Asynchronous data assimilation with the EnKF in presence of additive model error

Autor:	Pavel Sakov, Marc Bocquet
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	data assimilation ensemble Kalman filter asynchronous data assimilation model error additive model error Oceanography GC1-1581 Meteorology. Climatology QC851-999
Zdroj:	Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 70, Iss 1, Pp 1-7 (2018)
Druh dokumentu:	article
ISSN:	1600-0870 16000870
DOI:	10.1080/16000870.2017.1414545
Popis:	The term ‘asynchronous data assimilation’ (ADA) refers to modifications of sequential data assimilation methods that take into consideration the observation time. In Sakov et al. [Tellus A, 62, 24–29 (2010)], a simple rule has been formulated for the ADA with the ensemble Kalman filter (EnKF). To assimilate scattered in time observations, one needs to calculate ensemble forecast observations using the forecast ensemble at observation time. Using then these ensemble observations in the EnKF update matches the optimal analysis in the linear perfect model case. In this note, we generalise this rule for the case of additive model error.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/0f7e81e6051e4771b9ff44c1f86d5b6e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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