Storm Severity Index Research Articles

Toward the Development of an Impact-Based Decision Support Tool for Surface-Transportation Hazards. Part II: An Hourly Winter Storm Severity Index

Abstract In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety. Significance Statement A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.

Winter Storm Severity Index in Alaska: Understanding the Usefulness for Impact-Based Winter Weather Severity Forecast Information

Abstract There is growing interest in impact-based decision support services to address complex decision-making, especially for winter storm forecasting. Understanding users’ needs for winter storm forecast information is necessary to make such impact-based winter forecasts relevant and useful to the diverse regions affected. A mixed-method social science research study investigated extending the winter storm severity index (WSSI) [operational for the contiguous United States (CONUS)] to Alaska, with consideration of the distinct needs of Alaskan stakeholders and the Alaskan climate. Data availability differences suggest the need for an Alaska-specific WSSI, calling for user feedback to inform the direction of product modifications. Focus groups and surveys in six regions of Alaska provided information on how the WSSI components, definitions, and categorization of impacts could align with stakeholder expectations and led to recommendations for the Weather Prediction Center to consider in developing the WSSI Alaska product. Overall, wind (strength and direction) and precipitation are key components to include. Air travel is a critical concern requiring wind and visibility information, while road travel is less emphasized (contrasting with CONUS needs). Special Weather Statements and Winter Storm Warnings are highly valued, and storm trajectory and transition (between precipitation types) information are the important contexts for decision-makers. Alaska is accustomed to and prepared for winter impacts but being able to understand how components (wind, snow, and ice) contribute to overall impact enhances the ability to respond and mitigate damage effectively. The WSSI adapted for Alaska can help address regional forecast needs, particularly valuable as the climate changes and typical winter conditions become more variable. Significance Statement Impact-based support services can assist decision-makers in prioritizing preparedness and mitigation actions related to winter storm events. The winter storm severity index adapted for specific considerations in Alaska (such as including wind and visibility components) can extend winter weather impact-based forecasting’s utility. Additionally, lessons learned from the process of adapting a national product to specific regional needs may inform best practices for gathering stakeholder input and feedback.

Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

AbstractCrop yield prediction has gained major potential for global food production. Predicting crop yields based on specific parameters like soil, environment, crop, and water has been an interesting research topic in recent decades. To accurately predict crop yields, measuring the severities of natural calamities including water level is mainly required. However, the existing studies failed to predict crop yields accurately because of various issues like overfitting problems, difficulty in training, inability to handle large data, and reduced learning capability. Thus, the proposed study develops an efficient mechanism for accurately predicting crop yields by analyzing several natural calamities. Here, the input samples are initially pre‐processed to remove unwanted noises using data normalization and standardization. To enhance the performance of crop yield prediction, natural calamities are computed by using an Extreme Gradient Boosting (XGBoost) model based on parameters like the Palmer Drought Severity Index (PDSI), Severe Hail Index (SHI), and Storm Severity Index (SSI). Also, the hyperparameters of XGBoost model are tuned by utilizing Sheep Flock Optimization Algorithm (SFOA). Finally, the crop yield is predicted by proposing a new one‐dimensional convolutional gated recurrent unit neural network (1D‐CGRU). The proposed classifier predicts the crop yields with reduced error rates like mean square error (MSE) of 0.4363, root mean square error (RMSE) of 0.1904, normalized root mean squared error (NRMSE) of 0.00101, mean absolute error (MAE) of 0.2437, and R‐squared (R2) of .2756. Also, the significant findings of the proposed study positively indicate that this study can be applicable to real‐time agricultural practices and is highly suitable for water quality predictions. Also, it can assist farmers and farming businesses in predicting the yield of crops in a specific season when to harvest and crop a plant for attaining improved crop yields.

Synoptic conditions conducive for compound wind-flood events in Great Britain in present and future climates

Extreme wind is the main driver of loss in North-West Europe, with flooding being the second-highest driver. These hazards are currently modelled independently, and it is unclear what the contribution of their co-occurrence is to loss. They are often associated with extra-tropical cyclones, with studies focusing on co-occurrence of extreme meteorological variables. However, there has not been a systematic assessment of the meteorological drivers of the co-occurring impacts of compound wind-flood events. This study quantifies this using an established storm severity index (SSI) and recently developed flood severity index (FSI), applied to the UKCP18 12 km regional climate simulations, and a Great Britain (GB) focused hydrological model. The meteorological drivers are assessed using 30 weather types, which are designed to capture a broad spectrum of GB weather. Daily extreme compound events (exceeding 99th percentile of both SSI and FSI) are generally associated with cyclonic weather patterns, often from the positive phase of the North Atlantic Oscillation (NAO+) and Northwesterly classifications. Extreme compound events happen in a larger variety of weather patterns in a future climate. The location of extreme precipitation events shifts southward towards regions of increased exposure. The risk of extreme compound events increases almost four-fold in the UKCP18 simulations (from 14 events in the historical period, to 55 events in the future period). It is also more likely for there to be multi-day compound events. At seasonal timescales years tend to be either flood-prone or wind-damage-prone. In a future climate there is a larger proportion of years experiencing extreme seasonal SSI and FSI totals. This could lead to increases in reinsurance losses if not factored into current modelling.

Future increased risk from extratropical windstorms in northern Europe

European windstorms cause socioeconomic losses due to wind damage. Projections of future losses from such storms are subject to uncertainties from the frequency and tracks of the storms, their intensities and definitions thereof, and socio-economic scenarios. We use two storm severity indices applied to objectively identified extratropical cyclone footprints from a multi-model ensemble of state-of-the-art climate models under different future socio-economic scenarios. Here we show storm frequency increases across northern and central Europe, where the meteorological storm severity index more than doubles. The population-weighted storm severity index more than triples, due to projected population increases. Adapting to the increasing wind speeds using future damage thresholds, the population weighted storm severity index increases are only partially offset, despite a reduction in the meteorological storm severity through adaptation. Through following lower emissions scenarios, the future increase in risk is reduced, with the population-weighted storm severity index increase more than halved.

What Impact? Communicating Severity Forecast Information through the Winter Storm Severity Index

Abstract Communicating the threat of severe winter weather is not simply a matter of the number of inches of snow or degrees of cold; it also considers the potential impacts of the storm. The winter storm severity index (WSSI) is a graphical product from the National Weather Service that presents anticipated impacts from forecast winter weather for a range of winter conditions. To assess the utility of the WSSI and how an impact-based winter weather forecast product is interpreted and used to inform decision-making, a mixed-methods social science study was conducted by the Nurture Nature Center in coordination with the Weather Prediction Center. Through focus groups and surveys, testing in the Hydrometeorological Testbed, and iterations on design and category descriptions, several themes emerged about how professional stakeholders understand, interpret, and use this product for communicating about impending winter weather. There is perceived utility in the WSSI for situational awareness and as part of a package of other information to inform decision-making. However, there is variability in interpretations of impacts, resulting from differences in geography, community readiness, and experience, among other factors, which creates complications in communicating the forecast. Furthermore, many users seek quantities related to winter weather, suggesting that education about what impact-based products include and what data are shown is necessary. Understanding the factors that influence perspectives on impact levels and the variable needs for winter weather information across regions improves forecasters’ abilities to effectively communicate and provide critical information that helps end users prepare for severe winter weather. Significance Statement Effectively communicating severe winter weather is critical to supporting communities in being prepared for and mitigating weather-related losses and damages. The winter storm severity index focuses on impacts to provide awareness of impending winter weather, information that is useful but not always interpreted consistently, requiring an understanding of factors influencing perspectives on impact levels and user education.

Projected increase in windstorm severity and contribution from sting jets over the UK and Ireland

Extratropical windstorms can lead to extreme impacts in the UK; however, future changes in windstorm frequency and intensity are uncertain due to natural variability and lack of model consensus. Further uncertainty arises due to unresolved or poorly represented processes which can reduce the validity of traditional coarse resolution climate models. This study demonstrates the added value of higher resolution simulations in their representation of extreme windstorms. It also assesses future changes in windstorms in the first ensemble of convection-permitting model (CPM, 2.2 km grid spacing) projections over the UK, released as part of the UKCP18 project, and compares these to changes in the driving 60 km global climate model (GCM) and 12 km regional climate model (RCM). The representation of wind gusts in the CPM and RCM is improved with respect to ERA-Interim reanalysis data compared to observations. Both models are largely similar except over areas with orographic influences where the CPM provides greater detail and simulates higher wind gusts. In contrast, the GCM simulates lower wind gusts and underestimates windstorm intensity; the improvements from the RCM and CPM are partially linked to better representation of cold conveyor belts and sting jets at km-scale. The CPM projects an increase in the frequency of extreme windstorms. In line with a previous study, a large contribution comes from storms that develop sting jets which now seems to be a robust response of windstorms to a warming climate. Similar projected increases are also found in the coarser resolution models, although differences in projections exist at a regional scale in the CPM compared to the RCM and GCM. Overall, higher resolution simulations improve the representation of windstorms, offering greater detail at the local scale which may provide better information for impact modelling, though large-scale projected changes in the assessed storm severity index are insensitive to the model resolutions in the simulations tested here.

An Overview of Extreme Storm Trends in Java Island using Storm Severity Index (SSI)

Extreme winds can lead to extreme storms, which are among the most frequent forms of extreme weather in Indonesia. With climate change predicted to increase the adverse impact of extreme weather, it is crucial to keep track of and gauge the severity of storm events. Storm Severity Index (SSI) can be used to measure the severity of storms, which is defined by their intensity, duration, and extent. SSI has been used in continental areas. However, the application of SSI in the maritime continent is still limited. This study explores the application of SSI to measure extreme storms in Indonesia based on wind gust speed. This study uses wind gust data in 1959-2021 from ERA5 reanalysis data and calculates the SSI based on the 98th percentile and the 50-year return value of the wind data. This study also explores the meteorological events that coincide with high SSI values. This study found the SSI able to capture high wind gust events in Java Island. This study finds high SSI value coincides with tropical cyclone events in the Indian Ocean. Topography affects the area on Java Island that has a high SSI value. Further study should evaluate the impact of the coastal and non-coastal areas to reduce the significant effect of sea-land breeze or mountain-valley breeze. In addition, future research also needs to address the significance level of the duration of events in a severe storm to enhance the information on severity.

Co-occurring wintertime flooding and extreme wind over Europe, from daily to seasonal timescales

The risk posed by heavy rain and strong wind is now suspected to be exacerbated by the way they co-occur, yet this remains insufficiently understood to effectively plan and mitigate. This study systematically investigates the correlations between wintertime (Oct–Mar) extremes relating to wind and flooding at all timescales from daily to seasonal. Meteorological reanalysis and river flow datasets are used to explore the historical period, and climate projections at 12 km resolution are analysed to understand the possible effects of future climate change (2061–2080, RCP 8.5). A new flood severity index (FSI) is also developed to complement the existing storm severity index (SSI). Initially, Great Britain (GB) is taken as a comparatively simple yet informative study area, then analysis is extended to the full European domain.Aggregated across GB, wind gusts and precipitation correlate strongly (rs∼0.6–0.8) at timescales from daily to seasonal, but peak around 10 days. A later peak is seen when considering correlations between wind gusts and river flows (40–60 days). This time is likely needed for catchments’ soils to saturate. A conceptual multi-temporal, multi-process model of GB wintertime flood-wind co-occurrence is proposed as a basis for future investigation. When historical analysis is extended across Europe we find the timescale of maximum correlation varies strongly between nations, likely as a result of different meteorological drivers.Impact focused correlation (FSI–SSI) is lower (rs∼0.2) but increases notably with climate change at timescales of ∼40 days (rs∼0.4). Tentatively, very severe episodes (i.e., both >99th percentile) appear heavily influenced by climate change, increasing roughly threefold by 2061–2080 (p < 0.05). The return period of such an event is 16 years historically (compared to 56 years if the two hazards were independent), reduces to 5 years in future. Such metrics provide actionable information for insurers and other stakeholders.

The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data

Abstract. Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one specific geographic region in a short period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high-resolution climate model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric (storm severity index, SSI) that is based on near-surface meteorological variables (10 m wind speed) and is a good proxy for losses. The SSI is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of a present-day ensemble of coupled climate model simulations from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim reanalysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20 % larger than the accumulated seasonal loss from a set of random resamples of the HiGEM data. Seasonal losses are increased by 10 %–20 % relative to randomized seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.

Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

Abstract. New insights into the synoptic-scale predictability of 25 severe European winter storms of the 1995–2015 period are obtained using the homogeneous ensemble reforecast dataset from the European Centre for Medium-Range Weather Forecasts. The predictability of the storms is assessed with different metrics including (a) the track and intensity to investigate the storms' dynamics and (b) the Storm Severity Index to estimate the impact of the associated wind gusts. The storms are well predicted by the whole ensemble up to 2–4 days ahead. At longer lead times, the number of members predicting the observed storms decreases and the ensemble average is not clearly defined for the track and intensity. The Extreme Forecast Index and Shift of Tails are therefore computed from the deviation of the ensemble from the model climate. Based on these indices, the model has some skill in forecasting the area covered by extreme wind gusts up to 10 days, which indicates a clear potential for early warnings. However, large variability is found between the individual storms. The poor predictability of outliers appears related to their physical characteristics such as explosive intensification or small size. Longer datasets with more cases would be needed to further substantiate these points.

Quantifying the extremity of windstorms for regions featuring infrequent events

AbstractThis paper introduces the Distribution‐Independent Storm Severity Index (DI‐SSI). The DI‐SSI represents an approach to quantify the severity of exceptional surface wind speeds of large scale windstorms that is complementary to the SSI introduced by Leckebusch et al. While the SSI approaches the extremeness of a storm from a meteorological and potential loss (impact) perspective, the DI‐SSI defines the severity in a more climatological perspective. The idea is to assign equal index values to wind speeds of the same singularity (e.g. the 99th percentile) under consideration of the shape of the tail of the local wind speed climatology. Especially in regions at the edge of the classical storm track, the DI‐SSI shows more equitable severity estimates, e.g. for the extra‐tropical cyclone Klaus. In order to compare the indices, their relation with the North Atlantic Oscillation is studied, which is one of the main large scale drivers for the intensity of European windstorms.

Storm climate on the Danube delta coast: evidence of recent storminess change and links with large-scale teleconnection patterns

This paper presents an overview of storminess along the Danube delta coast since 1949 by analysing wind and wave data and discusses the influences of teleconnections on climate variability. To this end, a five-category storm classification is proposed based on wind speed intensity and storm duration. On average, this coast experiences 30 storms/year occurring predominantly in winter, three of them considered severe (categories III–IV). The extreme storms (cat. V) endanger most the coastal settlements and the back-beach ecosystems (sand dunes, wetlands, lagoons) and have a mean recurrence rate of 7 years, but occur with a large inter-annual variability more frequent during the late 1960s, the 1970s and the 1990s. The prevalence of northern storms, in particular for the severe ones (>90% frequency for wind speeds >20 m/s) is responsible for the vigorous southward longshore sediment transport, which shaped the Danube delta physiognomy over the last millennia. The application of the newly developed energetic (Storm Severity Index—SSI) and morphologic (Storm Impact Potential—SIP) proxies allowed the better assessment of both the storm strength and the temporal variation in storm energy. It appears that storm climate follows a cyclic pattern with successive periods of 7–9 years of high, moderate and low storminess in accordance with the main teleconnections patterns (North Atlantic Oscillation—NAO, East Atlantic oscillation—EA, East Atlantic/Western Russia—EAWR, Scandinavian oscillation—SCAND). If NAO succeeded to explain best most of the storminess evolution (r = −0.76 for 1962–2005), it failed during the latest decade (since 2006) when an unprecedented low in storminess occurred. There is also evidence of increased southern circulation during the latter period, associated with a reversal of correlation with NAO (from negative to positive). Significant correlations were also found for the EA, EAWR and SCAND (r = −0.55, 0.56, 0.55, respectively, significant at p < 0.01) for all the study period suggesting that besides NAO, the north-western Black Sea coast storminess is considerably influenced by several modes of climate variability, most notable the EA and the EAWR, which succeed to address the recent decrease in storminess.

European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

Abstract. Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961–2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was downscaled to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, downscaled wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed. The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025% and 0.056% of the total insured value of the portfolio.

Development and application of an objective storm severity measure for the Northeast Atlantic region

An objective index for the estimation of storm severity has been developed, validated and applied to both reanalysis data and an AOGCM simulation for the Northeast Atlantic region. The index is based on the exceedance of local thresholds of the daily maximum wind speed. Positive trends for both the severity of storms during the historic ERA40 period (1960-2000), and under anthropogenic climate change (ACC) conditions (SRES A1B and A2) are identified. Additionally an increase in the spatial extent of storms is diagnosed, amounting up to about 10 % between present day and the scenario climate. Two types of the index are introduced: One area integrated measure for the investigation domain of 50°W to 20°E, 45°N to 70°N, called ASSI (Area Storm Severity Index), per day; and one event integrated index, called ESSI (Event Storm Severity Index), per storm event. ASSI and ESSI clearly identify extreme cyclone bombs under ACC conditions, which exceed the range found in observed (ERA40) and simulated data for the recent climate. ASSI and ESSI identify an increase in the severity of storms for the Northeast Atlantic and western Central Europe under ACC conditions. The index can easily be calculated for large data sets, and is thus well applicable to multi-model ensemble simulations in order to objectively estimate climate change signals and related measures of uncertainty. Reasons for the increase in severity could be seen mainly in the occurrence of higher wind speeds, and in larger areas affected by storms. These larger areas result from longer tracks combined with a common broadening along the path.

Developing a Storm Severity Index

A primary goal in winter highway maintenance is to develop various maintenance processes so that quality control can be measured. If actions can be measured, they can be improved. A difficulty with this approach is that winter maintenance addresses the impacts of winter weather on the transportation system and that weather is inherently uncontrollable. Consequently, for a quality process to be applied to winter maintenance, the severity of individual storms must be assessed. This paper presents one way in which the severity of a storm can be measured, specifically by an index. The first step in developing an index for individual storms is to develop a method of describing storms. The method here describes storms by using six factors, including prestorm and poststorm conditions and temperatures, wind speed, and precipitation type. The matrix created is a refinement of that presented in FHWA's manual of practice for effective anti-icing. With the use of a simplified variation of this matrix-based description of storms (more than 250 descriptions), a score is generated for each storm type. This score is then adjusted so that scores for all storms fall into a normal distribution between 0 and 1. This ranking of storms was evaluated by winter maintenance garage supervisors at the Iowa Department of Transportation. Supervisors were asked to rank 10 storms (presented as brief written descriptions) from easiest to hardest to handle. Results were compared with those of the initial storm severity index. From that comparison, numerical values for certain factors were adjusted so that storm severity index scores for these 10 storms agreed with rankings given by the garage supervisors.

Developing a Storm Severity Index

A primary goal in winter highway maintenance is to develop various maintenance processes so that quality control can be measured. If actions can be measured, they can be improved. A difficulty with this approach is that winter maintenance addresses the impacts of winter weather on the transportation system and that weather is inherently uncontrollable. Consequently, for a quality process to be applied to winter maintenance, the severity of individual storms must be assessed. This paper presents one way in which the severity of a storm can be measured, specifically by an index. The first step in developing an index for individual storms is to develop a method of describing storms. The method here describes storms by using six factors, including prestorm and poststorm conditions and temperatures, wind speed, and precipitation type. The matrix created is a refinement of that presented in FHWA's manual of practice for effective anti-icing. With the use of a simplified variation of this matrix-based descriptio...

Measuring Efficiency of Winter Maintenance Practices

The need to objectively evaluate the effect of new technology and the advent of externally contracted winter maintenance have increased the need for quantitative measures of winter maintenance efficiency and effectiveness. Varying severity of winter storms and lane kilometers within a maintenance facility’s service area together largely account for storm-to-storm differences in the cost of fighting snow. Efforts were made to develop a measure of winter maintenance efficiency that accounts for labor, equipment, and material costs, as well as storm severity and duration, for an established number of lane kilometers of given service levels. The expenditures are normalized by the lane kilometers in the maintenance facility’s service area and a storm severity index. The storm severity index (WI) is a modified winter index calculated from weighted daily snowfall totals and minimum and maximum temperatures. The resulting winter maintenance metric (daily snow-fighting $/WI × lane-km) is a normalized measure of a maintenance facility’s efficiency in performing snow- and ice-control operations. Databases and resulting winter maintenance efficiency metrics were developed for the winters 1996–1997, 1997–1998, and 1999–2000 for three Utah Department of Transportation maintenance facilities. The efficiency of these three sheds is compared for the winter of 1996–1997, and the winter maintenance efficiency is considered at one shed for specific storms during winter 1998–1999. The winter maintenance efficiency metric allows maintenance managers to objectively consider storm-to-storm efficiency within a facility and to compare the storm-to-storm and annual efficiency of facilities. The impact of new technology on winter maintenance and the efficiency with which contract organizations perform snow-fighting duties can be measured with a winter maintenance metric.