IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v152y2020ics0167947320301365.html
   My bibliography  Save this article

An advanced hidden Markov model for hourly rainfall time series

Author

Listed:
  • Stoner, Oliver
  • Economou, Theo

Abstract

The hidden Markov framework is adapted to construct a compelling model for simulation of sub-daily rainfall, capable of capturing important characteristics of sub-daily rainfall well, including: long dry periods or droughts; seasonal and temporal variation in occurrence and intensity; and propensity for extreme values. These adaptations include both clone states and temporally non-homogeneous state persistence probabilities. Set in the Bayesian framework, a rich quantification of parametric and predictive uncertainty is available, and thorough model checking is made possible through posterior predictive analyses. Results from the model are highly interpretable, allowing for meaningful examination of diurnal, seasonal and annual variation in sub-daily rainfall occurrence and intensity. To demonstrate the effectiveness of this approach, both in terms of model fit and interpretability, the model is applied to an 8-year long time series of hourly observations.

Suggested Citation

  • Stoner, Oliver & Economou, Theo, 2020. "An advanced hidden Markov model for hourly rainfall time series," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:csdana:v:152:y:2020:i:c:s0167947320301365
    DOI: 10.1016/j.csda.2020.107045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947320301365
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2020.107045?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wood, Simon N., 2016. "Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i07).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Benny Ren & Ian Barnett, 2023. "Combining mixed effects hidden Markov models with latent alternating recurrent event processes to model diurnal active–rest cycles," Biometrics, The International Biometric Society, vol. 79(4), pages 3402-3417, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shaun R. Seaman & Pantelis Samartsidis & Meaghan Kall & Daniela De Angelis, 2022. "Nowcasting COVID‐19 deaths in England by age and region," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1266-1281, November.
    2. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
    3. Anna Vážná & Jana Vignerová & Marek Brabec & Jan Novák & Bohuslav Procházka & Antonín Gabera & Petr Sedlak, 2022. "Influence of COVID-19-Related Restrictions on the Prevalence of Overweight and Obese Czech Children," IJERPH, MDPI, vol. 19(19), pages 1-14, September.
    4. Chen, Kefei & O'Leary, Rebecca A. & Evans, Fiona H., 2019. "A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool," Agricultural Systems, Elsevier, vol. 173(C), pages 140-150.
    5. Oliver Stoner & Gavin Shaddick & Theo Economou & Sophie Gumy & Jessica Lewis & Itzel Lucio & Giulia Ruggeri & Heather Adair‐Rohani, 2020. "Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 815-839, August.
    6. Oliver Stoner & Theo Economou, 2020. "Multivariate hierarchical frameworks for modeling delayed reporting in count data," Biometrics, The International Biometric Society, vol. 76(3), pages 789-798, September.
    7. Oliver Stoner & Alba Halliday & Theo Economou, 2023. "Correcting delayed reporting of COVID‐19 using the generalized‐Dirichlet‐multinomial method," Biometrics, The International Biometric Society, vol. 79(3), pages 2537-2550, September.
    8. Francesco Brizzi & Paul J. Birrell & Martyn T. Plummer & Peter Kirwan & Alison E. Brown & Valerie C. Delpech & O. Noel Gill & Daniela Angelis, 2019. "Extending Bayesian back-calculation to estimate age and time specific HIV incidence," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 757-780, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:152:y:2020:i:c:s0167947320301365. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.