Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5785-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-5785-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
Hadleigh D. Thompson
Department of Earth and Atmospheric Sciences, Centre ESCER, Université du Québec à Montréal, Montréal, Quebec, H3C 3P8, Canada
Julie M. Thériault
CORRESPONDING AUTHOR
Department of Earth and Atmospheric Sciences, Centre ESCER, Université du Québec à Montréal, Montréal, Quebec, H3C 3P8, Canada
Stephen J. Déry
Department of Geography, Earth and Environmental Sciences and Natural Resources and Environmental Studies Program, University of Northern British Columbia, Prince George, British Columbia, V2N 4Z9, Canada
Ronald E. Stewart
Department of Environment and Geography, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
Dominique Boisvert
Department of Earth and Atmospheric Sciences, Centre ESCER, Université du Québec à Montréal, Montréal, Quebec, H3C 3P8, Canada
Lisa Rickard
Department of Geography, Earth and Environmental Sciences and Natural Resources and Environmental Studies Program, University of Northern British Columbia, Prince George, British Columbia, V2N 4Z9, Canada
Nicolas R. Leroux
Department of Earth and Atmospheric Sciences, Centre ESCER, Université du Québec à Montréal, Montréal, Quebec, H3C 3P8, Canada
Matteo Colli
Artys Srl, Piazza della Vittoria, 9/3, 16121 Genoa, Italy
Vincent Vionnet
Meteorological Research Division, Environment and Climate Change Canada, Dorval, Quebec, H9P 1J3, Canada
Related authors
Julie M. Thériault, Stephen J. Déry, John W. Pomeroy, Hilary M. Smith, Juris Almonte, André Bertoncini, Robert W. Crawford, Aurélie Desroches-Lapointe, Mathieu Lachapelle, Zen Mariani, Selina Mitchell, Jeremy E. Morris, Charlie Hébert-Pinard, Peter Rodriguez, and Hadleigh D. Thompson
Earth Syst. Sci. Data, 13, 1233–1249, https://doi.org/10.5194/essd-13-1233-2021, https://doi.org/10.5194/essd-13-1233-2021, 2021
Short summary
Short summary
This article discusses the data that were collected during the Storms and Precipitation Across the continental Divide (SPADE) field campaign in spring 2019 in the Canadian Rockies, along the Alberta and British Columbia border. Various instruments were installed at five field sites to gather information about atmospheric conditions focussing on precipitation. Details about the field sites, the instrumentation used, the variables collected, and the collection methods and intervals are presented.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
The Cryosphere, 19, 2949–2962, https://doi.org/10.5194/tc-19-2949-2025, https://doi.org/10.5194/tc-19-2949-2025, 2025
Short summary
Short summary
Measuring snow mass from radar measurements is possible with information on snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layers yielding richer information but at increased computational cost. Here, we show the capabilities of a new method for simplifying a complex snowpack while preserving the scattering behavior of the snowpack and conserving its mass.
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data, 17, 3619–3640, https://doi.org/10.5194/essd-17-3619-2025, https://doi.org/10.5194/essd-17-3619-2025, 2025
Short summary
Short summary
In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Norway, Finland, Switzerland, Russia, and Nepal over the period 1979–2021. It includes more than 11.5 million observations from more than 10 000 different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
Alireza Amani, Marie-Amélie Boucher, Alexandre R. Cabral, Vincent Vionnet, and Étienne Gaborit
Hydrol. Earth Syst. Sci., 29, 2445–2465, https://doi.org/10.5194/hess-29-2445-2025, https://doi.org/10.5194/hess-29-2445-2025, 2025
Short summary
Short summary
Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil-freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid in water resource management decisions in cold climates.
Benoit Montpetit, Julien Meloche, Vincent Vionnet, Chris Derksen, Georgina Wooley, Nicolas R. Leroux, Paul Siqueira, J. Max Adams, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2317, https://doi.org/10.5194/egusphere-2025-2317, 2025
Short summary
Short summary
This paper presents the workflow to retrieve snow water equivalent from radar measurements for the future Canadian radar satellite mission, TSMM. The workflow is validated by using airborne radar data collected at Trail Valley Creek, Canada, during winter 2018–19. We detail important considerations to have in the context of an Earth Observation mission over a vast region such as Canada. The results show that it is possible to achieve the desired accuracy for TSMM, over an Arctic environment.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498, https://doi.org/10.5194/egusphere-2025-1498, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
Short summary
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.
Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux
The Cryosphere, 19, 769–792, https://doi.org/10.5194/tc-19-769-2025, https://doi.org/10.5194/tc-19-769-2025, 2025
Short summary
Short summary
This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability in light-absorbing particles (LAPs) like black carbon and dust. A global climatology of LAP deposition was created and used to optimize a parameter in the Crocus snow model. Testing at 10 global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can enhance other snow models' predictions without complex simulations.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Mathieu Lachapelle, Mélissa Cholette, and Julie M. Thériault
Atmos. Chem. Phys., 24, 11285–11304, https://doi.org/10.5194/acp-24-11285-2024, https://doi.org/10.5194/acp-24-11285-2024, 2024
Short summary
Short summary
Hazardous precipitation types such as ice pellets and freezing rain are difficult to predict because they are associated with complex microphysical processes. Using Predicted Particle Properties (P3), this work shows that secondary ice production processes increase the amount of ice pellets simulated while decreasing the amount of freezing rain. Moreover, the properties of the simulated precipitation compare well with those that were measured.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
Short summary
Short summary
As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
Short summary
Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
Short summary
Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
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
Short summary
Short summary
Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
Short summary
Short summary
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
Short summary
Short summary
Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
Short summary
Short summary
Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
Chris M. DeBeer, Howard S. Wheater, John W. Pomeroy, Alan G. Barr, Jennifer L. Baltzer, Jill F. Johnstone, Merritt R. Turetsky, Ronald E. Stewart, Masaki Hayashi, Garth van der Kamp, Shawn Marshall, Elizabeth Campbell, Philip Marsh, Sean K. Carey, William L. Quinton, Yanping Li, Saman Razavi, Aaron Berg, Jeffrey J. McDonnell, Christopher Spence, Warren D. Helgason, Andrew M. Ireson, T. Andrew Black, Mohamed Elshamy, Fuad Yassin, Bruce Davison, Allan Howard, Julie M. Thériault, Kevin Shook, Michael N. Demuth, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 25, 1849–1882, https://doi.org/10.5194/hess-25-1849-2021, https://doi.org/10.5194/hess-25-1849-2021, 2021
Short summary
Short summary
This article examines future changes in land cover and hydrological cycling across the interior of western Canada under climate conditions projected for the 21st century. Key insights into the mechanisms and interactions of Earth system and hydrological process responses are presented, and this understanding is used together with model application to provide a synthesis of future change. This has allowed more scientifically informed projections than have hitherto been available.
Julie M. Thériault, Stephen J. Déry, John W. Pomeroy, Hilary M. Smith, Juris Almonte, André Bertoncini, Robert W. Crawford, Aurélie Desroches-Lapointe, Mathieu Lachapelle, Zen Mariani, Selina Mitchell, Jeremy E. Morris, Charlie Hébert-Pinard, Peter Rodriguez, and Hadleigh D. Thompson
Earth Syst. Sci. Data, 13, 1233–1249, https://doi.org/10.5194/essd-13-1233-2021, https://doi.org/10.5194/essd-13-1233-2021, 2021
Short summary
Short summary
This article discusses the data that were collected during the Storms and Precipitation Across the continental Divide (SPADE) field campaign in spring 2019 in the Canadian Rockies, along the Alberta and British Columbia border. Various instruments were installed at five field sites to gather information about atmospheric conditions focussing on precipitation. Details about the field sites, the instrumentation used, the variables collected, and the collection methods and intervals are presented.
Vincent Vionnet, Christopher B. Marsh, Brian Menounos, Simon Gascoin, Nicholas E. Wayand, Joseph Shea, Kriti Mukherjee, and John W. Pomeroy
The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, https://doi.org/10.5194/tc-15-743-2021, 2021
Short summary
Short summary
Mountain snow cover provides critical supplies of fresh water to downstream users. Its accurate prediction requires inclusion of often-ignored processes. A multi-scale modelling strategy is presented that efficiently accounts for snow redistribution. Model accuracy is assessed via airborne lidar and optical satellite imagery. With redistribution the model captures the elevation–snow depth relation. Redistribution processes are required to reproduce spatial variability, such as around ridges.
Guoqiang Tang, Martyn P. Clark, Andrew J. Newman, Andrew W. Wood, Simon Michael Papalexiou, Vincent Vionnet, and Paul H. Whitfield
Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, https://doi.org/10.5194/essd-12-2381-2020, 2020
Short summary
Short summary
Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.
Cited articles
Angulo-Martínez, M., Beguería, S., Latorre, B., and Fernández-Raga, M.: Comparison of precipitation measurements by OTT Parsivel2 and Thies LPM optical disdrometers, Hydrol. Earth Syst. Sci., 22, 2811–2837, https://doi.org/10.5194/hess-22-2811-2018, 2018.
Annandale, J., Jovanovic, N., Benadé, and N., Allen, R.: Software for missing data error analysis of Penman-Monteith reference evapotranspiration, Irrigation Sci., 21, 57–67, https://doi.org/10.1007/s002710100047, 2002.
Apogee Instruments Inc.: Infrared radiometers owner's manual, https://www.apogeeinstruments.com/content/SI-400-manual.pdf (last access: 9 March 2023), 2022.
Beltaos, S., Ismail, S., and Burrell, B.: Midwinter breakup and jamming on the upper Saint John River: A case study, Can. J. Civil Eng., 30, 77–88, https://doi.org/10.1139/l02-062, 2003.
Budhathoki, S., Rokaya, P., and Lindenschmidt, K. E.: Impacts of future climate on the hydrology of a transboundary river basin in northeastern North America, J. Hydrol., 605, 127317, https://doi.org/10.1016/j.jhydrol.2021.127317, 2022.
Buttle, J. M., Allen, D. M., Caissie, D., Davison, B., Hayashi, M., Peters, D. L., Pomeroy, J. W., Simonovic, S., St-Hilaire, A., and Whitfield, P. H.: Flood processes in Canada: Regional and special aspects, Can. Water Resour. J., 41, 7–30, https://doi.org/10.1080/07011784.2015.1131629, 2016.
Campbell Scientific: IRGASON: Integrated Open Path Gas Analyzer and 3-D Sonic Anemometer, https://s.campbellsci.com/documents/us/manuals/irgason.pdf (last access: 5 December 2023), 2022a.
Campbell Scientific: EASYFLUX DL: EASYFLUX DL CR6OP or CR1KXOP For CR6 or CR1000X and Open-Path Eddy-Covariance Systems. Retrieved August 31, 2022, https://s.campbellsci.com/documents/us/manuals/easyflux-dl-cr6op.pdf (last access: 6 September 2023), 2022b.
Cauteruccio, A., Chinchella, E., Stagnaro, M., and Lanza, L. G.: Snow particle collection efficiency and adjustment curves for the hotplate precipitation gauge, J. Hydrometeorol., 22, 941–954, https://doi.org/10.1175/JHM-D-20-0149.1, 2021.
Cifelli, R., Doesken, N., Kennedy, P., Carey, L. D., Rutledge, S. A., Gimmestad, C., and Depue, T.: The Community Collaborative Rain, Hail, and Snow Network: Informal education for scientists and citizens, B. Am. Meteorol. Soc., 86, 1069–1077, http://www.jstor.org/stable/26221344 (last access: 5 December 2023), 2005.
Colli, M., Stagnaro, M., Caridi, A., Lanza, L.G., Randazzo, A., Pastorino, M., Caviglia, D.D., and Delucchi, A.: A Field Assessment of a rain estimation system based on satellite-to-earth microwave links, IEEE T. Geosci. Remote, 57, 2864–2875, https://doi.org/10.1109/TGRS.2018.2878338, 2019.
Colli, M., Cassola, F., Martina, F., Trovatore, E., Delucchi, A., Maggiolo, S., and Caviglia, D.D.: Rainfall fields monitoring based on satellite microwave down-links and traditional techniques in the city of Genoa, IEEE T. Geosci. Remote, 58, 6266–6280, https://doi.org/10.1109/TGRS.2020.2976137, 2020.
Colorado Climate Center: Community collaborative rain, hail & snow network, CoCoRaHS Canada [data set], https://cocorahs.org/Canada.aspx (last access: 15 March 2022), 2017.
Domine, F., Lackner, G., Sarrazin, D., Poirier, M., and Belke-Brea, M.: Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models, Earth Syst. Sci. Data, 13, 4331–4348, https://doi.org/10.5194/essd-13-4331-2021, 2021.
Environment and Climate Change Canada (ECCC): Top ten weather stories for 2008: story four: Saint John River floods from top to bottom, https://www.ec.gc.ca/meteo-weather/default.asp?lang=En&n=7D6FDB7C-1 (last access: 25 March 2023), 2017.
Environment and Climate Change Canada (ECCC): Canada's top 10 weather stories of 2018: 7. Flash flooding of the Saint John River, https://www.canada.ca/en/environment-climate-change/services/top-ten-weather-stories/2018.html#toc6 (last access: 25 March 2023), 2019.
Environment and Climate Change Canada (ECCC): Canada's top 10 weather stories of 2019: 9. Saint John River floods again, https://www.canada.ca/en/environment-climate-change/services/top-ten-weather-stories/2019.html#toc10 (last access: 25 March 2023), 2020.
Falconi, M. T., von Lerber, A., Ori, D., Marzano, F. S., and Moisseev, D.: Snowfall retrieval at X, Ka and W bands: consistency of backscattering and microphysical properties using BAECC ground-based measurements, Atmos. Meas. Tech., 11, 3059–3079, https://doi.org/10.5194/amt-11-3059-2018, 2018.
Fitch, K. E., Hang, C., Talaei, A., and Garrett, T. J.: Arctic observations and numerical simulations of surface wind effects on Multi-Angle Snowflake Camera measurements, Atmos. Meas. Tech., 14, 1127–1142, https://doi.org/10.5194/amt-14-1127-2021, 2021.
Foken, T., Göockede, M., Mauder, M., Mahrt, L., Amiro, B., and Munger, W.: Post-Field Data Quality Control, in: Handbook of Micrometeorology, Atmospheric and Oceanographic Sciences Library, edited by: Lee, X., Massman, W., and Law, B., vol. 29, Springer, Dordrecht, https://doi.org/10.1007/1-4020-2265-4_9, 2004.
Fortin, G., and Dubreuil, V.: A geostatistical approach to create a new climate types map at regional scale: case study of New Brunswick, Canada, Theor. Appl. Climatol., 139, 323–334, https://doi.org/10.1007/s00704-019-02961-2, 2020.
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012.
Giannetti, F. and Reggiannini, R.: Opportunistic rain rate estimation from measurements of satellite downlink attenuation: A survey, Sensors, 21, 5872, https://doi.org/10.3390/s21175872, 2021.
Gibson, S. R. and Stewart, R. E.: Observations of ice pellets during a winter storm, Atmos. Res., 85, 64–76, https://doi.org/10.1016/j.atmosres.2006.11.004, 2007.
Hauser, D., Amayenc, P., Nutten, B., and Waldteufel, P.: A New Optical Instrument for Simultaneous Measurement of Raindrop Diameter and Fall Speed Distributions, J. Atmos. Ocean. Tech., 1, 256–269, https://doi.org/10.1175/1520-0426(1984)001<0256:ANOIFS>2.0.CO;2, 1984.
Hicks, A. and Notaroš, B. M.: Method for classification of snowflakes based on images by a multi-angle snowflake camera using convolutional neural networks, J. Atmos. Ocean. Tech., 36, 2267–2282, https://doi.org/10.1175/JTECH-D-19-0055.1, 2019.
Houze, R. A., McMurdie, L. A., Petersen, W. A., Schwall Er, M. R., Baccus, W., Lundquist, J. D., Mass, C. F., Nijssen, B., Rutledge, S. A., Hudak, D. R., Tanelli, S., Mace, G. G., Poellot, M. R., Lettenmaier, D. P., Zagrodnik, J. P., Rowe, A. K., DeHart, J. C., Madaus, L. E., and Barnes, H. C.: The olympic mountains experiment (Olympex), B. Am. Meteorol. Soc., 98, 2167–2188, https://doi.org/10.1175/BAMS-D-16-0182.1, 2017.
Ishizaka, M., Motoyoshi, H., Nakai, S., Shiina, T., Kumakura, T., and Muramoto, K. I.: A new method for identifying the main type of solid hydrometeors contributing to snowfall from measured size-fall speed relationship, J. Meteorol. Soc. Jpn., 91, 747–762, https://doi.org/10.2151/jmsj.2013-602, 2013.
Joe, P., Scott, B., Doyle, C., Isaac, G., Gultepe, I., Forsyth, D., Cober, S., Campos, E., Heckman, I., Donaldson, N., Hudak, D., Rasmussen, R., Kucera, P., Stewart, R., Thériault, J. M., Fisico, T., Rasmussen, K. L., Carmichael, H., Laplante, A., Bailey, M., and Boudala, F.: The Monitoring Network of the Vancouver 2010 Olympics, Pure Appl. Geophys., 171, 25–58, https://doi.org/10.1007/s00024-012-0588-z, 2014.
Kenny, J. L. and Secord, A. G.: Engineering modernity: Hydroelectric development in New Brunswick, 1945–1970, Acadiensis, 39, 3–26, 2010.
Kochendorfer, J., Rasmussen, R., Wolff, M., Baker, B., Hall, M. E., Meyers, T., Landolt, S., Jachcik, A., Isaksen, K., Brækkan, R., and Leeper, R.: The quantification and correction of wind-induced precipitation measurement errors, Hydrol. Earth Syst. Sci., 21, 1973–1989, https://doi.org/10.5194/hess-21-1973-2017, 2017.
Liao, L., Meneghini, R., Tokay, A., and Bliven, L. F.: Retrieval of snow properties for Ku- and Ka-band dual-frequency radar, J. Appl. Meteorol. Clim., 55, 1845–1858, https://doi.org/10.1175/JAMC-D-15-0355.1, 2016.
Lapo, K. E., Hinkelman, L. M., Landry, C. C., Massmann, A. K., and Lundquist, J. D.: A simple algorithm for identifying periods of snow accumulation on a radiometer, Water Resour. Res., 51, 7820–7828, https://doi.org/10.1002/2015WR017590, 2015.
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, 1–18, https://doi.org/10.1002/hyp.15028, 2023.
Löffler-Mang, M. and Joss, J.: An optical disdrometer for measuring size and velocity of hydrometeors, J. Atmos. Ocean. Tech., 17, 130–139, https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2, 2000.
Maahn, M. and Kollias, P.: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech., 5, 2661–2673, https://doi.org/10.5194/amt-5-2661-2012, 2012.
Marwitz, J.: A comparison of winter orographic storms over the San Juan Mountains and the Sierra Nevada, Meteor. Mon., 43, 109–114, https://doi.org/10.1175/0065-9401-21.43.109, 1986.
McMurdie, L. A., Heymsfield, G. M., Yorks, J. E., Braun, S. A., Skofronick-Jackson, G., Rauber, R. M., Yuter, S., Colle, B., McFarquhar, G. M., Poellot, M., Novak, D. R., Lang, T. J., Kroodsma, R., McLinden, M., Oue, M., Kollias, P., Kumjian, M. R., Greybush, S. J., Heymsfield, A. J., Finlon, J. A., McDonald, V. L., and Nicholls, S.: Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign, B. Am. Meteorol. Soc., 103, E1243–E1269, https://doi.org/10.1175/BAMS-D-20-0246.1, 2022.
METEK: Micro Rain Radar MRR-PRO, https://metek.de/wp-content/uploads/2016/12/20180206_Datenblatt_MRR-PRO.pdf (last access: 22 June 2022), 2017.
Newton, B. and Burrell, B. C.: The April–May 2008 flood event in the Saint John River Basin: Causes, assessment, and damages, Can. Water Resour. J., 41, 118–128, https://doi.org/10.1080/07011784.2015.1009950, 2016.
Nitu, R., Roulet, Y.-A., Wolff, M., Earle, M., Reverdin, A., Smith, C., Kochendorfer, J., Morin, S., Rasmussen, R., Wong, K., Alastrué, J., Arnold, L., Baker, B., Buisan, S., Collado, J. L., Colli, M., Collins, B., Gaydos, A., Hannula, H.-R., J. Hoover, Joe, P., Kontu, A., Laine, T., Lanza, L., Lanzinger, E., Lee, G. W., Lejeune, Y., Leppänen, L., Mekis, E., Panel, J.-M., Poikonen, A., Ryu, S., Sabatini, F., Theriault, J., Yang, D., Genthon, C., van den Heuvel, F., Hirasawa, N., Konishi, H., Motoyoshi, H., Nakai, S., Nishimura, K., Senese, A., and Yamashita, K.: WMO Solid Precipitation Intercomparison Experiment (SPICE) (2012–2015), Report 131, World Meteorological Organization, 1429 pp., https://library.wmo.int/doc_num.php?explnum_id=5686 (last access: 5 December 2023), 2018.
Onset Computer Corporation: Hobo temperature/RH data logger, HOBO Temperature/RH Data Logger MX2301A | Onset Data Loggers, https://www.onsetcomp.com/products/data-loggers/mx2301a/, last access: 15 March 2022.
OTT: Operating instructions Present Weather Sensor OTT Parsivel2, https://www.ott.com/download/operating-instructions-present-weather-sensor-ott-parsivel2-with-screen-heating-2/ (last access: 5 September 2023), 2019.
Pond Engineering: K63 Hotplate total precipitation gauge, http://www.pondengineering.com/k63 (last access: 25 March 2023), 2021.
Praz, C., Roulet, Y.-A., and Berne, A.: Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera, Atmos. Meas. Tech., 10, 1335–1357, https://doi.org/10.5194/amt-10-1335-2017, 2017.
Rasmussen, R. M., Vivekanandan, J., Cole, J., Myers, B., and Masters, C.: The estimation of snowfall rate using visibility, J. Appl. Meteorol., 38, 1542–1563, https://doi.org/10.1175/1520-0450(1999)038<1542:TEOSRU>2.0.CO;2, 1999.
Rasmussen, R. M., Hallett, J., Purcell, R., Landolt, S. D., and Cole, J.: The hotplate precipitation gauge, J. Atmos. Ocean. Tech., 28, 148–164, https://doi.org/10.1175/2010JTECHA1375.1, 2011.
Raupach, T. H. and Berne, A.: Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference, Atmos. Meas. Tech., 8, 343–365, https://doi.org/10.5194/amt-8-343-2015, 2015.
Reges, H. W., Doesken, N., Turner, J., Newman, N., Bergantino, A., andSchwalbe, Z.: CoCoRaHS: The evolution and accomplishments of a volunteer rain gauge network, B. Am. Meteorol. Soc., 97, 1831–1846, https://doi.org/10.1175/BAMS-D-14-00213.1, 2016.
Schaer, M., Praz, C., and Berne, A.: Identification of blowing snow particles in images from a Multi-Angle Snowflake Camera, The Cryosphere, 14, 367–384, https://doi.org/10.5194/tc-14-367-2020, 2020.
Sicart, J. E., Pomeroy, J. W., Essery, R. L. H., and Bewley, D.: Incoming longwave radiation to melting snow: observations, sensitivity and estimation in Northern environments, Hydrol. Process., 20, 3697–3708, https://doi.org/10.1002/hyp.6383, 2006.
Skofronick-Jackson, G., Hudak, D., Petersen, W., Nesbitt, S. W., Chandrasekar, V., Durden, S., Gleicher, K. J., Huang, G. J., Joe, P., Kollias, P., Reed, K. A., Schwaller, M. R., Stewart, R., Tanelli, S., Tokay, A., Wang, J. R., and Wolde, M.: Global precipitation measurement cold season precipitation experiment (GCPEX): For measurement's sake, let it snow, B. Am. Meteorol. Soc., 96, 1719–1741, https://doi.org/10.1175/BAMS-D-13-00262.1, 2015.
Souverijns, N., Gossart, A., Lhermitte, S., Gorodetskaya, I. V., Kneifel, S., Maahn, M., Bliven, F. L., and van Lipzig, N. P. M.: Estimating radar reflectivity – Snowfall rate relationships and their uncertainties over Antarctica by combining disdrometer and radar observations. Atmos. Res., 196, 211–223, https://doi.org/10.1016/j.atmosres.2017.06.001, 2017.
Stewart, R. E.: Canadian Atlantic Storms Program: Progress and plans of the meteorological component, B. Am. Meteorol. Soc., 72, 364–371, https://doi.org/10.1175/1520-0477(1991)072<0364:CASPPA>2.0.CO;2, 1991.
Stewart, R. E., Shaw, R. W., and Isaac, G. A.: Canadian Atlantic Storms Program: The meteorological field project, B. Am. Meteorol. Soc., 68, 338–345, http://www.jstor.org/stable/26225054 (last access: 5 December 2023), 1987.
Thériault, J. M., Hung, I., Vaquer, P., Stewart, R. E., and Pomeroy, J. W.: Precipitation characteristics and associated weather conditions on the eastern slopes of the Canadian Rockies during March–April 2015, Hydrol. Earth Syst. Sci., 22, 4491–4512, https://doi.org/10.5194/hess-22-4491-2018, 2018.
Thériault, J. M., Déry, S. J., Pomeroy, J. W., Smith, H. M., Almonte, J., Bertoncini, A., Crawford, R. W., Desroches-Lapointe, A., Lachapelle, M., Mariani, Z., Mitchell, S., Morris, J. E., Hébert-Pinard, C., Rodriguez, P., and Thompson, H. D.: Meteorological observations collected during the Storms and Precipitation Across the continental Divide Experiment (SPADE), April–June 2019, Earth Syst. Sci. Data, 13, 1233–1249, https://doi.org/10.5194/essd-13-1233-2021, 2021a.
Thériault, J. M., Leroux, N. R., and Rasmussen, R. M.: Improvement of solid precipitation measurements using a hotplate precipitation gauge, J. Hydrometeorol., 22, 877–885, https://doi.org/10.1175/JHM-D-20-0168.1, 2021b.
Thériault, J. M., Leroux, N. R., Stewart, R. E., Bertoncini, A., Déry, S. J., Pomeroy, J. W., Thompson, H. D., Smith, H., Mariani, Z., Desroches-Lapointe, A., Mitchell, S., and Almonte, J.: Storms and Precipitation across the continental Divide Experiment (SPADE), B. Am. Meteorol. Soc., 103, E2628–E2649, https://doi.org/10.1175/BAMS-D-21-0146.1, 2022.
Thompson, H. D., Thériault, J. M., Déry, S. J., Stewart, R. E., Boisvert, D., Rickard, L., Leroux, N. R., Colli, M., and Vincent Vionnet, V.: Atmospheric and surface observation data collected during the Saint John River Experiment on Cold Season Storms, Federated Research Data Repository [data set], https://doi.org/10.20383/103.0591, 2023.
Tokay, A., Hartmann, P., Battaglia, A., Gage, K. S., Clark, W. L., and Williams, C. R.: A field study of reflectivity and Z-R relations using vertically pointing radars and disdrometers, J. Atmos. Ocean. Technol., 26, 1120–1134, https://doi.org/10.1175/2008JTECHA1163.1, 2009.
US Department of Energy: FLUXNET: The Data Portal Serving the FLUXNET Community, https://fluxnet.org/2017/10/10/toolbox-a-rolling-list-of-softwarepackages-for-flux-related-data-processing/ (last access: 31 August 2022), 2021.
Short summary
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.
The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick,...
Altmetrics
Final-revised paper
Preprint