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Intercomparison of homogenization techniques for precipitation data continued: comparison of two recent Bayesian change point models

Intercomparison of homogenization techniques for precipitation data continued: comparison of two recent Bayesian change point models
Intercomparison of homogenization techniques for precipitation data continued: comparison of two recent Bayesian change point models
In this paper, two new Bayesian change point techniques are described and compared to eight other techniques presented in previous work to detect inhomogeneities in climatic series. An inhomogeneity can be defined as a change point (a time point in a series such that the observations have a different distribution before and after this time) in the data series induced from changes in measurement conditions at a given station. It is important to be able to detect and correct an inhomogeneity, as it can interfere with the real climate change signal. The first technique is a Bayesian method of multiple change point detection in a multiple linear regression. The second one allows the detection of a single change point in a multiple linear regression. These two techniques have never been used for homogenization purposes. The ability of the two techniques to discriminate homogeneous and inhomogeneous series was evaluated using simulated data series. Various sets of synthetic series (homogeneous, with a single shift, and with multiple shifts) representing the typical total annual precipitation observed in the southern and central parts of the province of Quebec, Canada, and nearby areas were generated for the purpose of this study. The two techniques gave small false detection rates on the homogeneous series. Furthermore, the two techniques proved to be efficient for the detection of a single shift in a series. For the series with multiple shifts, the Bayesian method of multiple change point detection performed better. An application to a real data set is also provided and validated with the available metadata.
homogenization, bayesian multiple linear regression, multiple shifts, precipitation series, change point
0043-1397
W08410
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Seidou, Ousmane
68a09e6d-e707-4156-a3ef-2c1ed3819898
Ouarda, Taha B.M.J.
33662875-c39e-42e9-8b21-9b1452d5d596
Zhang, Xuebin
4bb0c0b5-f0b7-4ccf-b41a-d96cb56714a9
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Seidou, Ousmane
68a09e6d-e707-4156-a3ef-2c1ed3819898
Ouarda, Taha B.M.J.
33662875-c39e-42e9-8b21-9b1452d5d596
Zhang, Xuebin
4bb0c0b5-f0b7-4ccf-b41a-d96cb56714a9

Beaulieu, Claudie, Seidou, Ousmane, Ouarda, Taha B.M.J. and Zhang, Xuebin (2009) Intercomparison of homogenization techniques for precipitation data continued: comparison of two recent Bayesian change point models. Water Resources Research, 45 (8), W08410. (doi:10.1029/2008WR007501).

Record type: Article

Abstract

In this paper, two new Bayesian change point techniques are described and compared to eight other techniques presented in previous work to detect inhomogeneities in climatic series. An inhomogeneity can be defined as a change point (a time point in a series such that the observations have a different distribution before and after this time) in the data series induced from changes in measurement conditions at a given station. It is important to be able to detect and correct an inhomogeneity, as it can interfere with the real climate change signal. The first technique is a Bayesian method of multiple change point detection in a multiple linear regression. The second one allows the detection of a single change point in a multiple linear regression. These two techniques have never been used for homogenization purposes. The ability of the two techniques to discriminate homogeneous and inhomogeneous series was evaluated using simulated data series. Various sets of synthetic series (homogeneous, with a single shift, and with multiple shifts) representing the typical total annual precipitation observed in the southern and central parts of the province of Quebec, Canada, and nearby areas were generated for the purpose of this study. The two techniques gave small false detection rates on the homogeneous series. Furthermore, the two techniques proved to be efficient for the detection of a single shift in a series. For the series with multiple shifts, the Bayesian method of multiple change point detection performed better. An application to a real data set is also provided and validated with the available metadata.

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More information

e-pub ahead of print date: 11 August 2009
Published date: August 2009
Keywords: homogenization, bayesian multiple linear regression, multiple shifts, precipitation series, change point
Organisations: Ocean and Earth Science

Identifiers

Local EPrints ID: 352261
URI: https://eprints.soton.ac.uk/id/eprint/352261
ISSN: 0043-1397
PURE UUID: 32f4e0ea-ccb4-4b8b-96c7-bee60006d386

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Date deposited: 08 May 2013 10:34
Last modified: 18 Jul 2017 04:15

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