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| Titel |
A non-parametric hidden Markov model for climate state identification |
| VerfasserIn |
M. F. Lambert, J. P. Whiting, A. V. Metcalfe |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 7, no. 5 ; Nr. 7, no. 5, S.652-667 |
| Datensatznummer |
250004787
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| Publikation (Nr.) |
 copernicus.org/hess-7-652-2003.pdf |
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| Zusammenfassung |
| Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the
climate without the need to simulate supplementary climate variables. The fitting of a
parametric HMM relies upon assumptions for the state conditional distributions. It is
shown that inappropriate assumptions about state conditional distributions can lead to
biased estimates of state transition probabilities. An alternative non-parametric model
with a hidden state structure that overcomes this problem is described. It is shown that
a two-state non-parametric model produces accurate estimates of both transition
probabilities and the state conditional distributions. The non-parametric model can be
used directly or as a technique for identifying appropriate state conditional distributions
to apply when fitting a parametric HMM. The non-parametric model is fitted to data from
ten rainfall stations and four streamflow gauging stations at varying distances inland
from the Pacific coast of Australia. Evidence for hydrological persistence, though not
mathematical persistence, was identified in both rainfall and streamflow records, with the
latter showing hidden states with longer sojourn times. Persistence appears to increase
with distance from the coast.
Keywords: Hidden Markov models, non-parametric, two-state model, climate states,
persistence, probability distributions |
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