The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence
Open accessAbstract
SUMMARY: A non-homogeneous hidden Markov model is proposed for relating precipitation occurrences at multiple rain-gauge stations to broad scale atmospheric circulation patterns (the so-called ‘downscaling problem’). We model a 15-year sequence of winter data from 30 rain stations in south-western Australia. The first 10 years of data are used for model development and the remaining 5 years are used for model evaluation. The fitted model accurately reproduces the observed rainfall statistics in the reserved data despite a shift in atmospheric circulation (and, consequently, rainfall) between the two periods. The fitted model also provides some useful insights into the processes driving rainfall in this region.
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Cites
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Hidden Markov Models for Speech Recognition
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Cited by
Estimating and Penalizing Preference Shift in Recommender Systems
Carroll, M., Hadfield-Menell, D., Russell, S., & Dragan, A. (2021) · Fifteenth ACM Conference on Recommender Systems · doi:10.1145/3460231.3478849 · Cited by 20
Metadata
- Authors
- Hughes, J. P., Guttorp, P., & Charles, S. P.
- Venue
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-03-01
- Volume
- 48
- Issue
- 1
- Pages
- 15-30
- ISSN
- 0035-9254 (ISSN-L)
- 1467-9876
- OpenAlex
- W2030520626
- Cited by
- 403
- References
- 17
- FWCI
- 6.85
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Hughes, J. P., Guttorp, P., & Charles, S. P. (1999). A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence. Journal of the Royal Statistical Society Series C: Applied Statistics. https://doi.org/10.1111/1467-9876.00136
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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