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Abstract Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.

Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.

Abstract A prognostic equation for the liquid fraction of mixed-phase particles has been recently added to the Predicted Particle Properties (P3) bulk microphysics scheme. Mixed-phase particles are necessary to simulate key microphysical processes leading to various winter precipitation types, such as ice pellets and freezing rain. To illustrate the impacts of predicting the bulk liquid fraction, the 1998 North American Ice Storm is simulated using the Weather Research and Forecasting (WRF) Model with the modified P3 scheme. It is found that simulating partial melting by predicting the bulk liquid fraction produces higher mass and number mixing ratios of rain. This leads to smaller rain sizes reaching the refreezing layer as well as a decrease in the freezing rain accumulation at the surface by up to 30% in some locations compared to when no liquid fraction is predicted. The increase in fall speed and density and decrease of particle diameter during partial melting combined with an improved representation of the refreezing process in the modified P3 leads to generally higher total solid surface precipitation rates than using the original P3 scheme. There is also an increase of solid precipitation in regions of ice pellet accumulation. Overall, the simulation of mixed-phase particles notably impacts the vertical and spatial distributions of precipitation properties.

Abstract Bulk microphysics parameterizations that are used to represent clouds and precipitation usually allow only solid and liquid hydrometeors. Predicting the bulk liquid fraction on ice allows an explicit representation of mixed-phase particles and various precipitation types, such as wet snow and ice pellets. In this paper, an approach for the representation of the bulk liquid fraction into the predicted particle properties (P3) microphysics scheme is proposed and described. Solid-phase microphysical processes, such as melting and sublimation, have been modified to account for the liquid component. New processes, such as refreezing and condensation of the liquid portion of mixed-phase particles, have been added to the parameterization. Idealized simulations using a one-dimensional framework illustrate the overall behavior of the modified scheme. The proposed approach compares well to a Lagrangian benchmark model. Temperatures required for populations of ice crystals to melt completely also agree well with previous studies. The new processes of refreezing and condensation impact both the surface precipitation type and feedback between the temperature and the phase changes. Overall, prediction of the bulk liquid fraction allows an explicit description of new precipitation types, such as wet snow and ice pellets, and improves the representation of hydrometeor properties when the temperature is near 0°C.

Abstract The aerodynamic response of snow gauges when exposed to the wind is responsible for a significant reduction of their collection performance. The modifications induced by the gauge and the windshield onto the space–time patterns of the undisturbed airflow deviate the snowflake trajectories. In Part I, the disturbed air velocity field in the vicinity of shielded and unshielded gauge configurations is investigated. In Part II, the airflow is the basis for a particle tracking model of snowflake trajectories to estimate the collection efficiency. A Geonor T-200B gauge inside a single Alter shield is simulated for wind speeds varying from 1 to 8 m s−1. Both time-averaged and time-dependent computational fluid dynamics simulations are performed, based on Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) models, respectively. A shear stress tensor k–Ω model (where k is the turbulent kinetic energy and Ω is the turbulent specific dissipation rate) is used for the RANS formulation and solved within a finite-volume method. The LES is implemented with a Smagorinsky subgrid-scale method that models the subgrid stresses as a gradient-diffusion process. The RANS simulations confirm the attenuation of the airflow velocity above the gauge when using a single Alter shield, but the generated turbulence above the orifice rim is underestimated. The intensity and spatial extension of the LES-resolved turbulent region show a dependency on the wind speed that was not detected by the RANS. The time-dependent analysis showed the propagation of turbulent structures and the impact on the turbulent kinetic energy above the gauge collecting section.

Abstract The use of windshields to reduce the impact of wind on snow measurements is common. This paper investigates the catching performance of shielded and unshielded gauges using numerical simulations. In Part II, the role of the windshield and gauge aerodynamics, as well as the varying flow field due to the turbulence generated by the shield–gauge configuration, in reducing the catch efficiency is investigated. This builds on the computational fluid dynamics results obtained in Part I, where the airflow patterns in the proximity of an unshielded and single Alter shielded Geonor T-200B gauge are obtained using both time-independent [Reynolds-averaged Navier–Stokes (RANS)] and time-dependent [large-eddy simulation (LES)] approaches. A Lagrangian trajectory model is used to track different types of snowflakes (wet and dry snow) and to assess the variation of the resulting gauge catching performance with the wind speed. The collection efficiency obtained with the LES approach is generally lower than the one obtained with the RANS approach. This is because of the impact of the LES-resolved turbulence above the gauge orifice rim. The comparison between the collection efficiency values obtained in case of shielded and unshielded gauge validates the choice of installing a single Alter shield in a windy environment. However, time-dependent simulations show that the propagating turbulent structures produced by the aerodynamic response of the upwind single Alter blades have an impact on the collection efficiency. Comparison with field observations provides the validation background for the model results.

Abstract This study aims to characterize the shapes and fall speeds of ice pellets formed in various atmospheric conditions and to investigate the possibility to use a laser-optical disdrometer to distinguish between ice pellets and other types of precipitation. To do so, four ice pellet events were documented using manual observations, macrophotography, and laser-optical disdrometer data. First, various ice pellet fall speeds and shapes, including spherical, bulged, fractured, and irregular particles, were associated with distinct atmospheric conditions. A higher fraction of bulged and fractured ice pellets was observed when solid precipitation was completely melted aloft while more irregular particles were observed during partial melting. These characteristics affected the diameter–fall speed relations measured. Second, the measurements of particles’ fall speed and diameter show that ice pellets could be differentiated from rain or freezing rain. Ice pellets larger than 1.5 mm tend to fall > 0.5 m s −1 slower than raindrops of the same size. In addition, the fall speed of a small fraction of ice pellets was < 2 m s −1 regardless of their size, as compared with a fall speed > 3 m s −1 for ice pellets with diameter > 1.5 mm. Video analysis suggests that these slower particles could be ice pellets passing through the laser-optical disdrometer after colliding with the head of the instrument. Overall, these findings contribute to a better understanding of the microphysics of ice pellets and their measurement using a laser-optical disdrometer. Significance Statement Ice pellets are challenging to forecast and to detect automatically. In this study, we documented the fall speed and physical characteristics of ice pellets during various atmospheric conditions using a combination of a laser-optical disdrometer, manual observations, and macrophotography images. Relationships were found between the shape and fall speed of ice pellets. These findings could be used to refine the parameterization of ice pellets in atmospheric models and, consequently, improve the forecast of impactful winter precipitation types such as freezing rain. Furthermore, they will also help to physically interpret laser-optical disdrometer data during ice pellets and freezing rain.

This dataset contains the observation data used to prepare the thesis of Mathieu Lachapelle. It contains radar data, laser-optical disdrometer data, standard meteorological data, manual observations, and macrophotography recorded during four ice pellet events that occurred in 2019 and 2020. The ice pellet episodes occurred in the Montreal region and most observational data were collected at UQAM-PK weather station, on the rooftop of President Kennedy building, in Downtown Montreal. More documentation is available in the READMEs provided with the dataset. Cette base de données contient les données d'observation utilisées pour rédiger la thèse de Mathieu Lachapelle. Elle inclut des données radar, des données d'un disdromètre optique, des mesures météorologiques de base, des observations manuelles et des macro photographies collectées pendant quatre épisodes de grésil qui se sont produit en 2019 et en 2020. Les épisodes de grésil ont eu lieu dans la région de Montréal et la plupart des données d'observation ont été collectées à la station météo UQAM-PK, installée sur le toit du bâtiment Président-Kennedy au centre-ville de Montréal. Davantage de documentation est accessible via les fichiers READMEs inclus dans la base de données.

Solid precipitation falling near 0 °C, mainly snow, can adhere to surface features and produce major impacts. This study is concerned with characterizing this precipitation over the Canadian Prairie provinces of Manitoba and Saskatchewan in the current (2000–2013) and pseudo-global warming future climate, with an average 5.9 °C temperature increase, through the use of high resolution (4 km) model simulations. On average, simulations in the current climate suggest that this precipitation occurs within 11 events per year, lasting 33.6 h in total and producing 27.5 mm melted equivalent, but there are wide spatial variations that are partly due to enhancements arising from its relatively low terrain. Within the warmer climate, average values generally increase, and spatial patterns shift somewhat. This precipitation consists of four categories covering its occurrence just below and just above a wet-bulb temperature of 0 °C, and with or without liquid precipitation. It generally peaks in March or April, as well as in October, and these peaks move towards mid-winter by approximately one month within the warmer climate. Storms producing this precipitation generally produce winds with a northerly component during or shortly after the precipitation; these winds contribute to further damage. Overall, this study has determined the features of and expected changes to adhering precipitation across this region.

Abstract Adjustments for the wind-induced undercatch of snowfall measurements use transfer functions to account for the expected reduction of the collection efficiency with increasing the wind speed for a particular catching-type gauge. Based on field experiments or numerical simulation, collection efficiency curves as a function of wind speed also involve further explanatory variables such as surface air temperature and/or precipitation type. However, while the wind speed or wind speed and temperature approach is generally effective at reducing the measurement bias, it does not significantly reduce the root-mean-square error (RMSE) of the residuals, implying that part of the variance is still unexplained. In this study, we show that using precipitation intensity as the explanatory variable significantly reduces the scatter of the residuals. This is achieved by optimized curve fitting of field measurements from the Marshall Field Site (Colorado, United States), using a nongradient optimization algorithm to ensure optimal binning of experimental data. The analysis of a recent quality-controlled dataset from the Solid Precipitation Intercomparison Experiment (SPICE) campaign of the World Meteorological Organization confirms the scatter reduction, showing that this approach is suitable to a variety of locations and catching-type gauges. Using computational fluid dynamics simulations, we demonstrate that the physical basis of the reduction in RMSE is the correlation of precipitation intensity with the particle size distribution. Overall, these findings could be relevant in operational conditions since the proposed adjustment of precipitation measurements only requires wind sensor and precipitation gauge data.

Abstract This study investigates the seasonality of near‐surface wind speeds associated with extratropical cyclones (ETCs) over northeastern North America using a global reanalysis data set during 1979–2020. As opposed to most studies that emphasize winter storms, ETCs during the fall exhibit significantly stronger 10‐m winds over this region due to the slightly stronger continental cyclones and significantly weaker low‐level stability during that time of the year. Also, ETCs favor inland lakes and Hudson Bay during the low‐ice‐content fall season, leading to lower surface roughness. Combining these results, we derive simple linear regressions to predict the 10‐m wind speed given three variables: 850‐hPa wind speed, low‐level Richardson number, and surface roughness length. This formula captures the observed seasonality and serves as a valuable tool for cyclone near‐surface wind risk assessment. , Plain Language Summary Extratropical cyclones can bring powerful winds that can cause severe damage to infrastructure. We find that cyclones with severe winds are the most frequent in the fall season over continental northeastern North America. Three reasons are found responsible: stronger continental cyclones, weaker low‐level atmospheric stability, and the lower surface roughness over lakes and Hudson Bay, where cyclones frequently occur in fall. A simple formula that can effectively assess the near‐surface wind speeds associated with cyclones is derived based on these results. , Key Points Extratropical‐cyclone‐associated 10‐m wind speeds are the strongest in the fall season over northeastern North America Besides stronger continental cyclones and 850‐hPa winds, weaker low‐level stability in fall favors stronger 10‐m wind speeds in this region Linear regression using 850‐hPa wind, Richardson number, and surface roughness well predicts cyclones' 10‐m wind speeds and seasonality

Abstract. Precipitation events that bring rain and snow to the Banff–Calgary area of Alberta are a critical aspect of the region's water cycle and can lead to major flooding events such as the June 2013 event that was the second most costly natural disaster in Canadian history. Because no special atmospheric-oriented observations of these events have been made, a field experiment was conducted in March and April 2015 in Kananaskis, Alberta, to begin to fill this gap. The goal was to characterize and better understand the formation of the precipitation at the surface during spring 2015 at a specific location in the Kananaskis Valley. Within the experiment, detailed measurements of precipitation and weather conditions were obtained, a vertically pointing Doppler radar was deployed and weather balloons were released. Although 17 precipitation events occurred, this period was associated with much less precipitation than normal (−35 %) and above-normal temperatures (2.5 ∘C). Of the 133 h of observed precipitation, solid precipitation occurred 71 % of the time, mixed precipitation occurred 9 % and rain occurred 20 %. An analysis of 17 504 precipitation particles from 1181 images showed that a wide variety of crystals and aggregates occurred and approximately 63 % showed signs of riming. This was largely independent of whether flows aloft were upslope (easterly) or downslope (westerly). In the often sub-saturated surface conditions, hydrometeors containing ice occurred at temperatures as high as 9 ∘C. Radar structures aloft were highly variable with reflectivity sometimes >30 dBZe and Doppler velocity up to −1 m s−1, which indicates upward motion of particles within ascending air masses. Precipitation was formed in this region within cloud fields sometimes having variable structures and within which supercooled water at least sometimes existed to produce accreted particles massive enough to reach the surface through the relatively dry sub-cloud region.

This dataset contains output of yearly frequencies (hours) of freezing rain identified using four precipitation-type algorithms applied to output of the fifth-generation Canadian Regional Climate Model (CRCM5) run at Ouranos. Algorithms are applied to three-hourly output of eight simulations of four dynamically-downscaled global climate models (GCMs) on a 0.22° horizontal grid over the North American domain. Simulations for 1980-2005 are forced with observed greenhouse gas concentrations, with data for 2006-2099 using the RCP 8.5 greenhouse gas concentration trajectory. Each occurrence of freezing rain identified in the model output is multiplied by 3 for comparison with hourly observations. These data are associated with the article "A multi-algorithm analysis of projected changes to freezing rain over North America in an ensemble of regional climate model simulations" by McCray et al., submitted in 2022 to the Journal of Geophysical Research: Atmospheres.

Abstract The Canadian Precipitation Analysis System (CaPA) is an operational system that uses a combination of weather gauge and ground-based radar measurements together with short-term forecasts from a numerical weather model to provide near-real-time estimates of 6- and 24-h precipitation amounts. During the winter season, many gauge measurements are rejected by the CaPA quality control process because of the wind-induced undercatch for solid precipitation. The goal of this study is to improve the precipitation estimates over central Canada during the winter seasons from 2019 to 2022. Two approaches were tested. First, the quality control procedure in CaPA has been relaxed to increase the number of surface observations assimilated. Second, the automatic solid precipitation measurements were adjusted using a universal transfer function to compensate for the undercatch problem. Although increasing the wind speed threshold resulted in lower amounts and worse biases in frequency, the overall precipitation estimates are improved as the equitable threat score is improved because of a substantial decrease in the false alarm ratio, which compensates the degradation of the probability of detection. The increase of solid precipitation amounts using a transfer function improves the biases in both frequency and amounts and the probability of detection for all precipitation thresholds. However, the false alarm ratio deteriorates for large thresholds. The statistics vary from year to year, but an overall improvement is demonstrated by increasing the number of stations and adjusting the solid precipitation amounts for wind speed undercatch.

Abstract. The 0 ∘C temperature threshold is critical for many meteorological and hydrological processes driven by melting and freezing in the atmosphere, surface, and sub-surface and by the associated precipitation varying between rain, freezing rain, wet snow, and snow. This threshold is especially important in cold regions such as Canada, because it is linked with freeze–thaw, snowmelt, and permafrost. This study develops a Canada-wide perspective on near-0 ∘C conditions using hourly surface temperature and precipitation type observations from 92 climate stations for the period from 1981 to 2011. In addition, nine stations from various climatic regions are selected for further analysis. Near-0 ∘C conditions are defined as periods when the surface temperature is between −2 and 2 ∘C. Near-0 ∘C conditions occur often across all regions of the country, although the annual number of days and hours and the duration of these events varies dramatically. Various types of precipitation (e.g., rain, freezing rain, wet snow, and ice pellets) sometimes occur with these temperatures. Near-0 ∘C conditions and the reported precipitation type occurrences tend to be higher in Atlantic Canada, although high values also occur in other regions. Trends of most temperature-based and precipitation-based indicators show little or no change despite a systematic warming in annual surface temperatures over Canada. Over the annual cycle, near-0 ∘C temperatures and precipitation often exhibit a pattern: short durations occur around summer, driven by the diurnal cycle, and a tendency toward longer durations around winter, associated with storms. There is also a tendency for near-0 ∘C surface temperatures to occur more often than expected relative to other temperature windows at some stations due, at least in part, to diabatic cooling and heating that take place with melting and freezing, respectively, in the atmosphere and at the surface.

Abstract Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge–shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynolds-averaged Navier–Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado.