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Abstract. The various precipitation types formed within winter storms (such as snow, wet snow and freezing rain) often lead to very hazardous weather conditions. These types of precipitation often occur during the passage of a warm front as a warm air mass ascends over a cold air mass. To address this issue further, we used a one-dimensional kinematic cloud model to simulate this gentle ascent (≤10 cm/s) of warm air. The initial temperature profile has an above 0°C inversion, a lower subfreezing layer, and precipitation falls from above the temperature inversion. The cloud model is coupled to a double-moment microphysics scheme that simulates the production of various types of winter precipitation. The results are compared with those from a previous study carried out in still air. Based on the temporal evolution of surface precipitation, snow reaches the surface significantly faster than in still air whereas other precipitation types including freezing rain and ice pellets have a shorter duration. Overall, even weak background vertical ascent has an important impact on the precipitation reaching the surface, the time of the elimination of the melting layer, and also the evolution of the lower subfreezing layer.

Abstract Temperatures near 0°C represent a critical threshold for many environmental processes and socio‐economic activities. This study examines surface air temperatures ( T ) near 0°C (−2°C ≤  T  ≤ 2°C) across much of southern Canada over a 13 year period (October 2000–September 2013). It utilized hourly data from 39 weather stations and from 4‐km resolution Weather Research and Forecasting model simulations that were both a retrospective simulation as well as a pseudo‐global warming simulation applicable near the end of the 21st century. Average annual occurrences of near‐0°C conditions increase by a relatively small amount of 5.1% from 985 hr in the current climate to 1,035 hr within the future one. Near‐0°C occurrences with precipitation vary from <5% to approximately 50% of these values. Near‐0°C occurrences are sometimes higher than values of neighboring temperatures. These near‐0°C peaks in temperature distributions can occur in both the current and future climate, in only one, or in neither. Only 4.3% of southern Canada is not associated with a near‐0°C peak and 65.8% is associated with a near‐0°C peak in both climates. It is inferred that latent heat exchanges from the melting and freezing of, for example, precipitation and the snowpack contribute significantly to some of these findings. , Plain Language Summary Our changing climate is spurring the development of huge efforts to improve resiliency. For many regions of the world, these efforts must account for potential changes in near‐0°C conditions within which both melting and freezing can occur and the accompanying latent heat exchanges can push air temperature toward 0°C. This article focuses on the occurrence of near‐0°C surface temperatures across southern Canada through an examination of observational and model information including projections in a future warmer (average 6.1°C increase) climate near the end of the 21st century. Average annual occurrences of near‐0°C conditions increase by a relatively small amount of 5.1% in the future climate and highest values continue to be along the Pacific coast or within the Western Cordillera and lowest values continue to be within central and northern areas. Near‐0°C occurrences are often higher than those of neighboring temperatures in the present climate and some of these elevated occurrences persist into the future one despite dramatic warming. It is inferred that latent heat exchanges from the melting and freezing of precipitation and snowcover contribute to these findings. , Key Points Near‐0°C surface air temperatures were examined over southern Canada using retrospective and pseudo‐global warming simulations Overall average occurrences increase slightly and their spatial patterns are largely maintained in the warmer climate Near‐0°C occurrences sometimes exceed those of neighboring temperatures and this feature often persists despite dramatic warming

Winter storms produce major problems for society, and the key responsible factor is often the varying types of precipitation. The objective of this study is to better understand the formation of different types of winter precipitation (freezing rain, ice pellets, snow, slush, wet snow and refrozen wet snow) within the varying and interacting environmental conditions in many winter storms. To address this issue, a one‐dimensional cloud model utilizing a double‐moment bulk microphysics scheme has been developed. Temperature and moisture profiles favorable for the formation of different winter precipitation types were varied in a systematic manner in an environment where snow is falling continuously through a temperature inversion. The ensuing precipitation evolved as a result of the variations in atmospheric temperature and moisture arising from phase changes such as melting and freezing. This study underlines the often complex manner through which different precipitation types form. It also demonstrates that the formation of semimelted particles can have a profound effect on the evolution of precipitation types aloft and at the surface. Furthermore, some types of precipitation only form within a narrow range of environmental conditions.

Abstract In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation‐phase partitioning methods (PPMs) that use near‐surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric‐based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5‐km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric‐based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post‐processing algorithm determining the snow level and the associated precipitation phase. Two ground‐based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet‐bulb temperature. Compared to the upper benchmark, the snow‐level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post‐processed snow level. The limitations to drive snowpack models at slope scale are also discussed. , Plain Language Summary The partitioning of precipitation between rainfall and snowfall is a crucial component of the evolution of the snowpack in mountains. Most snowpack models use the air temperature and humidity near the surface to derive the precipitation phase. However, the phase at the surface is strongly influenced by processes such as melting and refreezing of falling hydrometeors that occur above the surface. Atmospheric models simulate these processes and the corresponding phase at the surface. However, snowpack models rarely use this information. In this study, we considered two estimates of precipitation phase from an atmospheric model and tested them with a physically‐based snow model over the mountains of southwestern Canada and northwestern United States. The results were compared with traditional approaches using the air temperature and humidity near the surface to derive the precipitation phase. Our results showed that the precipitation phase associated with the snow level obtained from the atmospheric model improved snowfall estimate and snowpack prediction compared to the traditional approaches. In contrast, the cloud/precipitation scheme of the atmospheric model decreased performance in phase estimate and snow simulations due to missing physical processes. Our study highlights that snowpack predictions in the mountains can be improved if valuable information is obtained from atmospheric models. , Key Points Estimates of precipitation phase from an atmospheric model were used to drive snow simulations with a detailed snowpack model Snowfall prediction and snowpack modeling are improved by using the snow level from post‐processing of the atmospheric model Direct precipitation phase from the microphysics scheme does not improve snow simulations compared to simpler rain‐snow partitioning schemes

Abstract In June 2013, excessive rainfall associated with an intense weather system triggered severe flooding in southern Alberta, which became the costliest natural disaster in Canadian history. This article provides an overview of the climatological aspects and large‐scale hydrometeorological features associated with the flooding event based upon information from a variety of sources, including satellite data, upper air soundings, surface observations and operational model analyses. The results show that multiple factors combined to create this unusually severe event. The event was characterized by a slow‐moving upper level low pressure system west of Alberta, blocked by an upper level ridge, while an associated well‐organized surface low pressure system kept southern Alberta, especially the eastern slopes of the Rocky Mountains, in continuous precipitation for up to two days. Results from air parcel trajectory analysis show that a significant amount of the moisture originated from the central Great Plains, transported into Alberta by a southeasterly low level jet. The event was first dominated by significant thunderstorm activity, and then evolved into continuous precipitation supported by the synoptic‐scale low pressure system. Both the thunderstorm activity and upslope winds associated with the low pressure system produced large rainfall amounts. A comparison with previous similar events occurring in the same region suggests that the synoptic‐scale features associated with the 2013 rainfall event were not particularly intense; however, its storm environment was the most convectively unstable. The system also exhibited a relatively high freezing level, which resulted in rain, rather than snow, mainly falling over the still snow‐covered mountainous areas. Melting associated with this rain‐on‐snow scenario likely contributed to downstream flooding. Furthermore, above‐normal snowfall in the preceding spring helped to maintain snow in the high‐elevation areas, which facilitated the rain‐on‐snow event. Copyright © 2016 John Wiley & Sons, Ltd.

Manual hydrometeor macro photographs were collected during the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) between 01 Feb – 15 March 2022. The macro photographs were collected by manual ground observation teams from the University at Albany (UAlbany), University of Colorado Boulder (CU), Université du Québec à Montréal (UQAM), and McGill University (McGill). Sections in the readme provide information on the camera setup, protocol, and dataset file formats, as well as limitations associated with the data.

Sounding data collected during the WINTRE-MIX project field phase are included in this dataset. This dataset has soundings from the University of Colorado (CU) DOWs, McGill University at Gault, St Jean sur Richelieu, University at Albany (UA) DOWs, Université du Québec à Montréal (UQAM), and UA Essex sites. data file names are of the form "upperair.sounding.YYYYMMDDHHMM.siteName.[txt or csv]" where the YYYMMDDHHMM indicates the date and time of the sounding and the siteName indicates the site source and location. See the documentation for more information on this dataset.

Abstract Data obtained from a variety of sources including the Canadian Lightning Detection Network, weather radars, weather stations and operational numerical weather model analyses were used to address the evolution of precipitation during the June 2013 southern Alberta flood. The event was linked to a mid‐level closed low pressure system to the west of the region and a surface low pressure region initially to its south. This configuration brought warm, moist unstable air into the region that led to dramatic, organized convection with an abundance of lightning and some hail. Such conditions occurred in the southern parts of the region whereas the northern parts were devoid of lightning. Initially, precipitation rates were high (extreme 15‐min rainfall rates up to 102 mm h −1 were measured) but decreased to lower values as the precipitation shifted to long‐lived stratiform conditions. Both the convective and stratiform precipitation components were affected by the topography. Similar flooding events, such as June 2002, have occurred over this region although the 2002 event was colder and precipitation was not associated with substantial convection over southwest Alberta. Copyright © 2016 Her Majesty the Queen in Right of Canada. Hydrological Processes. © John Wiley & Sons, Ltd.

This dataset contains raw data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) permanently installed on the rooftop of UQAM President-Kennedy building in Montréal downtown, Québec. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site sits in the St. Lawrence River Valley. Several other sites also collected MRR data during WINTRE-MIX.

This dataset contains processed data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) permanently installed on the rooftop of UQAM President-Kennedy building in Montréal downtown, Québec. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site sits in the St. Lawrence River Valley. Several other sites also collected MRR data during WINTRE-MIX.

This dataset contains data from a METEK vertically profiling K-band Micro Rain Radar Pro (MRR-Pro) that was temporarily installed at the Université du Québec à Trois-Rivières (UQTR) campus during February and March 2022 to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site sits in the St. Lawrence River Valley. Several other sites also collected MRR data during WINTRE-MIX.

This dataset contains raw data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) installed at the climate sentinel in the Arboretum forest reserve (ARBO), about 30 km west of Montréal downtown, Québec, Canada. The data were collected as part of the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project held in February and March of 2022. The instrument used to collect the data in this dataset provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site is located near the confluence of the Ottawa River and the St. Lawrence River. Several other sites also collected MRR data during WINTRE-MIX.

This dataset contains post-processed data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) installed at the climate sentinel in the Gault Nature Reserve (GAUL), about 30 km east of Montréal, Québec. The data were collected as part of the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project held in February and March of 2022. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site is located at the southern flank of Mont-Saint-Hilaire, a mountain with an elevation of about 400 m (above mean sea-level). Several other sites also collected MRR data during WINTRE-MIX.

This dataset contains raw data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) installed at the climate sentinel in the Gault Nature Reserve (GAUL), about 30 km east of Montréal, Québec.The data were collected as part of the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project held in February and March of 2022. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site is located at the southern flank of Mont-Saint-Hilaire, a mountain with an elevation of about 400 m (above mean sea-level). Several other sites also collected MRR data during WINTRE-MIX.

This dataset contains raw data collected from an OTT Parsivel laser disdrometer installed at a climate sentinel (Arboretum) in the Saint Lawrence River Valley. The data is available from 1 Nov 2021 to 31 March 2022 (inclusive) to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides histograms of hydrometeor size and fallspeed. The Arboretum site is located on the southwestern tip of Montreal Island near the confluence of the Ottawa River and the St. Lawrence River. Several other sites also collected Parsivel data during WINTRE-MIX.

This dataset contains raw data collected from an OTT Parsivel laser disdrometer installed at a climate sentinel (Gault) in the Saint Lawrence River Valley The data is available from 1 Nov 2021 to 31 March 2022 (inclusive) to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides histograms of hydrometeor size and fallspeed. The Gault site is located behind Mont-Saint-Hilaire, about an hour’s drive east of Montreal. Other sites also collected Parsivel data during WINTRE-MIX.

This dataset contains ice thickness data collected by ice detectors installed at various climate sentinels within the Saint Lawrence River Valley for the WINTRE-MIX field project. The names of four stations for which ice accretion data are available in ‘CFI_Climate_Sentinels_Icing_Detector_Data.nc’ are given in Table 1 of the readme documentation, along with their corresponding four-letter identifiers.

This dataset includes snow depth and snow water equivalent data from 4 sites in the St. Lawrence River Valley collected for the WINTRE-MIX field project. The snow depth data were obtained by the SDMS40: Multipoint Scanning Snowfall Sensor and the SR50A Snow-Depth Sensor. The CS725 Snow-Water Equivalent Sensor measured the snow water equivalent data. This dataset includes measurements done at 4 different sites: UQAM-PK (UQAM), Trois-Rivières, Gault and Arboretum.