Winter Storms Research Articles

Can Machine Learning Models Be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Time Scales?

Abstract Skillful weather prediction on subseasonal to seasonal time scales is crucial for many socioeconomic ventures. But forecasting, especially extremes, on these time scales is very challenging because the information from initial conditions is gradually lost. Therefore, data-driven methods are discussed as an alternative to numerical weather prediction models. Here, quantile regression forests (QRFs) and random forest classifiers (RFCs) are used for probabilistic forecasting of central European mean wintertime 2-m temperatures and cold wave days at lead times of 14, 21, and 28 days. ERA5 reanalysis meteorological predictors are used as input data for the machine learning models. For the winters of 2000/01–2019/20, the predictions are compared with a climatological ensemble obtained from E-OBS observational data. The evaluation is performed as full distribution predictions for continuous values using the continuous ranked probability skill score and as binary categorical forecasts using the Brier skill score. We find skill at lead times up to 28 days in the 20-winter mean and for individual winters. Case studies show that all used machine learning models are able to learn patterns in the data beyond climatology. A more detailed analysis using Shapley additive explanations suggests that both random forest (RF)-based models are able to learn physically known relationships in the data. This underlines that RF-based data-driven models can be a suitable tool for forecasting central European mean wintertime 2-m temperatures and the occurrence of cold wave days. Significance Statement Because of the chaotic nature of weather, it is very complicated to make predictions with traditional numerical methods 2–4 weeks in advance. Therefore, we use alternative, interpretable methods that “learn” to find statistically relevant patterns in meteorological data that can be used for forecasting central European mean surface wintertime temperatures and cold wave days. These methods are part of the so-called machine learning methods that do not rely on the traditional numerical equations anymore. We test our methods for 20 winters between 2000/01 and 2019/20 against a static weather prediction consisting of the past 30 winters. For single winters and in a mean over the 20 predicted winters, we find improved predictions up to 4 weeks in advance.

The weather determines the number of cases of tick paralysis in dogs and cats in eastern Australia, caused by Ixodes holocyclus, the eastern paralysis tick.

We studied over 222,000 cases of emergency veterinary consultations in four regions along the eastern coast of Australia. We found that cases of tick paralysis (TP) caused by the eastern paralysis tick, Ixodes holocyclus, accounted for 7.5% of these cases: >16,000 cases. The season of TP and the number (prevalence) of TP cases varied among regions and over the years. Our study of the association between weather and (i) the start of the season of TP, and (ii) the number of TP cases revealed much about the intricate relationship between the weather and I. holocyclus. We studied the effect of the hypothetical availability of isoxazoline-containing tick-preventative medicines and found that an increase in the availability of these medicines had significantly contributed to the decrease in TP cases. We found that the weather in winter accounted for the time of the year the season of TP starts whereas the weather in summer accounted for the number of TP cases in the TP season. Last, through a study of the effects of shifts in the climate under four hypothetical scenarios (warmer/cooler and drier/wetter than average), we propose that the start of the season of TP depends on how soon the weather in winter becomes suitable for the activity (e.g. host-seeking) and the development of I. holocyclus nymphs, and that the number of TP cases during the TP season depends on how many engorged female ticks and their eggs survive during summer.

Statistical evaluation of different surface precipitation-type algorithms and its implications for NWP prediction and operational decision making

Abstract Several new precipitation-type algorithms have been developed to improve NWP predictions of surface precipitation type during winter storms. In this study, we evaluate whether it is possible to objectively declare one algorithm as superior to another through comparison of three precipitation-type algorithms when validated using different techniques. The apparent skill of the algorithms is dependent on the choice of performance metric – algorithms can have high scores for some metrics and poor scores for others. It is also possible for an algorithm to have high skill at diagnosing some precipitation types and poor skill with others. Algorithm skill is also highly dependent on the choice of verification data/methodology. Just by changing what data is considered “truth,” we were able to substantially change the apparent skill of all algorithms evaluated herein. These findings suggest an objective declaration of algorithm “goodness” is not possible. Moreover, they indicate the unambiguous declaration of superiority is difficult, if not impossible. A contributing factor to algorithm performance is uncertainty of the microphysical processes that lead to phase changes of falling hydrometeors, which are treated differently by each algorithm thus resulting in different biases in near-0°C environments. These biases are evident even when algorithms are applied to ensemble forecasts. Hence, a multi-algorithm approach is advocated to account for this source of uncertainty. Though the apparent performance of this approach is still dependent on the choice of performance metric and precipitation type, a case-study analysis shows it has the potential to provide better decision support than the single-algorithm approach.

Impact of Severe Weather on Operations of a Radiation Oncology Department

In 2022, two major storms in Western New York brought blizzard conditions and historic snowfall amounts to the region. Beyond the immediate impact to life and property, these weather events resulted in disruptions to cancer care as a result of hazardous travel conditions, and facility closures as a result of regional travel bans. We sought to understand the impact of these disruptive incidents on departmental operations to develop better contingency plans for future occurrences. The radiation treatment machine schedules for a large academic center in Western New York, as well as a neighboring affiliated satellite clinic were queried for daily treatment adherence rates from November 1, 2022 through February 4, 2023. We classified "severe weather days" based on travel advisories issued by the National Weather Service or from local county officials. Out of a total of 68 working days during the study period, 15 days were classified as having severe weather. We compared the percentage of scheduled patients treated (adherence rates) on severe weather days compared with days without severe weather. Statistical analysis was performed using a two-tailed Z-test for proportions. Under routine operating conditions, out of a total of 7045 scheduled treatment visits, we found a mean daily treatment adherence rate of 93.57%. On severe weather days, out of a total of 2093 scheduled treatments, we found a mean daily treatment adherence rate of 77.83%. Out of these 15 days, the satellite clinic closed for a total of 3 days, while the academic center closed for 1 day. Excluding the dates of facility closure, overall treatment adherence rate was 84.76% on severe weather days (Risk Ratio: 0.91). We found that the difference in treatment adherence rates between regular and severe weather days was statistically significant (p<0.001), even when excluding dates of facility closure. This study demonstrates a significant reduction in treatment adherence rates around severe winter storms, even when excluding dates of facility closure. We suggest that preparation for such incidents be a part of facility contingency planning. Weekend openings can be considered for closures ≥ 2 days, or sustained adherence rates of 85% or less for 3 or more days.

No detectable trend in mid-latitude cold extremes during the recent period of Arctic amplification

It is widely accepted that Arctic amplification—accelerated Arctic warming—will increasingly moderate cold air outbreaks to the mid-latitudes. Yet, an increasing number of recent studies also argue that Arctic amplification can contribute to more severe winter weather. Here we show that the temperature of cold extremes across the United States east of the Rockies, Northeast Asia and Europe have remained nearly constant over recent decades, in clear contrast to a robust Arctic warming trend. Analysis of trends in the frequency and magnitude of cold extremes is mixed across the US and Asia but with a clearer decreasing trend in occurrence across Europe, especially Southern Europe. This divergence between robust Arctic warming and no detectable trends in mid-latitude cold extremes highlights the need for a better understanding of the physical links between Arctic amplification and mid-latitude cold extremes.

A 1500‐year record of North Atlantic storm flooding from lacustrine sediments, Shetland Islands (UK)

ABSTRACTSevere storm flooding poses a major hazard to the coasts of north‐western Europe. However, the long‐term recurrence patterns of extreme coastal flooding and their governing factors are poorly understood. Therefore, high‐resolution sedimentary records of past North Atlantic storm flooding are required. This multi‐proxy study reconstructs storm‐induced overwash processes from coastal lake sediments on the Shetland Islands using grain‐size and geochemical data, and the re‐analysis of historical data. The chronostratigraphy is based on Bayesian age–depth modelling using accelerator mass spectrometry 14C and 137Cs data. A high XRF‐based Si/Ti ratio and the unimodal grain‐size distribution link the sand layers to the beach and thus storm‐induced overwash events. Periods with more frequent storm flooding occurred 980–1050, 1150–1300, 1450–1550, 1820–1900 and 1950–2000 ce, which is largely consistent with a positive North Atlantic Oscillation mode. The Little Ice Age (1400–1850 ce) shows a gap of major sand layers suggesting a southward shift of storm tracks and a seasonal variance with more storm floods in spring and autumn. Warmer phases shifted winter storm tracks towards the north‐east Atlantic, indicating a possible trend for future storm‐track changes and increased storm flooding in the northern North Sea region.

Characterizing Winter Weather Patterns and Pollution Sources of PM2.5 in Taipei City

Characterizing Winter Weather Patterns and Pollution Sources of PM2.5 in Taipei City

The February 2021 Winter Storm and its impact on Texas infrastructure: Lessons for communities, emergency managers, and first responders

AbstractThis article explores why Texas infrastructure failed during the February 2021 winter storm and discusses the rippling effects it had on governments, emergency managers, first responders, and the public. A qualitative approach was used for this study and was based primarily on interviews that were conducted with 29 individuals to understand the nature of the disaster and discover subsequent actions that took place. Research also incorporated news articles that discussed firsthand accounts taken from members of the public along with government documents that evaluated the impact and response to the disaster. The research discusses ERCOT's efforts to keep infrastructure operational for the public and reveals that emergency managers and first responders were left with insufficient information and resources while Texas was under a state of emergency. The article conveys what was done at the local, state, and federal levels and it also highlights successes and failures of the response. The article offers insight on lessons learned about infrastructure and provides recommendations to mitigate and prepare for complex disaster situations that are likely occur in the future.

Cyclist’s travel distances and risk of falls in snowy and icy conditions in German cities

Introduction: So far, no studies are known that estimate distance-based risks for cyclist falls in snowy/icy conditions compared to other conditions to account for differences in cycling levels in the different weather situations. Method: The number of cyclist falls was gathered from retrospective surveys in Germany. Cycling distances were obtained from the German Household Travel Survey “Mobility in cities – SrV,” assigned to meteorological data, and validated against counts and own surveys. The number of falls per distance cycled and Risk Ratios for snowy/icy versus other weather conditions were estimated. Results: An average decrease of 53% in the distance travelled per person and day is estimated for snowy/icy days versus other days. This decrease is lower in regions with higher general cycling mode shares. We find average risks of falls from 9.5 to 16 (field surveys) up to 76.5 falls per 10,000 km (online survey) and average Risk Ratios for cycling in snowy/icy conditions of 20 (field survey conducted in times of other weather) to 36 (field survey conducted in times of snow/ice) and 38 (online survey conducted in times of snow/ice). The risk of suffering an injury in the event of a fall is lower in snowy/icy compared to other weather conditions. Conclusions: Seeing the current trend of growing general cycling levels in Germany, we expect more cycling in winter and, in case of unchanged winter weather and maintenance, a substantial increase of cyclist falls. The reduced risk of being injured in the event of a fall in snowy/icy conditions does not outweigh the higher risk of falling in the first place. Practical Applications: Improved winter maintenance on cycling facilities can help increase winter cycling and reduce the risk of falls at the same time.

Seasonal prediction of UK mean and extreme winds

AbstractRecent studies have shown that seasonal forecasting systems have significant skill in predicting northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3‐month outlook for the United Kingdom (UK), we have investigated the skill in predicting seasonal (3‐month) mean UK wind speed and storms (extreme winds) with a one‐month lead‐time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December–February), related to predictability of the North Atlantic Oscillation. In contrast, summer (June–August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved.

A generalized framework for designing open-source natural hazard parametric insurance

Parametric insurance schemes allow for payouts to be triggered by real-time hydro-/meteorological parameters instead of waiting for damage assessments, which means they can be settled swiftly, giving people access to funds right after the event. In this work we propose a framework to design parametric insurance schemes and systematically quantify the basis risk: the difference between the parameter-based payout and the actual damage. We implement the framework in the open-source global risk assessment platform CLIMADA and illustrate it with two stylized parametric insurance case studies, targeting tropical cyclones in Mozambique and winter storms in France. The data used and the provided code base are globally-consistent, open-source, and readily available. The presented methods are therefore applicable in data-scarce areas and accessible to stakeholders from the public and private sector. Moreover, our approach can easily be adapted to other hazards and exposures worldwide. This improves the accessibility and transparency of such innovative insurance schemes.

An Iterative Approach toward Development of Ensemble Visualization Techniques for High-Impact Winter Weather Hazards: Part I: Product Development

Abstract We applied social science research principles to develop a suite of probabilistic winter weather forecasting visualizations for High-Resolution Ensemble Forecast (HREF) system output. This was achieved through an iterative, dialogic process with U.S. National Weather Service (NWS) forecasters to design nine new web-based, interactive products aimed toward improving visualizations of winter weather event magnitudes, characteristics, and timing. These products were influenced by feedback from a preliminary focus group, which emphasized the importance of product credibility, contextualization, and scalability. In a follow-up discussion, winter weather forecasting experts found the event timing products to have the greatest utility due to their association with impact-decision support services (IDSS). Furthermore, forecasters assessed snowfall rates as the most impactful variable rather than snowfall totals and radar reflectivity. The timing products include plots of probabilistic snowfall onset time and duration, rush hour intersection probabilities, and a combination meteogram. The onset and duration plots visualize the ensemble-average onset time and duration of a specified snowfall rate, as demonstrated in previous works, but with the addition of uncertainty information by visualizing the earliest, most likely, and latest potential onset times as well as the shortest, most likely, and longest potential durations. The rush hour product visualizes the probability of exceeding a specified snowfall rate during local commutes, and the combination meteogram allows rapid identification of high-impact periods by encoding probabilities of precipitation, precipitation-type probabilities, and average rates into one graphical tool. Examples of these interactive products are maintained on our companion website: www.visweather.com/bams2023.

COMPOUND URBAN COASTAL FLOOD MODELING: INTEGRATING TIDE, WAVES, PRECIPITATION AND HYDRAULIC INFRASTRUCTURE

Compound coastal flooding considers the joint impacts of marine and hydrologic interactions and has recently been identified as an international research priority. Along the US West Coast, winter storms often bring high marine water levels along with energetic waves and precipitation. Hydrodynamic models have been widely used to estimate flood impacts, including extreme estuarine water levels in compound events (e.g., riverine discharge-tide-storm surge interactions), and produced satisfactory results. However, few studies further consider overland flow routing and high-resolution flood mapping in highly urbanized, low-lying coastal areas. Here, an integrated Delft3D-FM based numerical modeling framework is used to explicitly resolve multi-pathway flood processes (i.e., high marine water levels, waves overtopping, precipitation) and infrastructure (e.g., seawalls, storm drains, artificial dunes).

Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 m2. Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.

Winter Precipitation Type from Microwave Radiometers in New York State Mesonet Profiler Network

Abstract Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts. Significance Statement Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.

Weathering the storm: Decreased activity and glucocorticoid levels in response to inclement weather in breeding Columbian ground squirrels

Inclement weather can rapidly modify the thermal conditions experienced by animals, inducing changes in their behavior, body condition, and stress physiology, and affecting their survival and breeding success. For animals living in variable environments, the extent to which they have adapted to cope with inclement weather is not established, especially for hibernating species with a short active season that are constrained temporally to breed and store energy for subsequent hibernation. We examined behavioral (foraging activity) and physiological (body mass and fecal cortisol metabolites) responses of Columbian ground squirrels (Urocitellus columbianus), small hibernating rodents inhabiting open meadows in Rocky Mountains, to 3 events of inclement weather (two snow storms in May 2021 and May 2022, one heavy rainfall in June 2022). We found that individuals adapted to inclement weather conditions by (1) reducing above-ground activity, including foraging, (2) decreasing the mobilization of stored resources as indicated by a decrease in the activity of the hypothalamo-pituitary-adrenal (HPA) axis and lower fecal cortisol metabolites in the hours/days following periods of inclement weather; and (3) compensating through increased foraging and more local activity when favorable conditions resumed. As a result, body mass and growth did not decrease following short periods of inclement weather. Columbian ground squirrels were well-adapted to short periods of inclement weather, coping via modifications of their behavior and the activity of the HPA axis.

Forecasting next-hour electricity demand in small-scale territories: Evidence from Jordan

In exceptional times of wars, natural crises (e.g., snow storms), or hosting massive events (e.g., international sports events), prior knowledge of hour-by-hour electricity demand might become critical for the concerned areas. Anticipating the next-hour demand within a bounded location might help cope with challenging situations. In this paper, we are concerned with the problem of electricity demand forecasting for the next hour within a small area in the context of the country of Jordan relying exclusively on historical electricity consumption. Existing electricity demand forecasting models predict electricity demand for the next day for the whole country of Jordan or for large cities using numerous indicators such as weather conditions and season-related features, together with historical electricity consumption. Our proposed solution consists of preprocessing the hourly-electricity-consumption data to rearrange it into time series of 25-hour length. Then, we applied supervised machine learning algorithms to build predictive models for electricity demand. Our methods proved effective to a certain degree in forecasting electricity demand using time series of historical electricity consumptions only. We employ several predictive machine learning models: neural network, decision tree, random forest, AdaBoost, support vector regression, nearest neighbors, linear regression, bayesian ridge, partial least squares, gradient boosting, and principal component regression. We found that the most critical indicators in our predictive models are the electricity consumption of the two hours preceding the target hour and the electricity consumption the day before in the hour (the same as the target hour) and the next hour. Our predictive models can efficiently forecast electricity demand in a small rural area in Jordan with an accuracy of nearly 97% at best and 92% at least. Our predictive models are constructed using historical data that includes the electricity consumption of a small Jordanian district for the years 2019–2021.

An Investigation of a Northeast U.S. Cyclone Event without Well-Defined Snow Banding during IMPACTS

Abstract On 7 February 2020 a relatively deep cyclone (∼980 hPa) with midlevel frontogenesis produced heavy snow (20–30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud, while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) Model simulations at 2- and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24-h runs realistically produced a broad sloping region of frontogenesis at midlevels typically; however, there were relatively large (20%–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (&lt;10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20%–30% have too little ice aloft and at low levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5%–10% more precipitation than the 2-km grid. Significance Statement Heavy snowfall from U.S. East Coast winter storms can cause major societal problems, yet few studies have investigated these storms using field observations and model data. This study focuses on the 7 February 2020 event, where 20–40 cm of snow fell over west-central New York. Our analysis shows a broad region of ascent, rather than a concentrated region favoring a well-defined snowband was the primary process contributing to snowfall. Last, model microphysics were validated within this storm using the in situ aircraft data. Errors in the snow generation aloft and snow growth at low levels likely contributed to the simulated surface precipitation underprediction, but most of the forecast uncertainty is from initial conditions for this short-term (∼24-h lead time) forecast.

EXPERIMENTAL AND NUMERICAL STUDY ON OBLIQUE WAVE-IN-DECK LOADS

Most studies on wave-in-deck loads focus on head-on wave impingement whereas wave-in-deck loads due to waves incident from oblique directions are rarely reported. Numerical simulations for oblique wave-in-deck loads were presented in Iwanowski et al. (2002), Brodtkorb (2008) and Chen et al. (2018). However, in these studies, only numerical validations were performed against either two-dimensional experiments or empirical formulations due to a lack of experimental results. In light of this, we present the experimental results for wave impacts on a solid deck model due to a transient focused wave group incident from oblique direction in this study. The oblique experimental data is compared with the head-on counterpart by Santo et al, (2020) to investigate the incident wave angle effect on wave-in-deck loads. Numerical simulations are carried out to reproduce both the head-on and oblique wave-in-deck experiments using a three-dimensional (3D) numerical wave tank (NWT), and flow field information is interrogated to derive physical insights into complex wave-deck interactions. The solid deck model is a representative of a typical 2nd generation North Sea topside structures commonly exposed to severe winter weather. The configuration is also applicable to coastal structures such as bridges, piers, jetties or docks.

An Iterative Approach toward Development of Ensemble Visualization Techniques for High-Impact Winter Weather Hazards: Part II: Product Evaluation

Abstract We developed five prototype convection-allowing model ensemble visualization products with the goal of improving depictions of the timing of winter weather hazards. These products are interactive, web-based plots visualizing probabilistic onset times and durations of intense snowfall rates, probabilities of heavy snow at rush hour, periods of heightened impacts, and mesoscale snowband probabilities. Prototypes were evaluated in three experimental groups coordinated by the Weather Prediction Center (WPC) Hydrometeorological Testbed (HMT), with a total of 53 National Weather Service (NWS) forecasters. Forecasters were asked to complete a simple forecast exercise for a snowfall event, with a control group using the Storm Prediction Center’s (SPC) High-Resolution Ensemble Forecast (HREF) system viewer, and an experimental group using both the HREF viewer and the five experimental graphics. Forecast accuracy was similar between the groups, but the experimental group exhibited smaller mean absolute error for snowfall duration forecasts. A series of Likert-scale questions saw participants respond favorably to all of the products and indicated that they would use them in operational forecasts and in communicating information to core partners. Forecasters also felt that the new products improved their comprehension of ensemble spread and reduced the time required to complete the forecasting exercise. Follow-up plenary discussions reiterated that there is a high demand for ensemble products of this type, though a number of potential improvements, such as greater customizability, were suggested. Ultimately, we demonstrated that social science methods can be effectively employed in the atmospheric sciences to yield improved visualization products.