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

Abstract km yields a slightly lower root mean square error (RMSE), and a better spread–skill than the strategy of assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modeling errors are the largest, e.g., the Haute-Ariège, Andorra, and the extreme southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses and, therefore, snow simulations. In situ HS observations thus show an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network ; volume:16 ; number:4 ; year:2022 ; pages:1281-1298 ; extent:18
The Cryosphere ; 16, Heft 4 (2022), 1281-1298 (gesamt 18)

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

DOI
10.5194/tc-16-1281-2022
URN
urn:nbn:de:101:1-2022041405233421309438
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:25 AM CEST

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Associated

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

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