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A Bayesian Multivariate Nonhomogeneous Markov Model

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Book cover Machine Learning and Data Mining Approaches to Climate Science

Abstract

We present a Bayesian scheme for the downscaling of daily rainfall over a network of stations. Rainfall is modeled locally as a state-dependent mixture, with the states progressing in time as a first-order Markov process. The Markovian transition matrix, as well as the local state distributions, are dependent on exogenous covariates via generalized linear models (GLMs). The methodology is applied to a large network of stations spanning the Indian subcontinent and extending into the proximal Himalaya. The combined GLM-NHMM approach offers considerable flexibility and can also be applied to maximum and minimum temperatures. The modeling framework has been made available in the NHMM package for the R programming language.

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Acknowledgements

This work was supported by the US Department of Energy grant DE-SC0006616, as 563 part of the Earth System Modeling (EaSM) multi-agency initiative.

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Authors and Affiliations

  1. International Research Institute for Climate and Society, Columbia University Palisades, Palisades, NY, USA

    Arthur M. Greene & Andrew W. Robertson

  2. University of California, Irvine, CA, USA

    Tracy Holsclaw & Padhraic Smyth

Authors
  1. Arthur M. Greene
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  2. Tracy Holsclaw
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  3. Andrew W. Robertson
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  4. Padhraic Smyth
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Corresponding author

Correspondence to Arthur M. Greene .

Editor information

Editors and Affiliations

  1. The Climate Corporation, Seattle, Washington, USA

    Valliappa Lakshmanan

  2. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA

    Eric Gilleland

  3. Computer Science, University of Oklahoma, Norman, Oklahoma, USA

    Amy McGovern

  4. Meteorology and Statistics, Pennsylvania State University, University Park, Pennsylvania, USA

    Martin Tingley

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Greene, A.M., Holsclaw, T., Robertson, A.W., Smyth, P. (2015). A Bayesian Multivariate Nonhomogeneous Markov Model. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_6

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