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.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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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)
- Urheber
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Cluzet, Bertrand
Lafaysse, Matthieu
Deschamps-Berger, César
Vernay, Matthieu
Dumont, Marie
- DOI
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10.5194/tc-16-1281-2022
- URN
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urn:nbn:de:101:1-2022041405233421309438
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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2025-08-15T07:25:51+0200
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Beteiligte
- Cluzet, Bertrand
- Lafaysse, Matthieu
- Deschamps-Berger, César
- Vernay, Matthieu
- Dumont, Marie
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