The University of Southampton
University of Southampton Institutional Repository

A Bayesian normal homogeneity test for the detection of artificial discontinuities in climatic series

A Bayesian normal homogeneity test for the detection of artificial discontinuities in climatic series
A Bayesian normal homogeneity test for the detection of artificial discontinuities in climatic series
A Bayesian Normal Homogeneity Test (BNHT) for the detection of artificial discontinuities in climatic series is presented. The test is simple to use and allows the integration of prior knowledge on the date of change from various sources of information (e.g. metadata or expert belief) in the analysis. The performance of the new test was evaluated on synthetic series with similar statistical properties as observed total annual precipitation in the southern and central parts of the province of Quebec, Canada. Different priors were used to investigate the sensitivity of the test to the choice of priors. It was found that (1) high-prior probability of no change yields low false detection rates on the homogeneous series; (2) the test has a very high power of detection on series with a single shift (the best power of detection if compared with previous methods applied to the same synthetic series); (3) shifts having a small magnitude are detectable with a low prior probability of no change and (4) when applied to series with multiple shifts with a segmentation procedure and a high probability of no change, the test proved to be performing well in detecting multiple shifts (as performing as the best techniques previously applied to the same synthetic series). An example of application to total annual precipitation in Quebec City, Canada is also presented to illustrate (1) a case for which the results are not affected by the choice of the prior parameters and (2) a case for which information about potential changes found in the metadata was integrated in the analysis and allowed the detection of a change that would not have been detected with a non-informative prior.
homogenisation, climatic series, bayesian technique, changepoint, exponential family, normal distribution, metadata
2342-2357
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Ouarda, Taha B.M.J.
33662875-c39e-42e9-8b21-9b1452d5d596
Seidou, Ousmane
68a09e6d-e707-4156-a3ef-2c1ed3819898
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Ouarda, Taha B.M.J.
33662875-c39e-42e9-8b21-9b1452d5d596
Seidou, Ousmane
68a09e6d-e707-4156-a3ef-2c1ed3819898

Beaulieu, Claudie, Ouarda, Taha B.M.J. and Seidou, Ousmane (2010) A Bayesian normal homogeneity test for the detection of artificial discontinuities in climatic series. International Journal of Climatology, 30 (15), 2342-2357. (doi:10.1002/joc.2056).

Record type: Article

Abstract

A Bayesian Normal Homogeneity Test (BNHT) for the detection of artificial discontinuities in climatic series is presented. The test is simple to use and allows the integration of prior knowledge on the date of change from various sources of information (e.g. metadata or expert belief) in the analysis. The performance of the new test was evaluated on synthetic series with similar statistical properties as observed total annual precipitation in the southern and central parts of the province of Quebec, Canada. Different priors were used to investigate the sensitivity of the test to the choice of priors. It was found that (1) high-prior probability of no change yields low false detection rates on the homogeneous series; (2) the test has a very high power of detection on series with a single shift (the best power of detection if compared with previous methods applied to the same synthetic series); (3) shifts having a small magnitude are detectable with a low prior probability of no change and (4) when applied to series with multiple shifts with a segmentation procedure and a high probability of no change, the test proved to be performing well in detecting multiple shifts (as performing as the best techniques previously applied to the same synthetic series). An example of application to total annual precipitation in Quebec City, Canada is also presented to illustrate (1) a case for which the results are not affected by the choice of the prior parameters and (2) a case for which information about potential changes found in the metadata was integrated in the analysis and allowed the detection of a change that would not have been detected with a non-informative prior.

Full text not available from this repository.

More information

e-pub ahead of print date: 24 December 2009
Published date: December 2010
Keywords: homogenisation, climatic series, bayesian technique, changepoint, exponential family, normal distribution, metadata
Organisations: Ocean and Earth Science

Identifiers

Local EPrints ID: 352257
URI: http://eprints.soton.ac.uk/id/eprint/352257
PURE UUID: fd0de6ac-c0c3-4f05-870f-4de6bcd9a05b

Catalogue record

Date deposited: 08 May 2013 10:21
Last modified: 16 Jul 2019 21:34

Export record

Altmetrics

Contributors

Author: Taha B.M.J. Ouarda
Author: Ousmane Seidou

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×