Wet Snow Events Research Articles

Future projections of wet snow frequency and wet snow load on overhead high voltage conductors over Italy

Heavy snowfalls represent some of the most serious hazard for transmission and distribution grids because they trigger the formation of sleeves on overhead power lines whose loads may cause outages and, consequently, prolonged disruptions of the energy supply. In order to provide useful information in planning and decision making to cope with wet snow impacts and strength the resilience of the Italian transmission network, some future climate projections about the occurrences of wet snow events and the related loads on overhead power lines were elaborated for different 20-year periods until 2100 by using 12 high-resolution Euro-CORDEX models (spatial resolution of ∼12 km) under the two different emission pathways RCP8.5 (without mitigation) and RCP4.5 (with moderate reductions of greenhouse gases emission). In addition, the reanalysis dataset MERIDA OI was used to bias-correct the climate models as well as to implement a methodology aimed to estimate climatic wet snow scenarios, by the use of a wet snow accretion model on power lines. The results were validated comparing them with wet snow loads observed at the Vinadio WILD station (Italy) and with the recorded failures in energy supply during some serious wet snow events occurred in Italy. Referring to Extreme Value Analysis, probability maps of wet snow loads exceeding some infrastructural hazard thresholds were estimated by means of the “Generalized Extreme Values” distributions, assuming a stationary climate in each 20-year period. In order not to have to assume climate stationarity, two new frequency indices were calculated directly from the outputs of the models, and the Wilcoxon test was applied to investigate the statistical significant of the trends, as well the agreement among the models to test the robustness of the results. In both methods the results point out relatively complex patterns of climate change effects: wet snow events will generally decrease in frequency and intensity at medium-low altitude, particularly under RCP8.5 pathway where projections highlight a sharply reduction of the events, as rainfalls will prevail over snowfalls due to global warming. But, by the end of the Century, wet snow events are likely to intensify over high Apennine and Alpine regions so far spared because of their historical cold temperatures. Finally, the evolution of wet snow events was investigated and tested more specifically for some mountain sites.

Extreme Value Analysis and Modelling of Wet Snow Accretion on Overhead Lines in Hungary

Wet snow events in Hungary can occasionally cause damage to overhead power lines and serious power supply failures. Return period calculation of such high snow mass events was determined upon a 50-year long data series available at 12 meteorological stations. Wet snow masses were estimated with a model of cylindrical accretion around wire of 3.1 cm diameter. Trends in the return periods and ice classes (according to the ISO 12494 standard) were assessed from division of the dataset into two periods (1965–1990 and 1991–2016). Ice classes in the range of R2 to R5 (mass of 0.7–6.1 kg m−1) were identified. The wet snow risk decreased in southern or central Hungary, while an increase was found in the western part of Hungary. Ice classes R6–R8 (up to 40 kg m−1) could occur in mountain areas of Hungary as indicated by numerical simulations in case studies. However, their return period is unknown due to the lack of long observation series.

Assessing past and future hazardous freezing rain and wet snow events in Manitoba, Canada using a pseudo-global warming approach

Freezing precipitation, in the form of freezing rain, freezing drizzle, and/or wet snow, can damage transportation networks, infrastructure, and vegetation. Ten events with freezing precipitation (including freezing rain and wet snow) over the province of Manitoba, Canada were examined using surface observational datasets, reanalysis products and 4-km resolution Weather Research and Forecasting (WRF) products that were both a retrospective control (CTRL) simulation as well as a pseudo-global warming (PGW) simulation. All events tracked to the south and/or east of Manitoba and most (8 of 10) events were associated with a consistent large scale pattern of extratropical cyclone with 500 hPa trough, low surface pressure center nearby, and an atmospheric river. Local factors, such as the 400 m elevated terrain of Riding Mountain, influenced 2 events mainly by altering surface temperature to be favorable for freezing precipitation. These events in the PGW simulation occurred 40–120 km farther north on average, with freezing rain generally being enhanced and wet snow generally being reduced, although wet snow was introduced into events which originally only had freezing rain. This study further showed that power lines aligned west to east, perpendicular to the strongest winds, are most susceptible to the consequences of icing and accretion within the current climate as well as the thermodynamically forced future one.

Improvements to melting snow behavior in a bulk microphysics scheme

Snow falling into a melting layer will eventually consist of a fraction of meltwater and hence change its characteristics in terms of size, shape, density and fall speed. Most microphysical parameterizations in numerical weather prediction models typically only represent purely solid or liquid hydrometeors. Generally, this has been an acceptable compromise since the melting layer is typically very shallow and adding a mixed solid/liquid particle type would result in increased computational time. This research shows how improvements were made to the treatment of melting snow in a microphysical parameterization within the Weather Research and Forecasting (WRF) model by implementing an approximation of snowflake melted fraction together with a physically-based expression for melting particle terminal velocity. In addition, the more appropriate definition of melting level defined by the wet-bulb temperature was consistently used in various process rates, all while not adding additional prognostic variables that would add computational cost. Multiple events observed during the 2015–2016 Olympic Mountain Experiment (OLYMPEX) were used to compare with the WRF model results. The modified scheme is able to represent disdrometer observations of joint particle size and fall velocity during wet snow events, as well as fall velocity profiles through the melting layer derived from a vertically-pointing radar. The improved scheme removes ‘bulls’ eyes' of snow accumulation in lee-side areas within the melting zone, and should result in better predictions of surface precipitation phase and amount.

Anti‐icing measures in the Italian power system

Despite the good operating results of passive anti-icing devices installed by the Italian transmission system operator (TSO) on high-voltage overhead lines (OHLs), the increasing number and consequences of wet snow events have prompted consideration of other anti-icing measures. In particular, attention focuses on the thermal approach, which is intended to achieve – before, or in the early stage of wet snow events – the anti-icing current in OHL phase conductors where snow sleeve formation is foreseen. The authors carried out a specific anti-icing study with reference to a 132 kV critical path in an area of the Apennines, with simulation results illustrated in this study. Anti-icing measures investigated were (i) load shifting actions and (ii) insertion of shunt reactors used as ballast loads. The results show that shunt reactors are the most effective anti-icing measure. The main disadvantage of this innovative solution is the degradation of voltage regulation in the area. However, this downside can be limited by combining the insertion of reactors with control of autotransformers under load tap changers. Similar results are obtained from analyses of other critical areas. As a result of these findings, the Italian TSO is now undertaking the installation of the first ballast reactors.

Rain-on-snow events in Alaska, their frequency and distribution from satellite observations

Wet snow and the icing events that frequently follow wintertime rain-on-snow (ROS) affect high latitude ecosystems at multiple spatial and temporal scales, including hydrology, carbon cycle, wildlife, and human development. However, the distribution of ROS events and their response to climatic changes are uncertain. In this study, we quantified ROS spatiotemporal variability across Alaska during the cold season (November to March) and clarified the influence of precipitation and temperature variations on these patterns. A satellite-based daily ROS geospatial classification was derived for the region by combining remote sensing information from overlapping MODIS and AMSR sensor records. The ROS record extended over the recent satellite record (water years 2003–2011 and 2013–2016) and was derived at a daily time step and 6 km grid, benefiting from finer (500 m) resolution MODIS snow cover observations and coarser (12.5 km) AMSR microwave brightness temperature-based freeze–thaw retrievals. The classification showed favorable ROS detection accuracy (75%–100%) against in situ climate observations across Alaska. Pixel-wise correlation analysis was used to clarify relationships between the ROS patterns and underlying physiography and climatic influences. Our findings indicate that cold season ROS events are most common during autumn and spring months along the maritime Bering Sea coast and boreal interior regions, but are infrequent on the colder arctic North Slope. The frequency and extent of ROS events coincided with warm temperature anomalies (p < 0.1), but showed a generally weaker relationship with precipitation. The weaker precipitation relationship was attributed to several factors, including large uncertainty in cold season precipitation measurements, and the important contribution of humidity and turbulent energy transfer in driving snowmelt and icing events independent of rainfall. Our results suggest that as high latitude temperatures increase, wet snow and ROS events will also increase in frequency and extent, particularly in the southwestern and interior regions of Alaska.

Effect of spatial variability of wet snow on modeled and observed microwave emissions

Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes including the presence of liquid water in the snow, which can be an indicator of snowmelt. The objective of this research is to test the sensitivity of the emission signal as it relates to the spatial distribution of liquid water content in the snowpack. This signal response was evaluated over an area approximately the size of a microwave pixel to assess whether a relationship exists between the aerial extent of wet snow and the magnitude of the TB response. A sensitivity analysis was performed using a high-resolution, physically based snow-emission model to simulate microwave emissions. The signal response to wet snow was evaluated given a range of spatially distributed snowpack conditions. Daily snow states were simulated for a 9-year period using a high-resolution (50m) energy balance snow model over a 34×34km domain. These data were fed into a microwave emission model to simulate brightness temperatures. A near-linear relationship was found between the TB signal response over a spatially heterogeneous snowpack and the percent area with liquid water content (LWC) present. The results were confirmed by evaluating actual wet snow events over a 9-year period. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the impact of spatially distributed snowmelt as detected by passive microwave data.

50-year return-period wet-snow load estimation based on weather station data for overhead line design in France

Abstract. Historically, as far as wet-snow loads were concerned, overhead line design was often based on experience or long-term applications with positive results. New standard like CENELEC EN 50341-1 (2012) take into account characteristic loads, i.e. 50-year return-period loads, for the overhead line design. This article proposes a method to estimate characteristic wet-snow loads based on meteorological data recorded at weather stations. The model used to calculate those loads is mainly inspired by a recent article written by Nygaard et al. (2013a) in which a new parameterization is proposed for the classical cylindrical wet-snow accretion model, as described in ISO 12494 standard (2001), annex C. After a complete description of the model and its parameterization adapted to French wet-snow events, the statistical issues are examined. Then, the model is used with the meteorological data of 87 weather stations in order to calculate wet-snow loads whose relevance has been positively tested according to real damages recorded in a complete wet-snow event database. At last, the characteristic loads of those 87 stations have been determined according to all the loads generated by the model and processed by a peak-over-threshold (POT) method. A practical method to determine the 90% confidence intervals of those characteristic values is given. As it is demonstrated that there is only one value of the ice class (IC) masses proposed by ISO 12494 in each confidence interval, characteristic loads can be easily expressed in terms of ICs. That method shows that ICs ranging from R1 (0.5 kg m−1) to R5 (5 kg m−1) could be used for overhead line design in France.

The snow storm of 8 March 2010 in Catalonia (Spain): a paradigmatic wet-snow event with a high societal impact

Abstract. A heavy precipitation event swept over Catalonia (NE Spain) on 8 March 2010, with a total amount that exceeded 100 mm locally and snowfall of more than 60 cm near the coast. Unusual for this region and at this time of the year, this snowfall event affected mainly the coastal region and was accompanied by thunderstorms and strong wind gusts in some areas. Most of the damage was due to "wet snow", a kind of snow that favours accretion on power lines and causes line-breaking and subsequent interruption of the electricity supply. This paper conducts an interdisciplinary analysis of the event to show its great societal impact and the role played by the recently developed social networks (it has been called the first "Snowfall 2.0"), as well to analyse the meteorological factors associated with the major damage, and to propose an indicator that could summarise them. With this aim, the paper introduces the event and its societal impact and compares it with other important snowfalls that have affected the Catalan coast, using the PRESSGAMA database. The second part of the paper shows the event's main meteorological features and analyses the near-surface atmospheric variables responsible for the major damage through the application of the SAFRAN (Système d'analyse fournissant des renseignements atmosphériques à la neige) mesoscale analysis, which, together with the proposed "wind, wet-snow index" (WWSI), allows to estimate the severity of the event. This snow storm provides further evidence of our vulnerability to natural hazards and highlights the importance of a multidisciplinary approach in analysing societal impact and the meteorological factors responsible for this kind of event.

Modeling the influence of periodic loads on snow detachment from suspended cables

Disasters to transmission lines due to wet snow events motivate researchers to carry out advanced dynamic analysis in order to understand and predict the line transient response to complex dynamic loads. The interest of numerical modeling of the effects of snow shedding phenomena is to check whether the amplitude of the induced motion is within the design limits of electrical clearances with the ground or with other conductors. This paper aims to develop a numerical model to consider wet snow adhesion on transmission line cables, and consequently, to predict snow shedding from cables due to dynamic effects. The finite element analysis software ADINA is used to simulate the nonlinear dynamic response of a snow-covered cable to the application of periodic loads and subsequent effects of snow shedding. Comparison of model results and small scale experimental simulations is used to validate the model. The model predicts cable rebound height following the application of different periodic loads as well as different snow shedding scenarios. It is found that for highest adhesion, snow sheds only at high excitation frequencies, whereas the inverse occurs for low adhesion. Cohesion force between the snow particles is greater than its adhesion strength to the cable. For large snow thickness, it is rather difficult to obtain snow shedding comparing to the small thickness.

Simulating wet snow loads on power line cables by a simple model

Abstract A wet snow event in the northwest part of Germany caused extensive damage to electric power lines in November 2005. Maximum wet snow loads of approximately 50 Nm − 1 were estimated by evaluating the snow deposit diameter from photographs and the snow density from meteorological data. A simple wet snow accretion model was applied to reconstruct the snow accretion process. The maximum wet snow loads estimated by the model are in good agreement with the observations when the model input parameter, visibility, was adjusted by a constant factor for night-time observations. The improved wet snow accretion model was also applied to historical weather data for the area demonstrating the power and weaknesses of the extreme value analysis of wet snow events.

A comparison of selected models for estimating cable icing

In many cold climate countries, it is becoming increasingly important to monitor transmission line icing. Indeed, by knowing in advance of localized danger for icing overloads, electric utilities can take measures in time to prevent generalized failure of the power transmission network. Recently in Canada, a study was made to compare the estimation of a few icing models working from meteorological data in estimating ice loads for freezing rain events. The models tested were using only standard meteorological parameters, i.e. wind speed and direction, temperature and precipitation rate. This study has shown that standard meteorological parameters can only achieve very limited accuracy, especially for longer icing events. However, with the help of an additional instrument monitoring the icing rate intensity, a significant improvement in model prediction might be achieved. The icing rate meter (IRM) which counts icing and de-icing cycles per unit time on a standard probe can be used to estimate the icing intensity. A cable icing estimation is then made by taking into consideration the accretion size, temperature, wind speed and direction, and precipitation rate. In this paper, a comparison is made between the predictions of two previously tested models (one obtained and the other reconstructed from their description in the public literature) and of a model based on the icing rate meter readings. The models are tested against nineteen events recorded on an icing test line at Mt. Valin, Canada, during the winter season 1991–1992. These events are mostly rime resulting from in-cloud icing. However, freezing rain and wet snow events were also recorded. Results indicate that a significant improvement in the estimation is attained by using the icing rate meter data together with the other standard meteorological parameters.