Snow Depth Sensors Research Articles

A random forest approach to quality-checking automatic snow-depth sensor measurements

Abstract. State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models.

Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)

Abstract. Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

Abstract. We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 103 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate.

Energy Efficient Wireless Sink Node for Monitoring of Snow Environment

Conventionally, sink node is considered to have large hardware and energy resources; however, many times sink node is working in same conditions as source nodes, especially when deployed for monitoring of the snow environment. In this paper, an effort has been made to practically realize a sink node which is energy efficient and cost effective for monitoring applications. To save energy, the Main Power Module is designed to provide controlled powers to sensors and sub-modules. The paper discusses design aspects of the sink node and its long-term field evaluation with environmental sensors, especially the Snow Depth Sensor of MaxBotix. Field performance of Snow Depth Sensor has been enhanced by Euclidean Minimum Distance filter which improved the correlation of data to 0.997. The proposed design helps to achieve energy consumption of 42.72mWh which is significantly lower than the previous work. The reliable working of the sink node in the long-term field evaluation indicates that snow environment can be monitored at less expense of energy by employing proposed sensors and the specially designed sink node.

Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation

ABSTRACT Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by a rapid decline in manual observations (loss of over 800 manual observing sites between 1995 and 2017) and an increasing number of automated stations equipped with sonic snow depth sensors. In 2017 these accounted for approximately 45% of the network and more than 80% of the snow depth–observing network north of latitude 55°N. Automated stations are characterized by more frequent missing and anomalous data than manual ruler observations, particularly at Arctic sites. A comparison of closely located automated sonic and manual ruler observations showed similar numbers of days with snow cover but the sonic sensors detected significantly lower snow depths. For time series analysis of annual snow cover variables, the systematic difference between ruler and sonic snow depth can be removed using a common 2003–2016 reference period to compute snow cover anomalies. The updated trend results are broadly similar to previously published assessments showing long-term decreases in annual snow cover duration (SCD) and snow depth over most of Canada, with the largest decreases observed in spring snow cover and seasonal maximum snow depth (SDmax). Significant declines in SCD and SDmax of −1.7 (±1.1) days decade-1 and −1.8 cm (±0.8) cm decade−1 were observed in the Canada–averaged series over the 1955–2017 period. These trends mainly reflect snow cover conditions over southern Canada where the observing network is concentrated and where there are significant negative correlations between snow cover and winter air temperature. Declining numbers of stations reporting snow depth, issues with sonic sensor data quality, and systematic differences between ruler and sonic sensor measurements are major challenges for continued climate monitoring with the current ECCC snow depth–observing network.

ROLE OF EARTH OBSERVATION DATA AND HYDROLOGICAL MODELING IN SUPPORTING UN SDGs IN NORTH WEST HIMALAYA

Abstract. The sustainable usage and accurate assessment of water resources in North West Himalaya (NWH) is very important for respective policy makers. NWH receives precipitation from both southwest and northeast monsoon system. The detailed assessment of current and future water resources and hydrological cycle component for NWH river basins using earth observation (EO) satellites and hydrological models is very critical for attaining United Nations sustainable development goals (SDGs) namely, climate action, affordable and clean energy, clean water and sanitation and building resilient infrastructure. Present work highlights the role of various EO sensors and hydrological models and ground based instruments for improved assessment of water resources of NWH river basins. The complete inventory of NWH surface water (including glacier lakes of UK, HP), snow cover, delta SWE and glaciers database was accomplished with Remote Sensing (RS) datasets. Similarly, glacier velocity was estimated for all major glaciers of NWH using feature tracking and differential interferometry (DInSAR) methods. Fully distributed grid based hydrological model was setup for entire NWH and model calibration/validation was done for Beas, Satluj, Upper Ganga and Jhelum river basins. Quantification of relative contribution of snowmelt, glacier melt and rainfall-runoff was estimated for Bhagirathi basin upto Uttarkashi. An extensive network of automatic weather stations (AWS), 27 nos, 10 snow depth sensors, 04 digital water level recorders, two snow pack analysers and 06 long wave solar radiation sensors were installed in various sites of HP and UK for hydro-meteorological data collection, model simulation and validation. A future climate change simulations were done for Beas and Jhelum basins using CORDEX 4.5 and 8.5 scenarios from 2006–2100. Number of flood peaks were found to be increasing in number as well as decrease in total snow fall.

Gap-filling snow-depth time-series with Kalman Filtering-Smoothing and Expectation Maximization: Proof of concept using spatially dense wireless-sensor-network data

We assessed the performance of Kalman Filtering-Smoothing and Expectation Maximization (EM-KF) in gap filling snow-depth sensor data. To this end, hourly snow-depth data from three spatially dense wireless-sensor networks were randomly removed and imputed using EM-KF. Maximum gap size was 40+ hours and differences between artificially removed and gap-filled data larger than 20 cm were removed before computing Root Mean Square Error and Bias. These differences are spurious over- and underestimations that can generally be identified through visual inspection, likely due to instability in the gap-filling process. The expected accuracy of EM-KF in this initial, controlled proof of concept is close to measurement uncertainty (1 to 2 cm for ultrasonic depth sensors). Compared to regressing missing data against nearby sensors, a frequently used strategy in the field, EM-KF tends to yield smaller errors in networks with a comparatively large number of co-located sensors (nine and eight as opposed to four in a third network). In these data-rich networks, maximum differences in daily Root Mean Square Errors between EM-KF and a regression are up to 6 to 8 cm at a daily time scale, with peaks in winter and in particular during snowfalls. EM-KF yields superior results particularly during snowfalls, likely because it exploits the temporal structure and uncertainty in the data through a state-space model. In the third network with fewer co-located sensors, differences in accuracy between EM-KF and a multilinear regression were inconsistent. This implies that the performance of EM-KF benefits from increasing the amount of available information and with increasing dependency of data across nodes. Temporally and spatially dense snow-depth data are being increasingly collected in operational contexts: EM-KF may support supervised filling of gaps in these data – particularly during snowfall events – and thus provide continuous-time information for avalanche or water-resources forecasting in snow-dominated regions. Automatic, unsupervised gap-filling using EM-KF will necessarily need more research to identify reasons of spurious over- and underestimations. Future work should also upscale this proof of concept to operational sensor networks spanning large water basins to bring conclusions closer to real-world applications.

Assimilating snow observations to snow interception process simulations

AbstractSnow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post‐storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time‐lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule‐based direct insertion method. Interception determined using density estimates from the Hedstrom‐Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree‐measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time‐lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.

Snowfall interception in a deciduousNothofagusforest and implications for spatial snowpack distribution

AbstractNative Nothofagus forests in the midlatitude region of the Andes Cordillera are notorious biodiversity hot spots, uniquely situated in the Southern Hemisphere such that they develop in snow‐dominated reaches of this mountain range. Spanning a smaller surface area than similar ecosystems, where forests and snow coexist in the Northern Hemisphere, the interaction between vegetation and snow processes in this ecotone has received lesser attention. We present the first systematic study of snow–vegetation interactions in the Nothofagus forests of the Southern Andes, focusing on how the interplay between interception and climate determines patterns of snow water equivalent (SWE) variability. The Valle Hermoso experimental catchment, located in the Nevados de Chillán vicinity, was fitted with eight snow depth sensors that provided continuous measurements at varying elevations, aspect, and forest cover. Also, manual measurements of snow properties were obtained during snow surveys conducted during end of winter and spring seasons for 3 years, between 2015 and 2017. Each year was characterized by distinct climatological conditions, with 2016 representing one of the driest winters on record in this region. Distance to canopy, leaf area index, and total gap area were measured at each observational site. A regression model was built on the basis of statistical analysis of local parameters to model snow interception in this kind of forest. We find that interception implied a 23.2% reduction in snow accumulation in forested sites compared with clearings. The interception in these deciduous trees represents, on average, 23.6% of total annual snowfall, reaching a maximum measured interception value of 13.8‐mm SWE for all snowfall events analysed in this research.

Bioclimatic and Soil Moisture Monitoring Across Elevation in a Mountain Watershed: Opportunities for Research and Resource Management

AbstractSoil moisture data are critical to understanding biophysical and societal impacts of climate change. However, soil moisture data availability is limited due to sparse in situ monitoring, particularly in mountain regions. Here we present methods, specifications, and initial results from the interactive Roaring Fork Observation Network (iRON), a soil, weather, and ecological monitoring system in the Southern Rocky Mountains of Colorado. Initiated in 2012, the network is currently composed of nine stations, distributed in elevation from 1,890 to 3,680 m, that continually collect and transmit measurements of soil moisture at three depths (5, 20, and 50 cm), soil temperature (20 cm), and meteorological conditions. Time‐lapse cameras for phenological observations, snow depth sensors, and periodic co‐located vegetation surveys complement selected stations. iRON was conceived and designed with the joint purpose of supporting bioclimatic research and resource management objectives in a snow‐dominated watershed. In the short term, iRON data can be applied to assessing the impact of temperature and precipitation on seasonal soil moisture conditions and trends. As more data are collected over time, iRON will help improve understanding of climate‐driven changes to soil, vegetation, and hydrologic conditions. In presenting this network and its initial data, we hope that the network's elevational gradient will contribute to bioclimatic mountain research, while active collaboration with partners in resource management may provide a model for science‐practice interaction in support of long‐term monitoring.

Canopy Effects on Snow Accumulation: Observations from Lidar, Canonical-View Photos, and Continuous Ground Measurements from Sensor Networks

A variety of canopy metrics were extracted from the snow-off airborne light detection and ranging (lidar) measurements over three study areas in the central and southern Sierra Nevada. Two of the sites, Providence and Wolverton, had wireless snow-depth sensors since 2008, with the third site, Pinecrest having sensors since 2014. At Wolverton and Pinecrest, images were captured and the sky-view factors were derived from hemispherical-view photos. We found the variation of snow accumulation across the landscape to be significantly related to canopy-cover conditions. Using a regularized regression model Elastic Net to model the normalized snow accumulation with canopy metrics as independent variables, we found that about 50 % of snow accumulation variability at each site can be explained by the canopy metrics from lidar.

Improving simulations of precipitation phase and snowpack at a site subject to cold air intrusions: Snoqualmie Pass, WA

Low-level cold air from eastern Washington often flows westward through mountain passes in the Washington Cascades, creating localized inversions and locally reducing climatological temperatures. The persistence of this inversion during a frontal passage can result in complex patterns of snow and rain that are difficult to predict. Yet, these predictions are critical to support highway avalanche control, ski resort operations, and modeling of headwater snowpack storage. In this study we used observations of precipitation phase from a disdrometer and snow depth sensors across Snoqualmie Pass, WA, to evaluate surface-air-temperature-based and mesoscale-model-based predictions of precipitation phase during the anomalously warm 2014-2015 winter. Correlations of phase between surface-based methods and observations were greatly improved (r2 from 0.45 to 0.66) and frozen precipitation biases reduced (+36% to -6% of accumulated snow water equivalent) by using air temperature from a nearby higher-elevation station, which was less impacted by low-level inversions. Alternatively, we found a hybrid method that combines surface-based predictions with output from the Weather Research and Forecasting mesoscale model to have improved skill (r2 = 0.61) over both parent models (r2 = 0.42 and 0.55). These results suggest that prediction of precipitation phase in mountain passes can be improved by incorporating observations or models from above the surface layer.

Design and Implementation of a Novel Automated Snow Depth Sensing System

Accurate and automated snowfall measurement is a major challenge in different environmental conditions. Minimization of environmental effects is essential for accurate measurement of snowfall. It is required to get reliable information about the snow depth for true weather report and better forecasting of snowfall. Automation of snowfall measurement is also necessary to remove the human error, to get repeated information about the snow depth, continuous measurement of snow depth and instantaneously storage of measured data. The first phase of this paper addresses design and implementation of an encoder based electromechanical snow depth sensor to enhance accuracy and resolution of snow depth measurement. It works as a standalone-embedded device with intelligent functions. Additionally, a dedicated virtual instrument for the sensing device is developed to provide easy connectivity with PC for online snow depth measurement. The second phase grapples with measurement of snowfall in the different climate conditions in which ultrasonic snow depth sensor faces large variability. The results of measured snow depth with prototype of proposed sensor are presented which are comparable with direct and standard measurement method of snow depth and indicate higher level of accuracy.

Railway Disaster Prevention and Snow Survey Design Optimization Technology

Snow disasters cause immeasurable losses to human society each year, threatening people's lives and proper- ties and therefore attaching much importance to the measurement of snow cover. Snowfall, however, is one of the most difficult to be measured among meteorological elements, especially on the railways. To solve this problem, the real-time monitoring of snow along the railway can be helpful. This can effectively avoid railway accidents caused by snowstorms, improving transport safety and efficiency as well as providing a quantitative reference for the safe operation of the rail- way. In this paper, a snow disaster prevention system is designed to measure the snow depth and issue the early warning. Also this paper achieves measurement by the integration of laser and video, where laser distance sensors detect snow depth and image sensors capture videos to find out whether the track is covered with snow and to obtain snow depth with the help of photographing rulers.

Verification of analysed and forecasted winter precipitation in complex terrain

Abstract. Numerical Weather Prediction (NWP) models are rarely verified for mountainous regions during the winter season, although avalanche forecasters and other decision makers frequently rely on NWP models. Winter precipitation from two NWP models (GEM-LAM and GEM15) and from a precipitation analysis system (CaPA) was verified at approximately 100 stations in the mountains of western Canada and the north-western US. Ultrasonic snow depth sensors and snow pillows were used to observe daily precipitation amounts. For the first time, a detailed objective validation scheme was performed highlighting many aspects of forecast quality. Overall, the models underestimated precipitation amounts, although low precipitation categories were overestimated. The finer resolution model GEM-LAM performed best in all analysed aspects of model performance, while the precipitation analysis system performed worst. An analysis of the economic value of large precipitation categories showed that only mitigation measures with low cost–loss ratios (i.e. measures that can be performed often) will benefit from these NWP models. This means that measures with large associated costs relative to anticipated losses when the measure is not performed should not or not primarily depend on forecasted precipitation.

Snowline and snow cover monitoring at high spatial resolution in a mountainous river basin based on a time-lapse camera at a daily scale

Snowline change and snow cover distribution patterns are still poorly understood in steep alpine basins of the Qilian Mountainous region because fast changes in snow cover cannot be observed by current sensing methods due to their short time scale. To address this issue of daily snowline and snow cover observations, a ground-based EOS 7D camera and four infrared digital hunting video cameras (LTL5210A) were installed around the Hulugou river basin (HRB) in the Qilian Mountains along northeastern margin of the Tibetan Plateau (3815'54"N, 9952'53"E) in September 2011. Pictures taken with the EOS 7D camera were georeferenced and the data from four LIL5210A cameras and snow depth sensors were used to assist snow cover estimation. The results showed that the time-lapse photography can be very useful and precise for monitoring snowline and snow cover in mountainous regions. The snowline and snow cover evolution at this basin can be precisely captured at daily scale. In HRB snow cover is mainly established after October, and the maximum snow cover appeared during February and March. The consistent rise of the snowline and decrease in snow cover appeared after middle part of March. This melt process is strongly associated with air temperature increase.

Winter is coming.

In Antarctica this week, the daylight is dimming as the polar night approaches. “The weather is pretty changing at the moment. There are a lot of low-pressure systems coming from the Weddell Sea, leading to high wind speeds up to 55 knots and a lot of snowdrift,” says meteorologist Elena Stautzebach. She is part of a small team of early-career scientists overwintering at the German research base Neumayer-Station III. “In between, we have periods of sunny weather and temperatures around −10°C.” But the darkness will be continuous starting on 28 May. It will last 56 days. Located 450 kilometers from its closest neighbor, the station has a proud meteorological history: Scientists have conducted measurements and observations there every 3 hours for more than 30 years. Many measurements run on a programmed schedule. But visual observations—cloud and weather conditions, air pressure, dew-point temperature, wind speed and direction, and visibility, for example—must be made by people, rather, a person. “We have just one meteorologist, who has to sleep now and then,” says the team's lead off-continent contact, Gert Konig-Langlo of the Alfred Wegener Institute for Polar and Marine Research in Germany. This winter, that meteorologist is Stautzebach. The team operates on Coordinated Universal Time. At 9 a.m., noon, 3 p.m., 6 p.m., 9 p.m., and midnight, she is outside collecting data, whatever the weather. ![Figure][1] “Overwintering and being in charge of a scientific observatory in Antarctica is a challenge.” ILLUSTRATION: MARC ROSENTHAL “As it gets darker, it is getting more difficult to distinguish between different cloud layers and types. Observations are therefore taking a lot longer without daylight, as you have to stay outside longer in order to adapt your eyes to the darkness. But this also means that you have time to watch the beautiful stars of the Southern Hemisphere and that you have higher chances of seeing polar lights,” she says. To ensure that her measurements are accurate, Stautzebach must do daily rounds, dusting snow and breaking off ice crystals from 10 radiation sensors, two ventilated temperature sensors, two wind anemometers (one of them 10 meters above the ground), three humidity sensors, and snow depth and air pressure sensors—all located 200 meters from the station to avoid wind and shadow influences. Stautzebach also climbs up on the station's roof to check the visibility sensor and the ceilometer, which detects cloud height. As the temperature drops, Stautzebach will start to travel 20 kilometers by snowmobile every 3 weeks to Atka Bay, where she will measure the first growth of sea ice and platelet ice. As winter digs in, the ice will become strong enough to support an automated meteorological station equipped with radiation sensors. “Overwintering and being in charge of a scientific observatory in Antarctica is a challenge,” Stautzebach says. “During the winter season, we have to deal with all sorts of situations on our own. Our team includes one engineer who is responsible for the functioning of the entire station, one IT-specialist who has to maintain the communication with the world outside Antarctica, and one doctor. Additionally, in case of a fire, we have to turn into our own firefighters.” Winter for the team officially started at the end of February “when the last plane left Antarctica,” Stautzebach says. “We are now on our own until November.” The nine-person team will then continue its work through the following Antarctic summer, helping the next overwintering team prepare for what is coming. “Now that I am in Antarctica, the continent with the harshest weather conditions in the world, I can't wait to experience probably the most extreme weather in my life. Even more exciting than cold temperatures are storm events with snowdrift and whiteouts. As soon as the highest wind speeds of a storm event are reached, I often go outside with one of my colleagues (and a radio and GPS of course) in order to experience the strength of the wind. “This winter I will simply enjoy the harsh weather conditions in Antarctica, and as soon as I am back in Europe, I'll think back to storm events that I had the chance to experience down here.” [1]: pending:yes

Upward-looking L-band FMCW radar for snow cover monitoring

Forecasting snow avalanche danger in mountainous regions is of major importance for the protection of infrastructure in avalanche run-out zones. Inexpensive measurement devices capable of measuring snow height and layer properties in avalanche starting zones may help to improve the quality of risk assessment. We present a low-cost L-band frequency modulated continuous wave radar system (FMCW) in upward-looking configuration. To monitor the snowpack evolution, the radar system was deployed in fall and subsequently was covered by snowfalls. During two winter seasons we recorded reflections from the overlying snowpack. The influence of reflection magnitude and phase to the measured frequency spectra, as well as the influence of signal processing were investigated. We present a method to extract the phase of the reflection coefficients from the phase response of the frequency spectra and their integration into the presentation of the measurement data. The phase information significantly improved the detectability of the temporal evolution of the snow surface reflection. We developed an automated and a semi-automated snow surface tracking algorithm. Results were compared with independently measured snow height from a laser snow-depth sensor and results derived from an upward-looking impulse radar system (upGPR). The semi-automated tracking used the phase information and had an accuracy of about 6 to 8cm for dry-snow conditions, similar to the accuracy of the upGPR, compared to measurements from the laser snow-depth sensor. The percolation of water was observable in the radargrams. Results suggest that the upward-looking FMCW system may be a valuable alternative to conventional snow-depth sensors for locations, where fixed installations above ground are not feasible.

Wind effects on snow cover in Pascua-Lama, Dry Andes of Chile

We present the first application of a distributed snow model (SnowModel) in the instrumented site of Pascua-Lama in the Dry Andes (2600–5630m above sea level, 29°S). A model experiment was performed to assess the effect of wind on the snow cover patterns. A particular objective was to evaluate the role of blowing snow on the glacier formation. The model was run using the data from 11 weather stations over a complete snow season. First, a cross-validation of the meteorological variables interpolation model (MicroMet submodel) was performed to evaluate the performance of the simulated meteorological forcing. Secondly, two SnowModel simulations were set up: one without and the other with the wind transport submodel (SnowTran-3D). Results from both simulations were compared with in situ snow depth measurements and remotely sensed snow cover data. The inclusion of SnowTran-3D does not change the fact that the model is unable to capture the small-scale snow depth spatial variability (as captured by in situ snow depth sensors). However, remote sensing data (MODIS daily snow product) indicate that at broader scales the wind module produced an improved representation of the snow distribution near the glaciers (2-D correlation coefficient increased from R=0.04 to R=0.27). The model outputs show that a key process is the sublimation of blowing snow, which amounts to 18% of the total ablation over the whole study area, with a high spatial variability. The effect of snow drift is more visible on the glaciers, where wind-transported snow accumulates preferentially. Net deposition occurred for 43% of the glacier grid points, whereas it is only 23% of non-glacier grid points located above the minimum glacier altitude (4475m).

The Monitoring Network of the Vancouver 2010 Olympics

An innovative monitoring network was implemented to support the operational and science programs for the Vancouver 2010 Winter Olympics. It consisted of in situ weather stations on custom-designed platforms. The sensors included an HMP45C for temperature, humidity and pressure, a tipping bucket rain gauge, an acoustic snow depth sensor, a Pluvio 1 precipitation gauge and an anemometer placed at gauge height and at 10 m height. Modifications to commercial automated precipitation gauges were necessary for the heavy snowfall conditions. Advanced or emerging technologies were deployed to support scientific and nowcasting studies into precipitation intensity, typing, visibility and wind. The sensors included an FD12P visibility and precipitation sensor, a precipitation occurrence sensing system (POSS) present weather sensor, a Hotplate precipitation sensor and a Parsivel disdrometer. Data were collected at 1 min sampling intervals. A Doppler weather radar was deployed in a valley location and provided critical detailed low-level data. An X-band dual-polarized radar was deployed by the National Oceanic and Atmospheric Administration to monitor Vancouver and Cypress Mountain. Three remote sensing stations for vertical profiling were established. At the base of Whistler Mountain, a micro-rain radar, a 22-channel radiometer, a ceilometer, a Parsivel and a POSS were installed. At the base of Cypress Mountain, a micro-rain radar, a ceilometer, a low cost rain sensor (LCR by ATTEX) and a POSS were installed. At Squamish, a wind profiler and a POSS were installed. Weather sensors were mounted on the Whistler Village Gondola and on the Peak to Peak gondola. Sites were established along the Whistler Mountain slope and at other key locations. The combination of sites and instruments formed a comprehensive network to provide observations appropriate for nowcasting in winter complex terrain and investigate precipitation, visibility and wind processes. The contribution provides a detailed description of the network, their sensors, the innovations and some examples.