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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

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.

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.

This dataset includes hotplate precipitation gauge data from 4 different sites sitting in the St. Lawrence River Valley. The hotplate data were obtained by the K63 Hotplate Total Precipitation Gauge. The instruments belonged to Université du Québec à Montréal (UQAM) and McGill University. UQAM has one hotplate permanently installed on the rooftop of the President-Kennedy building, in downtown Montreal. Another hotplate was temporarily deployed by the UQAM team in the instrument yard of UQTR as part of the WINTRE-MIX field campaign. McGill’s instruments are permanently installed in the instrument yards of Gault and Arboretum.

The Sentinel Radiation MetData provided in situ measurements of radiation fluxes for the Winter Precip Type Research Multi-scale Experiment (WINTRE-MIX) are included in this dataset. Pyranometer and radiation data are provided in netCDF format. Data were collected from the following 4 sites: Gault, Arboretum, UQAM-PK and Trois-Rivières.

This dataset contains raw data from an OTT Parsivel laser disdrometer that was temporarily installed at the Université du Québec à Trois-Rivières (UQTR) campus from December 2021 to April 2022 to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides histograms of hydrometeor size and fallspeed. The site sits in the St. Lawrence River Valley. Several other sites also collected Parsivel data during WINTRE-MIX.

This dataset contains raw data from an OTT Parsivel laser disdrometer permanently installed on the rooftop of UQAM President-Kennedy building in Montréal downtown, Québec. The instrument provides histograms of hydrometeor size and fallspeed. The site sits in the St. Lawrence River Valley. Several other sites also collected Parsivel data during WINTRE-MIX 2022.

The Sentinel Non-Radiation MetData provided in situ measurements of meteorological variables (such as 2-meter temperature, relative humidity, wind speed and direction, and precipitation) collected for the the Winter Precip Type Research Multi-scale Experiment (WINTRE-MIX) are included in this dataset. Data are provided in netCDF format. Data were collected from the following 4 sites: Gault, Arboretum, UQAM-PK and Trois-Rivières.