Articles | Volume 18, issue 8
https://doi.org/10.5194/amt-18-1731-2025
© 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/amt-18-1731-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2025
Research article |
| 22 Apr 2025
| 22 Apr 2025
Radar-based high-resolution ensemble precipitation analyses over the French Alps
Matthieu Vernay
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France
Matthieu Lafaysse
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France
Clotilde Augros
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
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Saharan dust deposits frequently color alpine glaciers orange. Mineral dust reduces snow albedo and increases snow and glaciers melt rate. Using physical modeling, we quantified the impact of dust on the Argentière Glacier over the period 2019–2022. We found that that the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt. At specific locations, the impact of dust over one year can rise to an equivalent of 1 meter of melted ice.
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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.
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
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This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
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Atmos. Meas. Tech., 18, 3715–3745, https://doi.org/10.5194/amt-18-3715-2025, https://doi.org/10.5194/amt-18-3715-2025, 2025
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Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Zacharie Barrou Dumont, Simon Gascoin, Jordi Inglada, Andreas Dietz, Jonas Köhler, Matthieu Lafaysse, Diego Monteiro, Carlo Carmagnola, Arthur Bayle, Jean-Pierre Dedieu, Olivier Hagolle, and Philippe Choler
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We generated annual maps of snow melt-out days at 20 m resolution over a period of 38 years from 10 different satellites. This study fills a knowledge gap regarding the evolution of mountain snow in Europe by covering a much longer period and characterizing trends at much higher resolutions than previous studies. We found a trend for earlier melt-out with average reductions of 5.51 d per decade over the French Alps and 4.04 d per decade over the Pyrenees for the period 1986–2023.
Léon Roussel, Marie Dumont, Marion Réveillet, Delphine Six, Marin Kneib, Pierre Nabat, Kevin Fourteau, Diego Monteiro, Simon Gascoin, Emmanuel Thibert, Antoine Rabatel, Jean-Emmanuel Sicart, Mylène Bonnefoy, Luc Piard, Olivier Laarman, Bruno Jourdain, Mathieu Fructus, Matthieu Vernay, and Matthieu Lafaysse
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Saharan dust deposits frequently color alpine glaciers orange. Mineral dust reduces snow albedo and increases snow and glaciers melt rate. Using physical modeling, we quantified the impact of dust on the Argentière Glacier over the period 2019–2022. We found that that the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt. At specific locations, the impact of dust over one year can rise to an equivalent of 1 meter of melted ice.
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
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
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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.
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
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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.
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
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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.
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
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The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
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Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
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.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
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This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
Short summary
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The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
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.
Guillaume Evin, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo
Nonlin. Processes Geophys., 28, 467–480, https://doi.org/10.5194/npg-28-467-2021, https://doi.org/10.5194/npg-28-467-2021, 2021
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Forecasting the height of new snow is essential for avalanche hazard surveys, road and ski resort management, tourism attractiveness, etc. Météo-France operates a probabilistic forecasting system using a numerical weather prediction system and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
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In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Patrick Le Moigne, François Besson, Eric Martin, Julien Boé, Aaron Boone, Bertrand Decharme, Pierre Etchevers, Stéphanie Faroux, Florence Habets, Matthieu Lafaysse, Delphine Leroux, and Fabienne Rousset-Regimbeau
Geosci. Model Dev., 13, 3925–3946, https://doi.org/10.5194/gmd-13-3925-2020, https://doi.org/10.5194/gmd-13-3925-2020, 2020
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The study describes how a hydrometeorological model, operational at Météo-France, has been improved. Particular emphasis is placed on the impact of climatic data, surface, and soil parametrizations on the model results. Model simulations and evaluations carried out on a variety of measurements of river flows and snow depths are presented. All improvements in climate, surface data, and model physics have a positive impact on system performance.
Cited articles
Atencia, A., Wang, Y., Kann, A., and Meier, F.: Localization and flow-dependency on blending techniques, Meteorol. Z., 29, 231–246, https://doi.org/10.1127/metz/2019/0987, 2020a. a
Atencia, A., Wang, Y., Kann, A., and Wastl, C.: A probabilistic precipitation nowcasting system constrained by an Ensemble Prediction System., Meteorol. Z., 29, 183–202, https://doi.org/10.1127/metz/2020/1030, 2020b. a
Awasthi, S. and Varade, D.: Recent advances in the remote sensing of alpine snow: a review, GISci. Remote Sens., 58, 852–888, https://doi.org/10.1080/15481603.2021.1946938, 2021. a
Birman, C., Karbou, F., Mahfouf, J.-F., Lafaysse, M., Durand, Y., Giraud, G., Mérindol, L., and Hermozo, L.: Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations, J. Hydrometeorol., 18, 1425–1451, https://doi.org/10.1175/JHM-D-16-0144.1, 2017. a
Bouttier, F., Raynaud, L., Nuissier, O., and Ménétrier, B.: Sensitivity of the AROME ensemble to initial and surface perturbations during HyMeX, Q. J. Roy. Meteorol. Soc., 142, 390–403, https://doi.org/10.1002/qj.2622, 2016. a
Brier, G. W.: Verification of forecasts expressed in terms of probability, Mon. Weather Rev., 78, 1–3, https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2, 1950. a
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the forecast of convective activity from the AROME-France system, Q. J. Roy. Meteorol. Soc., 142, 2231–2243, https://doi.org/10.1002/qj.2822, 2016. a
Candille, G. and Talagrand, O.: Evaluation of probabilistic prediction systems for a scalar variable, Q. J. Roy. Meteorol. Soc., 131, 2131–2150, https://doi.org/10.1256/qj.04.71, 2005. a
Champeaux, J.-L., Dupuy, P., Laurantin, O., Soulan, I., Tabary, P., and Soubeyroux, J.-M.: Les mesures de précipitations et l'estimation des lames d'eau à Météo-France: état de l'art et perspectives, La Houille Blanche, Numero 5, 28–34, https://doi.org/10.1051/lhb/2009052, 2009. a, b
Clark, M. P. and Slater, A.: Probabilistic Quantitative Precipitation Estimation in Complex Terrain, J. Hydrometeorol., 7, 3–22, 2005. a
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745, 2011. a
Cluzet, B., Lafaysse, M., Cosme, E., Albergel, C., Meunier, L.-F., and Dumont, M.: CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework, Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, 2021. a, b, c
Cluzet, B., Lafaysse, M., Deschamps-Berger, C., Vernay, M., and Dumont, M.: Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network, The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, 2022. a, b, c, d
Dai, Q., Han, D., Rico-Ramirez, M., and Srivastava, P. K.: Multivariate distributed ensemble generator: A new scheme for ensemble radar precipitation estimation over temperate maritime climate, J. Hydrol., 511, 17–27, https://doi.org/10.1016/j.jhydrol.2014.01.016, 2014. a
DeMaria, M., Knaff, J. A., Knabb, R., Lauer, C., Sampson, C. R., and DeMaria, R. T.: A New Method for Estimating Tropical Cyclone Wind Speed Probabilities, Weather Forecast., 24, 1573–1591, https://doi.org/10.1175/2009WAF2222286.1, 2009. a
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020. a
Deschamps-Berger, C., Cluzet, B., Dumont, M., Lafaysse, M., Berthier, E., Fanise, P., and Gascoin, S.: Improving the Spatial Distribution of Snow Cover Simulations by Assimilation of Satellite Stereoscopic Imagery, Water Resour. Res., 58, e2021WR030271, https://doi.org/10.1029/2021WR030271, 2022. a, b, c, d
Durand, Y., Brun, E., Mérindol, L., Guyomarc'h, G., Lesaffre, B., and Martin, E.: A meteorological estimation of relevant parameters for snow models, Ann. Glaciol., 18, 65–71, 1993. a
Essery, R., Morin, S., Lejeune, Y., and B Ménard, C.: A comparison of 1701 snow models using observations from an alpine site, Adva. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013. a
Evensen, G.: The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Faure, D., Gaussiat, N., Dupuy, P., Delrieu, G., Yu, N., and Sarter, F.: Quality analysis of the 2016 quantitative precipitation estimates in the French Alps, 5 pp., https://ams.confex.com/ams/38RADAR/webprogram/Paper320389.html (last access: 17 April 2025), 2017. a
Faure, D., Delrieu, G., and Gaussiat, N.: Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps, Atmosphere, 10, 306, https://doi.org/10.3390/atmos10060306, 2019. a, b
Fierz, C., R.L, A., Y, D., P, E., Greene, E., Mcclung, D., Nishimura, K., Satyawali, P., and Sokratov, S.: The international classification for seasonal snow on the ground (UNESCO, IHP (International Hydrological Programme)–VII, Technical Documents in Hydrology, No 83; IACS (International Association of Cryospheric Sciences) contribution No 1), 90 pp., https://unesdoc.unesco.org/ark:/48223/pf0000186462?posInSet=1&queryId=7c665048-4e3f-464a-b282-66a33989ff00 (last access: 17 April 2025), 2009. a
Figueras i Ventura, J. and Tabary, P.: The New French Operational Polarimetric Radar Rainfall Rate Product, J. Appl. Meteorol. Climatol., 52, 1817–1835, https://doi.org/10.1175/JAMC-D-12-0179.1, 2013. a
Foehn, A., Hernandez, J. G., Schaefli, B., and De Cesare, G.: Spatial interpolation of precipitation from multiple rain gauge networks and weather radar data for operational applications in Alpine catchments, J. of Hydrol., 563, 1092–1110, https://doi.org/10.1016/j.jhydrol.2018.05.027, 2018. a
Foresti, L., Kanevski, M. F., and Pozdnoukhov, A.: Kernel-Based Mapping of Orographic Rainfall Enhancement in the Swiss Alps as Detected by Weather Radar, IEEE T. Geosci. Remote Sens., 50, 2954–2967, 2012. a
Foresti, L., Sideris, I. V., Panziera, L., Nerini, D., and Germann, U.: A 10-year radar-based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region, Q. J. Roy. Meteorol. Soc., 144, 2277–2301, https://doi.org/10.1002/qj.3364, 2018. a
Fortin, V., Roy, G., Donaldson, N., and Mahidjiba, A.: Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA), J. Hydrol., 531, 296–307, https://doi.org/10.1016/j.jhydrol.2015.08.003, 2015. a
Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., and Mahidjiba, A.: Ten years of science based on the Canadian Precipitation Analysis: A CaPA system overview and literature review, Atmos.-Ocean, 56, 178–196, https://doi.org/10.1080/07055900.2018.1474728, 2018. a
Frei, C. and Isotta, F. A.: Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps, J. Geophys. Res.-Atmos., 124, 5757–5778, 2019. a
Germann, U., Berenguer, M., Sempere-Torres, D., and Zappa, M.: REAL – Ensemble radar precipitation estimation for hydrology in a mountainous region, Q. J. Roy. Meteorol. Soc., 135, 445–456, https://doi.org/10.1002/qj.375, 2009. a
Germann, U., Boscacci, M., Clementi, L., Gabella, M., Hering, A., Sartori, M., Sideris, I. V., and Calpini, B.: Weather Radar in Complex Orography, Remote Sens., 14, 503, https://doi.org/10.3390/rs14030503, 2022. a, b, c, d
Ghaemi, E., Gabella, M., Foelsche, U., Sideris, I., and Nerini, D.: The effect of altitude on the uncertainty of radar-based precipitation estimates over Switzerland, Int. J. Remote Sens., 44, 2495–2517, https://doi.org/10.1080/01431161.2023.2203339, 2023. a
Haddjeri, A., Baron, M., Lafaysse, M., Le Toumelin, L., Deschamp-Berger, C., Vionnet, V., Gascoin, S., Vernay, M., and Dumont, M.: Exploring the sensitivity to precipitation, blowing snow, and horizontal resolution of the spatial distribution of simulated snow cover, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2604, 2023. a, b, c, d, e
Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., and Gruber, C.: The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region, Weather Forecast., 26, 166–183, https://doi.org/10.1175/2010WAF2222451.1, 2011. a, b
Hopson, T. M.: Assessing the Ensemble Spread–Error Relationship, Mon. Weather Rev., 142, 1125–1142, https://doi.org/10.1175/MWR-D-12-00111.1, 2014. a, b
Hotta, D. and Ota, Y.: Why does EnKF suffer from analysis overconfidence? An insight into exploiting the ever-increasing volume of observations, Q. J. Roy. Meteorol. Soc., 147, 1258–1277, https://doi.org/10.1002/qj.3970, 2021. a
IPCC: High Mountain Areas, p. 131–202, Cambridge University Press, https://doi.org/10.1017/9781009157964.004, 2022. a
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems, J. Basic Eng., 82, 35–45, https://doi.org/10.1115/1.3662552, 1960. a
Khedhaouiria, D., Bélair, S., Fortin, V., Roy, G., and Lespinas, F.: Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis, Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022, 2022. a
Kirstetter, P.-E., Delrieu, G., Boudevillain, B., and Obled, C.: Toward an error model for radar quantitative precipitation estimation in the Cévennes–Vivarais region, France, J. Hydrol., 394, 28–41, https://doi.org/10.1016/j.jhydrol.2010.01.009, 2010. a
Kirstetter, P.-E., Gourley, J. J., Hong, Y., Zhang, J., Moazamigoodarzi, S., Langston, C., and Arthur, A.: Probabilistic precipitation rate estimates with ground-based radar networks, Water Resour. Res., 51, 1422–1442, https://doi.org/10.1002/2014WR015672, 2015. a, b
Kochendorfer, J., Earle, M. E., Hodyss, D., Reverdin, A., Roulet, Y.-A., Nitu, R., Rasmussen, R., Landolt, S., Buisán, S., and Laine, T.: Undercatch Adjustments for Tipping-Bucket Gauge Measurements of Solid Precipitation, J. Hydrometeorol., 21, 1193–1205, https://doi.org/10.1175/JHM-D-19-0256.1, 2020. a, b
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a
Lafaysse, M., Cluzet, B., Dumont, M., Lejeune, Y., Vionnet, V., and Morin, S.: A multiphysical ensemble system of numerical snow modelling, The Cryosphere, 11, 1173–1198, https://doi.org/10.5194/tc-11-1173-2017, 2017. a
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S., Cosme, E., Jonas, T., Winstral, A., and Margulis, S. A.: Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review, Front. Earth Sci., 8, 325, https://doi.org/10.3389/feart.2020.00325, 2020. a
Le Bastard, T., Caumont, O., Gaussiat, N., and Karbou, F.: Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept, Atmos. Meas. Tech., 12, 5669–5684, https://doi.org/10.5194/amt-12-5669-2019, 2019. a
Lespinas, F., Fortin, V., Roy, G., Rasmussen, P., and Stadnyk, T.: Performance Evaluation of the Canadian Precipitation Analysis (CaPA), J. Hydrometeorol., 16, 2045–2064, https://doi.org/10.1175/JHM-D-14-0191.1, 2015. a, b
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our Skill in Modeling Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks, B. Am. Meteorol. Soc., 100, 2473–2490, https://doi.org/10.1175/BAMS-D-19-0001.1, 2019. a, b, c
Magnusson, J., Winstral, A., Stordal, A. S., Essery, R., and Jonas, T.: Improving physically based snow simulations by assimilating snow depths using the particle filter, Water Resour. Res., 53, 1125–1143, https://doi.org/10.1002/2016WR019092, 2017. a
Marshall, J. S. and Palmer, W. M. K.: THE DISTRIBUTION OF RAINDROPS WITH SIZE, J. Atmos. Sci., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2, 1948. a
Matheson, J. E. and Winkler, R. L.: Scoring Rules for Continuous Probability Distributions, Manage. Sci., 22, 1087–1096, https://doi.org/10.1287/mnsc.22.10.1087, 1976. a, b
McRoberts, D. B. and Nielsen-Gammon, J. W.: Detecting Beam Blockage in Radar-Based Precipitation Estimates, J. Atmos. Ocean. Technol., 34, 1407–1422, https://doi.org/10.1175/JTECH-D-16-0174.1, 2017. a
Météo-France: AROME NWP model, Météo-France [data set], https://portail-api.meteofrance.fr/web/fr/api/AROME (last access: 17 April 2025), 2025a. a
Météo-France: Ensemble AROME NWP model, Météo-France [data set], https://portail-api.meteofrance.fr/web/fr/api/PE-AROME (last access: 17 April 2025), 2025b. a
Météo-France: In-situ observations, Météo-France [data set], https://portail-api.meteofrance.fr/web/fr/api/DonneesPubliquesObservation (last access: 17 April 2025), 2025c. a
Météo-France: Radar data, Météo-France [data set], https://portail-api.meteofrance.fr/web/fr/api/DonneesPubliquesRadar (last access: 17 April 2025), 2025d. a
Météo-France: SAFRAN data, Météo-France [data set], https://www.aeris-data.fr/en/landing-page/?uuid=865730e8-edeb-4c6b-ae58-80f95166509b (last access: 17 April 2025), 2025e. a
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet, A., Hagenmuller, P., Lafaysse, M., Ližar, M., Mitterer, C., Monti, F., Müller, K., Olefs, M., Snook, J. S., van Herwijnen, A., and Vionnet, V.: Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future, Cold Reg. Sci. Technol., 170, 102910, https://doi.org/10.1016/j.coldregions.2019.102910, 2020. a
Mott, R., Winstral, A., Cluzet, B., Helbig, N., Magnusson, J., Mazzotti, G., Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: Operational snow-hydrological modeling for Switzerland, Front. Earth Sci., 11, 2296–6463, https://doi.org/10.3389/feart.2023.1228158, 2023. a
Nerini, D., Foresti, L., Leuenberger, D., Robert, S., and Germann, U.: A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles, Mon. Weather Rev., 147, 987–1006, https://doi.org/10.1175/MWR-D-18-0258.1, 2019. a
Olefs, M., Schöner, W., Suklitsch, M., Wittmann, C., Niedermoser, B., Neururer, A., and Wurzer, A.: SNOWGRID – A New Operational Snow Cover Model in Austria, 8 pp., https://api.semanticscholar.org/CorpusID:55519841 (last access: 17 April 2025), 2013. a
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. a, b
Schefzik, R., Thorarinsdottir, T. L., and Gneiting, T.: Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling, Stat. Sci., 28, 616–640, https://doi.org/10.1214/13-STS443, 2013. a, b
Schirmer, M. and Jamieson, B.: Verification of analysed and forecasted winter precipitation in complex terrain, The Cryosphere, 9, 587–601, https://doi.org/10.5194/tc-9-587-2015, 2015. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model, Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Sideris, I. V., Gabella, M., Erdin, R., and Germann, U.: Real-time radar–rain-gauge merging using spatio-temporal co-kriging with external drift in the alpine terrain of Switzerland, Q. J. Roy. Meteorol. Soc., 140, 1097–1111, https://doi.org/10.1002/qj.2188, 2014. a
Sideris, I. V., Foresti, L., Nerini, D., and Germann, U.: NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland, Q. J. Roy. Meteorol. Soc., 146, 1768–1800, https://doi.org/10.1002/qj.3766, 2020. a
Sivasubramaniam, K., Sharma, A., and Alfredsen, K.: Merging radar and gauge information within a dynamical model combination framework for precipitation estimation in cold climates, Environ. Modell. Softw., 119, 99–110, https://doi.org/10.1016/j.envsoft.2019.05.013, 2019. a
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to High-Dimensional Particle Filtering, Mon. Weather Rev., 136, 4629– 4640, https://doi.org/10.1175/2008MWR2529.1, 2008. a
Soci, C., Bazile, E., Besson, F., and Landelius, T.: High-resolution precipitation re-analysis system for climatological purposes, Tellus A, 68, 29879, https://doi.org/10.3402/tellusa.v68.29879, 2016. a
Tabary, P.: The New French Operational Radar Rainfall Product. Part I: Methodology, Weather Forecast., 22, 393–408, https://doi.org/10.1175/WAF1004.1, 2007. a
Taillardat, M. and Mestre, O.: From research to applications – examples of operational ensemble post-processing in France using machine learning, Nonlin. Processes Geophys., 27, 329–347, https://doi.org/10.5194/npg-27-329-2020, 2020. a, b
Taillardat, M., Fougères, A.-L., Naveau, P., and Mestre, O.: Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting, Weather Forecast., 34, 617–634, https://doi.org/10.1175/WAF-D-18-0149.1, 2019. a
Thornton, J. M., Pepin, N., Shahgedanova, M., and Adler, C.: Coverage of In Situ Climatological Observations in the World's Mountains, Front. Climate, 4, 2624–9553, https://doi.org/10.3389/fclim.2022.814181, 2022. a, b
van Leeuwen, P. J.: Particle Filtering in Geophysical Systems, Mon. Weather Rev., 137, 4089–4114, https://doi.org/10.1175/2009MWR2835.1, 2009. a
vernaym: vernaym/Codes_These: Vernay et al. 2025 (AMT) (Vernay2025_AMT), Zenodo [software], https://doi.org/10.5281/zenodo.15210095, 2025. a
Vernay, M., Lafaysse, M., Mérindol, L., Giraud, G., and Morin, S.: Ensemble forecasting of snowpack conditions and avalanche hazard, Cold Reg. Sci. Technol., 120, 0165-232X, https://doi.org/10.1016/j.coldregions.2015.04.010, 2015. a
Villarini, G., Seo, B.-C., Serinaldi, F., and Krajewski, W. F.: Spatial and temporal modeling of radar rainfall uncertainties, Atmos. Res., 135–136, 91–101, https://doi.org/10.1016/j.atmosres.2013.09.007, 2014. a, b
Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Quéno, L., Seity, Y., and Bazile, E.: Numerical Weather Forecasts at Kilometer Scale in the French Alps: Evaluation and Application for Snowpack Modeling, J. Hydrometeorol., 17, 2591–2614, https://doi.org/10.1175/JHM-D-15-0241.1, 2016. a
Vionnet, V., Six, D., Auger, L., Dumont, M., Lafaysse, M., Quéno, L., Réveillet, M., Dombrowski-Etchevers, I., Thibert, E., and Vincent, C.: Sub-kilometer Precipitation Datasets for Snowpack and Glacier Modeling in Alpine Terrain, Front. Earth Sci., 7, 2296–6463, https://doi.org/10.3389/feart.2019.00182, 2019. a
Vionnet, V., Verville, M., Fortin, V., Brugman, M., Abrahamowicz, M., Lemay, F., Thériault, J. M., Lafaysse, M., and Milbrandt, J. A.: Snow level from post-processing of atmospheric model improves snowfall estimate and snowpack prediction in mountains, Water Resour. Res., 58, e2021WR031778, https://doi.org/10.1029/2021WR031778, 2022. a
Vogl, S., Laux, P., Qiu, W., Mao, G., and Kunstmann, H.: Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields, Hydrol. Earth Syst. Sci., 16, 2311–2328, https://doi.org/10.5194/hess-16-2311-2012, 2012. a
Yu, N., Gaussiat, N., and Tabary, P.: Polarimetric X-band weather radars for quantitative precipitation estimation in mountainous regions, Q. J. Roy. Meteorol. Soc., 144, 2603–2619, https://doi.org/10.1002/qj.3366, 2018. a
Short summary
This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.
This paper provides a comprehensive evaluation of the quality of radar-based precipitation...
