary Layer Properties Over Dome C, Antarctica.

Clouds and blowing snow (BLSN) occur frequently over Antarctica, where it is critical to understand their feedbacks to surface and atmospheric boundary layer processes. Dome C, an elevated East Antarctic station, dominated by lengthy periods of surface longwave (LW) radiative cooling, is selected to reveal cloud and BLSN impacts within a largely stable environment. The sky condition is classified as clear, cloudy, or BLSN, using 3years of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite data. Co-located and contemporaneous in situ observations are used to investigate the relationship of sky condition with surface and atmospheric boundary layer thermal structure, focusing on seasonal variability. Results show that increased downwelling LW radiation from clouds abate surface radiative cooling losses, contributing to warming during all seasons. An increase of 3°C in the mean surface air temperature is observed during spring, whereas, a more dramatic rise (around 10°C), due to accompanying large-scale subsidence, is observed during fall and winter in association with clouds. For all seasons, the wind speed and wind speed shear are strongest during BLSN events, and the surface-based inversion is weakened by cooling which peaks in a shallow above-surface turbulent layer. The stronger background stability during fall and winter seasons, restricts turbulence and BLSN depths generally to the lowest tens of meters. The Earth's cryosphere is among the most rapidly evolving yet least well-observed regions, and knowledge of clouds and BLSN interactions with the typical stable atmospheric boundary layer can help further understand energy and moisture exchanges.

Idealized Study of a Static Electrical Field on Charged Saltating Snow Particles

Strong electric fields, with values of tens kV/m near the surface, are observed during drifting and blowing snow events. Charge separation can significantly affect particle motion. Although several investigations attempted to shed light on the mechanisms of charge separation and the resulting electric field structure, few studies paid attention to the effect of electrification on the particle trajectory, which may influence the transport mechanism. In this work, we studied trajectories of individual, charged particles in an idealized static electrical field by solving the equations of motion in a neutral atmospheric boundary layer. The results show that negatively charged particles have a lower saltation height while positively charged particles jump higher as long as friction velocities are small. This effect reverses for higher friction velocities as rebound velocities start to dominate over vertical acceleration. We find regimes, in which charge separation leads to suspension of particles close to the ground. The threshold condition for this saltation-suspension transition is related to the rebound velocity and charge-to-mass ratio of the charged particle. Our study is a first step towards a better understanding on the influence of charge separation on drifting snow and should lead to include this effect in state of the art saltation models.

Study of an Arctic blowing snow-induced bromine explosion event in Ny-Ålesund, Svalbard

Bromine explosion events (BEEs) are important processes that influence the atmospheric oxidation capacity, especially in the polar troposphere during spring. Although sea ice surface is thought to be a significant bromine source, bromine release mechanisms remain unclear. High-resolution ground-based observations of reactive bromine, such as BrO, are important for assessing the potential impacts on tropospheric ozone and evaluating chemical models. However, previous model studies paid little attention to Svalbard, which is surrounded by both open ocean and sea ice. In this paper, we present continuous BrO slant column densities and vertical column densities derived by Multi-Axis Differential Optical Absorption Spectroscopy deployed at Ny-Ålesund (78.92°N, 11.93°E) in March 2017. We focused on one BEE in mid-March, during which the vertical column densities of BrO surged from 4.26 × 1013 molecular cm−2 to the peak at 1.23 × 1014 molecular cm−2 on March 17, surface ozone depleted from a background level of 46.25 parts per billion by volume (ppbv) to 13.9 ppbv. This case study indicates that the BEE was strongly associated with blowing snow induced by the cyclone systems that approached Svalbard from March 14 to 18. By considering meteorological conditions, sea ice coverage, and airmass trajectory history, we demonstrate that sea salt aerosols (SSAs) from blowing snow on sea ice, rather than from open ocean, are attributed to the occurrence of this BEE. Model results from a parallelized-tropospheric offline model of chemistry and transport (p-TOMCAT) indicate that this BEE was mainly triggered by a blowing snow event associated with a low-pressure cyclone system. The concentration of blowing-snow-sourced SSAs surged to peak when the airmass pass across the sea-ice-covered area under high wind speed, which is a critical factor in the process of bromine explosion observed in Ny-Ålesund. Due to the coarse resolution, the possible delayed timing of bromine release from SSA and the model-data discrepancies still exist.

Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems

Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are ‘true’. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events.

Decision tree-based detection of blowing snow events in the European Alps

Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and constant threshold wind speeds have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity) and snow measurements (including in situ snow depth observations and satellite-derived products). Twenty repetitions of a random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subsets were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if the divergent distribution of wind speed exists between stations. Although both the site-specific DTMs and site-independent DTM show great ability in detecting blowing snow occurrence and are superior to commonly used empirical parameterizations, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated to be a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Applying a Physically Based Blowing Snow Diagnostic Parameterization to Improve Wintertime Visibility Forecasts in the WRF Model

AbstractBlowing snow presents substantial risk to human activities by causing severe visibility degradation and snow drifting. Furthermore, blowing snow presents a weather forecast challenge since it is not generally simulated in operational weather forecast models. In this study, we apply a physically based blowing snow model as a diagnostic overlay to output from a reforecast WRF simulation of a significant blowing snow event that occurred over the northern Great Plains of the United States during the winter of 2019. The blowing snow model is coupled to an optics parameterization that estimates the visibility reduction by blowing snow. This overlay is qualitatively evaluated against false color satellite imagery from the GOES-16 operational weather satellite and available surface visibility observations. The WRF-simulated visibility is substantially improved when incorporating blowing snow hydrometeors. Furthermore, the model-simulated plume of blowing snow roughly corresponds to the blowing snow plumes visible in the satellite imagery. Overall, this study illustrates how a blowing snow diagnostic model can aid weather forecasters in making blowing snow visibility forecasts, and demonstrates how the model can be evaluated against satellite imagery.

Warm-air entrainment and advection during alpine blowing snow events

Abstract. Blowing snow transport has considerable impact on the hydrological cycle in alpine regions both through the redistribution of the seasonal snowpack and through sublimation back into the atmosphere. Alpine energy and mass balances are typically modeled with time-averaged approximations of sensible and latent heat fluxes. This oversimplifies nonstationary turbulent mixing in complex terrain and may overlook important exchange processes for hydrometeorological prediction. To determine if specific turbulent motions are responsible for warm- and dry-air advection during blowing snow events, quadrant analysis and variable interval time averaging was used to investigate turbulent time series from the Fortress Mountain Snow Laboratory alpine study site in the Canadian Rockies, Alberta, Canada, during the winter of 2015–2016. By analyzing wind velocity and sonic temperature time series with concurrent blowing snow, such turbulent motions were found to supply substantial sensible heat to near-surface wind flows. These motions were responsible for temperature fluctuations of up to 1 ∘C, a considerable change for energy balance estimation. A simple scaling relationship was derived that related the frequency of dominant downdraft and updraft events to their duration and local variance. This allows for the first parameterization of entrained or advected energy for time-averaged representations of blowing snow sublimation and suggests that advection can strongly reduce thermodynamic feedbacks between blowing snow sublimation and the near-surface atmosphere. The downdraft and updraft scaling relationship described herein provides a significant step towards a more physically based blowing snow sublimation model with more realistic mixing of atmospheric heat. Additionally, calculations of return frequencies and event durations provide a field-measurement context for recent findings of nonstationarity impacts on sublimation rates.

Importance of Blowing Snow During Cloudy Conditions in East Antarctica: Comparison of Ground-Based and Space-Borne Retrievals Over Ice-Shelf and Mountain Regions

Continuous measurements of blowing snow are scarce, both in time and space. Satellites now provide the opportunity to derive blowing snow occurrences, transport and sublimation rates over Antarctica. These products are extremely valuable and offer a continental-wide assessment of blowing snow, which is an important but unknown component of the surface mass balance of the Antarctic ice sheet. However, little ground truth is available to validate these retrievals. The recent application of ceilometers for detection of blowing snow frequencies provides an opportunity to validate the satellite retrievals of blowing snow frequencies at the Princess Elisabeth and Neumayer stations, East Antarctica for the 2011-2016 time period. A routine to detect blowing snow occurrence from remote sensing ceilometers has been developed at those locations. Thanks to their ground-based location, ceilometers are able to detect blowing snow events in the presence of clouds and precipitation, which can be missed by the satellite, since optically thick clouds impede the penetration of the signal. This is important, since the proportion of events missed by the CALIPSO and ICESat-2 satellites due to the presence of cloud decks is currently unknown. Over coastal areas, up to 90% of blowing snow happens under cloudy conditions and represent 30%of all cloudy conditions at both Neumayer and Princess Elisabeth stations. Although both detection methods have their limitations, 10% (4%) of the measurements at Princess Elisabeth (and Neumayer) are identified as blowing snow by the satellite but not by the ceilometer, likely due to differences in sensors, limitation of the surface identification by the satellite, or the spatial inhomogeneity of the blowing snow event. While the satellite blowing snow retrieval is a useful product, further investigation is needed to reduce the uncertainties on blowing snow frequencies associated with clouds.

Toward predicting expressway closures due to blowing snow events

This study aimed to improve the estimation of blowing snow events for predicting expressway closures. The Japan Meteorological Agency mesoscale model analysis with a grid resolution of 5 km was dynamically downscaled to 1 km using the SCALE meteorological model in the domain including Hokkaido, Japan, in the period from 1 November 2017 to 28 February 2018. This study focused on the E5 expressway, run by East Nippon Expressway Co., Ltd., in Hokkaido, where closures frequently occur as a result of low visibility due to blowing snow in winter. The blowing snow potential was evaluated in terms of the minimum driver visibility estimated by an empirical relation among wind speed, air temperature, and snowfall intensity. Because the downscaling overestimated wind speed, a simple bias correction was applied. The downscaling produced small-scale snow cloud streaks that led to blowing snow, although snowfall spots were often mispositioned. In contrast, the mesoscale model analysis did not produce such small-scale phenomena, and thus the blowing snow potential matched closures only when the snowfall was caused by synoptic-scale disturbances.

Radar measurements of blowing snow off a mountain ridge

Abstract. Modelling and forecasting wind-driven redistribution of snow in mountainous regions with its implications on avalanche danger, mountain hydrology or flood hazard is still a challenging task often lacking in essential details. Measurements of drifting and blowing snow for improving process understanding and model validation are typically limited to point measurements at meteorological stations, providing no information on the spatial variability of horizontal mass fluxes or even the vertically integrated mass flux. We present a promising application of a compact and low-cost radar system for measuring and characterizing larger-scale (hundreds of metres) snow redistribution processes, specifically blowing snow off a mountain ridge. These measurements provide valuable information of blowing snow velocities, frequency of occurrence, travel distances and turbulence characteristics. Three blowing snow events are investigated, two in the absence of precipitation and one with concurrent precipitation. Blowing snow velocities measured with the radar are validated by comparison against wind velocities measured with a 3D ultra-sonic anemometer. A minimal blowing snow travel distance of 60–120 m is reached 10–20 % of the time during a snow storm, depending on the strength of the storm event. The relative frequency of transport distances decreases exponentially above the minimal travel distance, with a maximum measured distance of 280 m. In a first-order approximation, the travel distance increases linearly with the wind velocity, allowing for an estimate of a threshold wind velocity for snow particle entrainment and transport of 7.5–8.8 m s−1, most likely depending on the prevailing snow cover properties. Turbulence statistics did not allow a conclusion to be drawn on whether low-level, low-turbulence jets or highly turbulent gusts are more effective in transporting blowing snow over longer distances, but highly turbulent flows are more likely to bring particles to greater heights and thus influence cloud processes. Drone-based photogrammetry measurements of the spatial snow height distribution revealed that increased snow accumulation in the lee of the ridge is the result of the measured local blowing snow conditions.

GOES-16 Observations of Blowing Snow in Horizontal Convective Rolls on 24 February 2019

Abstract On 24 February 2019, strong winds behind an Arctic cold front led to widespread blowing snow across the northern Great Plains including areas in eastern North/South Dakota and western Minnesota. Impacts of the event ranged from blizzard conditions in northwest Minnesota to sporadic, minor reductions in visibility across the region. This study documents the event using remotely sensed observations from platforms including geostationary and polar-orbiting satellites, an S-band radar, and time-lapse images from a camera located at the University of North Dakota in Grand Forks, North Dakota. Blowing snow is observed as plumes that resemble horizontal convective rolls (HCRs). Variations in near-infrared imagery are documented, and supporting observations suggest this is due to the occurrence or absence of clouds on top of the blowing snow layer. While lack of in situ observations preclude further investigation of physical differences between plumes, the utility of the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite to operational forecasters is discussed. Improvements to spatial, radiometric, and temporal resolution courtesy of the Advanced Baseline Imager (ABI) on board GOES-16 allows for daytime detection of blowing snow events that previously, was only possible with instruments on board polar-orbiting satellites. This has improved Impact-Based Decision Support Services (IDSS) at National Weather Service offices that deal with the hazard of blowing snow.

A Climatology of Atmospheric Patterns Associated with Red River Valley Blizzards

Stretching along the border of North Dakota and Minnesota, The Red River Valley (RRV) of the North has the highest frequency of reported blizzards within the contiguous United States. Despite the numerous impacts these events have, few systematic studies exist that discuss the meteorological properties of blizzards. As a result, forecasting these events and lesser blowing snow events is an ongoing challenge. This study presents a climatology of atmospheric patterns associated with RRV blizzards for the winter seasons of 1979–1980 and 2017–2018. Patterns were identified using subjective and objective techniques using meteorological fields from the North American Regional Re-analysis (NARR). The RRV experiences, on average, 2.6 events per year. Blizzard frequency is bimodal, with peaks occurring in December and March. The events can largely be typed into four meteorological categories dependent on the forcing that drives the blizzard: Alberta Clippers, Arctic Fronts, Colorado Lows, and Hybrids. The objective classification of these blizzards using a competitive neural network known as the Self-Organizing Map (SOM) demonstrates that gross segregation of the events can be achieved with a small (eight-class) map. This implies that objective analysis techniques can be used to identify these events in weather and climate model output that may aid future forecasting and risk assessment projects.

Using CALIOP to constrain blowing snow emissions of sea salt aerosols over Arctic and Antarctic sea ice

Abstract. Sea salt aerosols (SSA) produced on sea ice surfaces by blowing snow events or the lifting of frost flower crystals have been suggested as important sources of SSA during winter over polar regions. The magnitude and relative contribution of blowing snow and frost flower SSA sources, however, remain uncertain. In this study, we use 2007–2009 aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the GEOS-Chem global chemical transport model to constrain sources of SSA over Arctic and Antarctic sea ice. CALIOP retrievals show elevated levels of aerosol extinction coefficients (10–20 Mm−1) in the lower troposphere (0–2 km) over polar regions during cold months. The standard GEOS-Chem model underestimates the CALIOP extinction coefficients by 50 %–70 %. Adding frost flower emissions of SSA fails to explain the CALIOP observations. With blowing snow SSA emissions, the model captures the overall spatial and seasonal variation of CALIOP aerosol extinction coefficients over the polar regions but underestimates aerosol extinction over Arctic sea ice in fall to early winter and overestimates winter-to-spring extinction over Antarctic sea ice. We infer the monthly surface snow salinity on first-year sea ice required to minimize the discrepancy between CALIOP extinction coefficients and the GEOS-Chem simulation. The empirically derived snow salinity shows a decreasing trend between fall and spring. The optimized blowing snow model with inferred snow salinities generally agrees with CALIOP extinction coefficients to within 10 % over sea ice but underestimates them over the regions where frost flowers are expected to have a large influence. Frost flowers could thus contribute indirectly to SSA production by increasing the local surface snow salinity and, therefore, the SSA production from blowing snow. We carry out a case study of an Arctic blowing snow SSA feature predicted by GEOS-Chem and sampled by CALIOP. Using back trajectories, we link this feature to a blowing snow event that occurred 2 days earlier over first-year sea ice and was also detected by CALIOP.

Merging a terrain-based parameter with blowing snow fluxes for assessing snow redistribution in alpine terrain

Wind and the associated snow transport are dominating factors determining the snow distribution and accumulation in alpine areas. These factors result in a high spatial variability of snow heights that is difficult to quantify. In this study, we propose an efficient method for estimations of changes in snow heights during blowing snow events. We merge a terrain-based parameter Sx, which characterizes the degree of shelter or exposure of a point provided by the upwind terrain, with estimations of quantity of snow transported by the wind. This estimation is provided by snow particle counters (SPC) that estimate the snow flux, the mass of drifting snow particles per time and area. A modified terrain-based parameter Sxm is then used to distribute snow over the terrain. The results are compared with measured changes in snow heights resulting from blowing snow events, obtained with terrestrial laser scanning (TLS). Data and results are from the Col du Lac Blanc research site in the French Alps. We use a high raster resolution of 1 m, which is required when assessing the snow-redistribution situation in highly structured terrain or in the starting zones of small and medium-sized avalanches. Results show that the proposed method can estimate snow distributions based on a modified terrain parameter Sxm and measured snow flux data. It can reproduce patterns of snow redistribution and estimate changes in snow heights reasonably well, as shown by correlation coefficients (R) of 0.78 to 0.86. The derivation of the modified terrain parameter Sxm and snow flux are specific to the research site and not yet generally applicable. The formulations require the calibration and alteration of two parameters only for use in studies with other terrain and weather characteristics.

Operational implementation and evaluation of a blowing snow scheme for avalanche hazard forecasting

In alpine terrain, blowing snow events strongly affect the local evolution of the avalanche danger and must be taken into account by avalanche hazard forecasters. This study presents the implementation and the evaluation of the blowing snow scheme Sytron into the operational chain for avalanche hazard forecasting (named S2M) used in the main French mountain ranges. S2M-Sytron provides information on blowing snow occurrence and intensity per 300-m elevation bands and aspects for several regions of the French mountains. The wind forcing is provided by the meteorological analysis system SAFRAN. S2M-Sytron was evaluated for winter 2015/16 at 11 automatic stations measuring wind speed and blowing snow fluxes in the French Alps. The system detects 55% of blowing snow days with less than 10% of false alarms. S2M-Sytron captures the occurrence of blowing snow events with and without concurrent snowfall. Improvements are obtained when considering an updated parameterization for the properties of falling snow which reduces the threshold velocity for freshly fallen snow. Using observed wind speed instead of SAFRAN wind speed to drive Sytron shows further improvements at stations where SAFRAN wind speed differs from the observations due to local topographic features. Overall, S2M-Sytron provides a regional blowing snow assessment but cannot fully reproduce the local intensity of blowing snow events.