Articles | Volume 16, issue 4
The Cryosphere, 16, 1281–1298, 2022
The Cryosphere, 16, 1281–1298, 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 et al.

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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,, 2006. a
Atger, F.: The skill of ensemble prediction systems, Mon. Weather Rev., 127, 1941–1953,<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,, 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,, 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,, 2017. a
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.