Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset

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dc.contributor.authorLiu, Hongli
dc.contributor.authorThiboult, Antoine
dc.contributor.authorTolson, Bryan A.
dc.contributor.authorAnctil, François
dc.contributor.authorMai, Juliane
dc.date.accessioned2019-02-05T17:20:38Z
dc.date.available2019-02-05T17:20:38Z
dc.date.issued2019-01
dc.descriptionThe final publication is available at Elsevier via https://doi.org/10.1016/j.jhydrol.2018.11.047. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.description.abstractSuccessful data assimilation depends on the accurate estimation of forcing data uncertainty. Forcing data uncertainty is typically estimated based on statistical error models. In practice, the hyper-parameters of statistical error models are often estimated by a trial-and-error tuning process, requiring significant analyst and computational time. To improve the efficiency of forcing data uncertainty estimation, this study proposes the direct use of existing ensemble climate products to represent climate data uncertainty in the ensemble Kalman filter (EnKF) of flow forecasting. Specifically, the Newman et al. (2015) dataset (N15 for short), covering the contiguous United States, northern Mexico, and southern Canada, is used here to generate the precipitation and temperature ensemble in the EnKF application. This study for the first time compares the N15 generated climate ensemble with the carefully tuned hyper-parameters generated climate ensemble in a real flow forecasting framework. The forecast performance comparison of 20 Québec catchments shows that the N15 generated climate ensemble yields improved or similar deterministic and probabilistic flow forecasts relative to the carefully tuned hyper-parameters generated climate ensemble. Improvements are most evident for short lead times (i.e., 1–3 days) when the influence of data assimilation dominates. However, the analysis and computational time required to use N15 is much less compared to the typical trial-and-error hyper-parameter tuning process.en
dc.description.sponsorshipNatural Sciences and Engineering Research Council [NETGP 451456]en
dc.identifier.urihttps://doi.org/10.1016/j.jhydrol.2018.11.047
dc.identifier.urihttp://hdl.handle.net/10012/14458
dc.language.isoenen
dc.publisherElsevieren
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectClimate data uncertaintyen
dc.subjectHyper-parameter tuningen
dc.subjectEnsemble Kalman filter (EnKF)en
dc.subjectShort-term ensemble flow forecastingen
dc.subjectNewman et al. (2015) dataseten
dc.titleEfficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataseten
dc.typeArticleen
dcterms.bibliographicCitationLiu, H., Thiboult, A., Tolson, B., Anctil, F., Mai, J., Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset, Journal of Hydrology (2018), doi: https://doi.org/10.1016/j.jhydrol.2018.11.047en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Civil and Environmental Engineeringen
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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