Evaluating forecasting performance for data assimilation methods: the ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

Gift Dumedah; Paulin Coulibaly

doi:10.1016/j.advwatres.2013.07.007

Evaluating forecasting performance for data assimilation methods: the ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

Gift Dumedah
, Paulin Coulibaly

Civil & Environmental Engineering

Research output: Contribution to journal › Article › Research › peer-review

38 Citations (Scopus)

Original language	English
Pages (from-to)	47 - 63
Number of pages	17
Journal	Advances in Water Resources
Volume	60
DOIs	https://doi.org/10.1016/j.advwatres.2013.07.007
Publication status	Published - 2013

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10.1016/j.advwatres.2013.07.007

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title = "Evaluating forecasting performance for data assimilation methods: the ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation",

author = "Gift Dumedah and Paulin Coulibaly",

year = "2013",

doi = "10.1016/j.advwatres.2013.07.007",

language = "English",

volume = "60",

pages = "47 -- 63",

journal = "Advances in Water Resources",

issn = "0309-1708",

publisher = "Elsevier",

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T1 - Evaluating forecasting performance for data assimilation methods: the ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

AU - Dumedah, Gift

AU - Coulibaly, Paulin

PY - 2013

Y1 - 2013

UR - http://dx.doi.org/10.1016/j.advwatres.2013.07.007

U2 - 10.1016/j.advwatres.2013.07.007

DO - 10.1016/j.advwatres.2013.07.007

M3 - Article

SN - 0309-1708

VL - 60

SP - 47

EP - 63

JO - Advances in Water Resources

JF - Advances in Water Resources

ER -

Evaluating forecasting performance for data assimilation methods: the ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

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