Articles | Volume 16, issue 4
https://doi.org/10.5194/tc-16-1281-2022
https://doi.org/10.5194/tc-16-1281-2022
Research article
 | 
11 Apr 2022
Research article |  | 11 Apr 2022

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont

Related authors

High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-374,https://doi.org/10.5194/essd-2024-374, 2024
Preprint under review for ESSD
Short summary
Snow redistribution in an intermediate-complexity snow hydrology modelling framework
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024,https://doi.org/10.5194/tc-18-3533-2024, 2024
Short summary
Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models
Bertrand Cluzet, Jan Magnusson, Louis Quéno, Giulia Mazzotti, Rebecca Mott, and Tobias Jonas
EGUsphere, https://doi.org/10.5194/egusphere-2024-209,https://doi.org/10.5194/egusphere-2024-209, 2024
Short summary
Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests
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
Short summary
Canopy structure, topography, and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023,https://doi.org/10.5194/hess-27-2099-2023, 2023
Short summary

Related subject area

Discipline: Snow | Subject: Seasonal Snow
Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?
Shirui Yan, Yang Chen, Yaliang Hou, Kexin Liu, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, Wei Pu, and Xin Wang
The Cryosphere, 18, 4089–4109, https://doi.org/10.5194/tc-18-4089-2024,https://doi.org/10.5194/tc-18-4089-2024, 2024
Short summary
Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps
Matthew Switanek, Gernot Resch, Andreas Gobiet, Daniel Günther, Christoph Marty, and Wolfgang Schöner
EGUsphere, https://doi.org/10.5194/egusphere-2024-1172,https://doi.org/10.5194/egusphere-2024-1172, 2024
Short summary
Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?
Benjamin Poschlod and Anne Sophie Daloz
The Cryosphere, 18, 1959–1981, https://doi.org/10.5194/tc-18-1959-2024,https://doi.org/10.5194/tc-18-1959-2024, 2024
Short summary
Characterization of Non-Gaussianity in the Snow Distributions of Various Landscapes
Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão
EGUsphere, https://doi.org/10.5194/egusphere-2024-395,https://doi.org/10.5194/egusphere-2024-395, 2024
Short summary
Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024,https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary

Cited articles

Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, https://doi.org/10.1016/j.advwatres.2005.08.004, 2006. a
Atger, F.: The skill of ensemble prediction systems, Mon. Weather Rev., 127, 1941–1953, https://doi.org/10.1175/1520-0493(1999)127<1941:TSOEPS>2.0.CO;2, 1999. a
Bellier, J., Zin, I., and Bontron, G.: Sample stratification in verification of ensemble forecasts of continuous scalar variables: Potential benefits and pitfalls, Mon. Weather Rev., 145, 3529–3544, https://doi.org/10.1175/MWR-D-16-0487.1, 2017. a, b
Bengtsson, T., Bickel, P., and Li, B.: Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems, in: Probability and Statistics: Essays in Honor of David A. Freedman, edited by: Nolan, D. and Speed, T., Volume 2 of Collections, Institute of Mathematical Statistics, Beachwood, Ohio, USA, pp. 316–334, https://doi.org/10.1214/193940307000000518, 2008. 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
Download
Short summary
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.