Articles | Volume 29, issue 4
https://doi.org/10.5194/hess-29-1135-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-1135-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2025
Research article |
| 28 Feb 2025
| 28 Feb 2025
Leveraging a radar-based disdrometer network to develop a probabilistic precipitation phase model in eastern Canada
Alexis Bédard-Therrien
CORRESPONDING AUTHOR
Département de génie civil et de génie des eaux, Université Laval, Québec, QC, Canada
François Anctil
Département de génie civil et de génie des eaux, Université Laval, Québec, QC, Canada
Julie M. Thériault
Département des sciences de la Terre et de l'atmosphère, Université du Québec à Montréal, Montréal, QC, Canada
Olivier Chalifour
Département des sciences de la Terre et de l'atmosphère, Université du Québec à Montréal, Montréal, QC, Canada
Fanny Payette
Hydro-Québec, Direction Planification de la conduite du système énergétique, Montréal, QC, Canada
Alexandre Vidal
Hydro-Québec, Direction Planification de la conduite du système énergétique, Montréal, QC, Canada
Daniel F. Nadeau
Département de génie civil et de génie des eaux, Université Laval, Québec, QC, Canada
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EGUsphere, https://doi.org/10.5194/egusphere-2026-1343, https://doi.org/10.5194/egusphere-2026-1343, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Mineral dust emissions are simulated for the first time from proglacial valleys of the St. Elias Mountains (western Canada/Alaska) to elucidate their contributions to regional climate forcing and global mineral dust loading. We implement a regional-scale dust emission model and validate surface meteorology, dust emissions, and boundary layer processes controlling aerosol dispersion for a holistic evaluation of the model.
Pascal Matte, Xavier Chartrand, David Purnell, Marc Simard, Alexandra Christensen, Francis Fortin-Legault, Jonathan Morin, Yann Côté-Nadeau, Élaine Desrosiers, Anthony Ouellet, Cédric Chavanne, François Anctil, Gabriela Siles, Scott Bedart, Silvia Innocenti, Mohammed Dabboor, Daniel L. Peters, and Mélanie Trudel
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Preprint under review for ESSD
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Kh Rahat Usman, Rodolfo Alvarado Montero, Tadros Ghobrial, François Anctil, and Arnejan van Loenen
Geosci. Model Dev., 19, 1559–1580, https://doi.org/10.5194/gmd-19-1559-2026, https://doi.org/10.5194/gmd-19-1559-2026, 2026
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Rivers in cold climate regions such as Canada undergo freeze up during winters which makes the estimation forecasting of under-ice discharge very challenging and uncertain since there is no reliable method other than direct measurements. The current study explored the potential of deploying a coupled modelling framework for the estimation and forecasting of this parameter. The framework showed promising potential in addressing the challenge of estimating and forecasting the under-ice discharge.
Clarence Gagnon, Daniel F. Nadeau, Alejandro Di Luca, Benoit Brault, Romane Hamon, Nicolas L. Roy, Marc-André Bourgault, and François Anctil
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Joseph Durat, Hadleigh David Thompson, Julie Mireille Thériault, and Philip Marsh
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Arctic snowfall measurements are challenging as light precipitation often goes undetected. Using measurements from a portable upward-pointing radar near Inuvik, Northwest Territories, our team captured low-rate snowfall that standard instruments usually miss. Including these data increased the winter snowfall amount by 24 %, confirming that current methods underestimate the amount of snow that falls each winter and highlighting the value of improved measurements techniques.
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Richard O'Keefe, Tiffany Windholz, Kyle A. Arndt, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Mathias Göckede, David Olefeldt, Gerard Rocher-Ros, Edward A. G. Schuur, David Bastviken, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Katrin Attermeyer, Mika Aurela, Sivakiruthika Balathandayuthabani, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andres Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Macall Hock, David Holl, Beth Holmes, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Chersity P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natacha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Misha Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martkainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Daniel F. Nadeau, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykanen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleski Räsänen, Helena Rautakoski, Riika Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Elena Veretennikova, Timo Vesala, Tarmo Virtanen, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
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Revised manuscript under review for ESSD
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Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
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Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
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Observations over several winters at two boreal sites in eastern Canada show that rain-on-snow (ROS) events lead to the formation of melt–freeze layers and that preferential flow is an important water transport mechanism in the sub-canopy snowpack. Simulations with SNOWPACK generally show good agreement with observations, except for the reproduction of melt–freeze layers. This was improved by simulating intercepted snow microstructure evolution, which also modulates ROS-induced runoff.
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François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
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Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci., 27, 2375–2395, https://doi.org/10.5194/hess-27-2375-2023, https://doi.org/10.5194/hess-27-2375-2023, 2023
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A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. The proposed approach supports the participation of end-users in interpreting the impact of climate change on water resources.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
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We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Jing Xu, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 26, 1001–1017, https://doi.org/10.5194/hess-26-1001-2022, https://doi.org/10.5194/hess-26-1001-2022, 2022
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The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
Emixi Sthefany Valdez, François Anctil, and Maria-Helena Ramos
Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, https://doi.org/10.5194/hess-26-197-2022, 2022
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We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
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The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
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Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Simon Ricard, Philippe Lucas-Picher, and François Anctil
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-451, https://doi.org/10.5194/hess-2021-451, 2021
Revised manuscript not accepted
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We propose a simplified hydroclimatic modelling workflow for producing hydrologic scenarios without resorting to meteorological observations. This innovative approach preserves trends and physical consistency between simulated climate variables, allows the implementation of modelling cascades despite observation scarcity, and supports the participation of end-users in producing and interpreting climate change impacts on water resources.
Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil
Hydrol. Earth Syst. Sci., 25, 4611–4629, https://doi.org/10.5194/hess-25-4611-2021, https://doi.org/10.5194/hess-25-4611-2021, 2021
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The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.
Cited articles
Atlas, D., Srivastava, R. C., and Sekhon, R. S.: Doppler radar characteristics of precipitation at vertical incidence, Rev. Geophys., 11, 1, https://doi.org/10.1029/RG011i001p00001, 1973.
Bédard-Therrien, A., Anctil, F., Theriault, J. M., Chalifour, O., Payette, F., Vidal, A., and Nadeau, D.: Data for “Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada”, Zenodo [data set], https://doi.org/10.5281/zenodo.10790810, 2024.
Behrangi, A., Yin, X., Rajagopal, S., Stampoulis, D., and Ye, H.: On distinguishing snowfall from rainfall using near-surface atmospheric information: Comparative analysis, uncertainties and hydrologic importance, Q. J. Roy. Meteor. Soc., 144, 89–102, https://doi.org/10.1002/qj.3240, 2018.
Behrangi, A., Singh, A., Song, Y., and Panahi, M.: Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations, Geophys. Res. Lett., 46, 11358–11366, https://doi.org/10.1029/2019GL084221, 2019.
Bourgouin, P.: A method to determine precipitation types, Weather Forecast., 15, 583–592, https://doi.org/10.1175/1520-0434(2000)015<0583:AMTDPT>2.0.CO;2, 2000.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Casellas, E., Bech, J., Veciana, R., Pineda, N., Rigo, T., Miró, J. R., and Sairouni, A.: Surface precipitation phase discrimination in complex terrain, J. Hydrol., 592, 125780, https://doi.org/10.1016/j.jhydrol.2020.125780, 2021.
Dai, A.: Temperature and pressure dependence of the rain-snow phase transition over land and ocean, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008GL033295, 2008.
Ding, B., Yang, K., Qin, J., Wang, L., Chen, Y., and He, X.: The dependence of precipitation types on surface elevation and meteorological conditions and its parameterization, J. Hydrol., 513, 154–163, https://doi.org/10.1016/j.jhydrol.2014.03.038, 2014.
Dingman, S. L. (Ed.): Physical hydrology, 3rd edn., Waveland press, ISBN 10 1-4786-1118-9 ISBN 13 978-1-4786-1118-9, 2015.
Ehsani, M. R. and Behrangi, A.: A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate, J. Hydrol., 610, 127884, https://doi.org/10.1016/j.jhydrol.2022.127884, 2022.
Essery, R., Morin, S., Lejeune, Y., and B Ménard, C.: A comparison of 1701 snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013.
Fehlmann, M., Rohrer, M., von Lerber, A., and Stoffel, M.: Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement, Atmos. Meas. Tech., 13, 4683–4698, https://doi.org/10.5194/amt-13-4683-2020, 2020.
Feiccabrino, J. M.: Precipitation phase uncertainty in cold region conceptual models resulting from meteorological forcing time-step intervals, Hydrol. Res., 51, 180–187, https://doi.org/10.2166/nh.2020.080, 2020.
Feiccabrino, J., Gustafsson, D., and Lundberg, A.: Surface-based precipitation phase determination methods in hydrological models, Hydrol. Res., 44, 44–57, https://doi.org/10.2166/nh.2012.158, 2013.
Feiccabrino, J., Graff, W., Lundberg, A., Sandström, N., and Gustafsson, D.: Meteorological knowledge useful for the improvement of snow rain separation in surface based models, Hydrology, 2, 266–288, https://doi.org/10.3390/hydrology2040266, 2015.
Froidurot, S., Hingray, B., Zin, I., and Gautheron, A.: Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables, J. Hydrometeorol., 15, 685–696, https://doi.org/10.1175/JHM-D-13-073.1, 2014.
Garcia-Benadi, A., Bech, J., Gonzalez, S., Udina, M., Codina, B., and Georgis, J.-F.: Precipitation type classification of micro rain radar data using an improved doppler spectral processing methodology, Remote Sens., 12, 4113, https://doi.org/10.3390/rs12244113, 2020.
Gunn, R. and Kinzer, G. D.: The terminal velocity of fall for water droplets in stagnant air, J. Meteorol., 6, 243–248, https://doi.org/10.1175/1520-0469(1949)006<0243:TTVOFF>2.0.CO;2, 1949.
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in Snowpack Simulations—Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance, Water Resour. Res., 55, 2779–2800, https://doi.org/10.1029/2018WR023403, 2019.
Harder, P. and Pomeroy, J.: Estimating precipitation phase using a psychrometric energy balance method, Hydrol. Process., 27, 1901–1914, https://doi.org/10.1002/hyp.9799, 2013.
Harder, P. and Pomeroy, J. W.: Hydrological model uncertainty due to precipitation – phase partitioning methods, Hydrol. Process., 28, 4311–4327, https://doi.org/10.1002/hyp.10214, 2014.
Harpold, A. A., Kaplan, M. L., Klos, P. Z., Link, T., McNamara, J. P., Rajagopal, S., Schumer, R., and Steele, C. M.: Rain or snow: hydrologic processes, observations, prediction, and research needs, Hydrol. Earth Syst. Sci., 21, 1–22, https://doi.org/10.5194/hess-21-1-2017, 2017.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Jennings, K. S., Winchell, T. S., Livneh, B., and Molotch, N. P.: Spatial variation of the rain-snow temperature threshold across the Northern Hemisphere, Nat. Commun., 9, 1148, https://doi.org/10.1038/s41467-018-03629-7, 2018.
Jennings, K. S., Arienzo, M. M., Collins, M., Hatchett, B. J., Nolin, A. W., and Aggett, G.: Crowdsourced Data Highlight Precipitation Phase Partitioning Variability in Rain-Snow Transition Zone, Earth Space Sci., 10, e2022EA002714, https://doi.org/10.1029/2022EA002714, 2023.
Jeong, D. I. and Sushama, L.: Rain-on-snow events over North America based on two Canadian regional climate models, Clim. Dynam., 50, 303–316, https://doi.org/10.1007/s00382-017-3609-x, 2018.
Kneifel, S., Maahn, M., Peters, G., and Simmer, C.: Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar), Meteorol. Atmos. Phys., 113, 75–87, https://doi.org/10.1007/s00703-011-0142-z, 2011.
Kochendorfer, J., Nitu, R., Wolff, M., Mekis, E., Rasmussen, R., Baker, B., Earle, M. E., Reverdin, A., Wong, K., Smith, C. D., Yang, D., Roulet, Y.-A., Buisan, S., Laine, T., Lee, G., Aceituno, J. L. C., Alastrué, J., Isaksen, K., Meyers, T., Brækkan, R., Landolt, S., Jachcik, A., and Poikonen, A.: Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE, Hydrol. Earth Syst. Sci., 21, 3525–3542, https://doi.org/10.5194/hess-21-3525-2017, 2017.
Leroux, N. R., Vionnet, V., and Thériault, J. M.: Performance of precipitation phase partitioning methods and their impact on snowpack evolution in a humid continental climate, Hydrol. Process., 37, e15028, https://doi.org/10.1002/hyp.15028, 2023.
List, R. J. (Ed.): Smithsonian meteorological tables, Smithsonian miscellaneous collections, Smithsonian Institution Press, 114, 1–527, 1951.
Locatelli, J. D. and Hobbs, P. V.: Fall speeds and masses of solid precipitation particles, J. Geophys. Res., 79, 2185–2197, https://doi.org/10.1029/JC079i015p02185, 1974.
Löffler-Mang, M., Kunz, M., and Schmid, W.: On the Performance of a Low-Cost K-Band Doppler Radar for Quantitative Rain Measurements, J. Atmos. Ocean. Tech., 16, 379–387, https://doi.org/10.1175/1520-0426(1999)016<0379:OTPOAL>2.0.CO;2, 1999.
Marks, D., Winstral, A., Reba, M., Pomeroy, J., and Kumar, M.: An evaluation of methods for determining during-storm precipitation phase and the rain/snow transition elevation at the surface in a mountain basin, Adv. Water Resour., 55, 98–110, https://doi.org/10.1016/j.advwatres.2012.11.012, 2013.
McGovern, A., Lagerquist, R., John Gagne, D., Jergensen, G. E., Elmore, K. L., Homeyer, C. R., and Smith, T.: Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning, B. Am. Meteorol. Soc., 100, 2175–2199, https://doi.org/10.1175/BAMS-D-18-0195.1, 2019.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Musselman, K. N., Lehner, F., Ikeda, K., Clark, M. P., Prein, A. F., Liu, C., Barlage, M., and Rasmussen, R.: Projected increases and shifts in rain-on-snow flood risk over western North America, Nat. Clim. Change, 8, 808–812, https://doi.org/10.1038/s41558-018-0236-4, 2018.
Pierre, A., Jutras, S., Smith, C., Kochendorfer, J., Fortin, V., and Anctil, F.: Evaluation of Catch Efficiency Transfer Functions for Unshielded and Single-Alter-Shielded Solid Precipitation Measurements, J. Atmos. Ocean. Tech., 36, 865–881, https://doi.org/10.1175/JTECH-D-18-0112.1, 2019.
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed, B. Am. Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/BAMS-D-11-00052.1, 2012.
Rokach, L., Maimon, O., and Shmueli, E.: Machine Learning for Data Science Handbook, 3, Springer Cham, https://doi.org/10.1007/978-3-031-24628-9, 2023.
Ross, A., Smith, C. D., and Barr, A.: An improved post-processing technique for automatic precipitation gauge time series, Atmos. Meas. Tech., 13, 2979–2994, https://doi.org/10.5194/amt-13-2979-2020, 2020.
Sarkar, T., Das, S., and Maitra, A.: Assessment of different raindrop size measuring techniques: Inter-comparison of Doppler radar, impact and optical disdrometer, Atmos. Res., 160, 15–27, https://doi.org/10.1016/j.atmosres.2015.03.001, 2015.
Shin, K., Kim, K., Song, J. J., and Lee, G.: Classification of Precipitation Types Based on Machine Learning Using Dual-Polarization Radar Measurements and Thermodynamic Fields, Remote Sens., 14, 3820, https://doi.org/10.3390/rs14153820, 2022.
Singh, P., Spitzbart, G., Hübl, H., and Weinmeister, H. W.: Hydrological response of snowpack under rain-on-snow events: a field study, J. Hydrol., 202, 1–20, https://doi.org/10.1016/S0022-1694(97)00004-8, 1997.
Smith, C. D., Mekis, E., Hartwell, M., and Ross, A.: The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019), Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, 2022.
Stewart, R. E.: Precipitation types in the transition region of winter storms, B. Am. Meteorol. Soc., 73, 287–296, https://doi.org/10.1175/1520-0477(1992)073<0287:PTITTR>2.0.CO;2, 1992.
Thériault, J. M. and Stewart, R. E.: A Parameterization of the Microphysical Processes Forming Many Types of Winter Precipitation, J. Atmos. Sci., 67, 1492–1508, https://doi.org/10.1175/2009JAS3224.1, 2010.
Thériault, J. M., Stewart, R. E., and Henson, W.: On the Dependence of Winter Precipitation Types on Temperature, Precipitation Rate, and Associated Features, J. Appl. Meteorol. Climatol., 49, 1429–1442, https://doi.org/10.1175/2010JAMC2321.1, 2010.
Thorpe, A. and Mason, B.: The evaporation of ice spheres and ice crystals, Brit. J. Appl. Phys., 17, 541, https://doi.org/10.1088/0508-3443/17/4/316, 1966.
Wang, Y. H., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., and Niu, G. Y.: A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States, Geophys. Res. Lett., 46, 13825–13835, https://doi.org/10.1029/2019GL085722, 2019.
Wayand, N. E., Stimberis, J., Zagrodnik, J. P., Mass, C. F., and Lundquist, J. D.: Improving simulations of precipitation phase and snowpack at a site subject to cold air intrusions: Snoqualmie Pass, WA, J. Geophys. Res.-Atmos., 121, 9929–9942, https://doi.org/10.1002/2016JD025387, 2016.
Wen, L., Nagabhatla, N., Lü, S., and Wang, S.-Y.: Impact of rain snow threshold temperature on snow depth simulation in land surface and regional atmospheric models, Adv. Atmos. Sci., 30, 1449–1460, https://doi.org/10.1007/s00376-012-2192-7, 2013.
Wever, N., Würzer, S., Fierz, C., and Lehning, M.: Simulating ice layer formation under the presence of preferential flow in layered snowpacks, The Cryosphere, 10, 2731–2744, https://doi.org/10.5194/tc-10-2731-2016, 2016.
WMO: 6.1.2 Units and scales, in: Measurement of Meteorological Variables, 2023 edition ed., Guide to instruments and methods of observation, Volume 1, WMO, Geneva, 574, 2018.
Yau, M. K. and Rogers, R. R.: A short course in cloud physics, 3rd edn., Elsevier, ISBN 9780080570945, 1996.
Ye, H., Yang, D., and Robinson, D.: Winter rain on snow and its association with air temperature in northern Eurasia, Hydrol. Process., 22, 2728–2736, https://doi.org/10.1002/hyp.7094, 2008.
Ye, H., Cohen, J., and Rawlins, M.: Discrimination of Solid from Liquid Precipitation over Northern Eurasia Using Surface Atmospheric Conditions, J. Hydrometeorol., 14, 1345–1355, https://doi.org/10.1175/JHM-D-12-0164.1, 2013.
Short summary
Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Precipitation data from an automated observational network in eastern Canada showed a...
