Dynamic harmonic regression and irregular sampling; avoiding pre-processing and minimising modelling assumptions

Mindham, David and Tych, Wlodek (2019) Dynamic harmonic regression and irregular sampling; avoiding pre-processing and minimising modelling assumptions. Environmental Modelling and Software, 121: 104503. ISSN 1364-8152

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Abstract

Many environmental time-series measurements are characterised by irregular sampling. A significant improvement of the Dynamic Harmonic Regression (DHR) modelling technique to accommodate irregular sampled time-series, without the need for data pre-processing, has been developed. Taylor's series is used to obtain the time-step state increments, modifying the transition equation matrices. This allows the user to avoid artefacts arising and insertion of assumptions from interpolation and regularisation of the data to a regular time-base and makes DHR more consistent with the Data-Based Mechanistic approach to modelling environmental systems. The new technique implemented as a Matlab package has been tested on demanding simulated data-sets and demonstrated on various environmental time-series data with significantly varying sampling times. The results have been compared with standard DHR, where possible, and the method reduces analysis time and produces unambiguous results (by removing the need for pre-processing – always based on assumptions) based only on the observed environmental data.

Item Type:

Journal Article

Journal or Publication Title:

Environmental Modelling and Software

Additional Information:

This is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software, 121, 2019 DOI: 10.1016/j.envsoft.2019.104503

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/2300/2301

Subjects:

?? time seriesharmonic regressionarbitrary samplingkalman filterclimatologypaleo-climatic dataproxy seriesdata based mechanistic modellingenvironmental science (miscellaneous)general mathematicsgeneral environmental scienceecological modellingenvironmental s ??

Departments:

Faculty of Science and Technology > Lancaster Environment Centre

ID Code:

138014

Deposited By:

ep_importer_pure

Deposited On:

17 Oct 2019 13:20

Refereed?:

Yes

Published?:

Published