Editorial Type:
Article
Article Type:
Research Article

A Bayesian Hidden Markov Model of Daily Precipitation over South and East Asia

Tracy Holsclaw University of California, Irvine, Irvine, California

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Arthur M. Greene International Research Institute for Climate and Society, Earth Institute at Columbia University, Palisades, New York

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Andrew W. Robertson International Research Institute for Climate and Society, Earth Institute at Columbia University, Palisades, New York

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Padhraic Smyth University of California, Irvine, Irvine, California

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Print Publication:
01 Jan 2016
DOI:
https://doi.org/10.1175/JHM-D-14-0142.1
Page(s):
3–25
Restricted access

Abstract

A Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional characteristics of precipitation at each site over time, while the Markovian transitions between discrete states represent seasonality and subseasonal weather variability. Model performance is evaluated for station networks of summer rainfall over the Punjab region in northern India and Pakistan and the upper Yangtze River basin in south-central China. The model captures seasonality and the marginal daily distributions well in both regions. Extremes are reproduced relatively well in the Punjab region, but underestimated for the Yangtze. In terms of interannual variability, the combined GLM–HMM with spatiotemporal averages of observed rainfall as a predictor is shown to exhibit skill (in terms of reduced RMSE) at the station level, particularly for the Punjab region. The skill is largest for dry-day counts, moderate for seasonal rainfall totals, and very small for the number of extreme wet days.

Corresponding author address: Andrew Robertson, IRI, Earth Institute at Columbia University, 230 Monell, 61 Route 9W, P.O. Box 1000, Palisades, NY 10964-8000. E-mail: awr@iri.columbia.edu

Abstract

A Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional characteristics of precipitation at each site over time, while the Markovian transitions between discrete states represent seasonality and subseasonal weather variability. Model performance is evaluated for station networks of summer rainfall over the Punjab region in northern India and Pakistan and the upper Yangtze River basin in south-central China. The model captures seasonality and the marginal daily distributions well in both regions. Extremes are reproduced relatively well in the Punjab region, but underestimated for the Yangtze. In terms of interannual variability, the combined GLM–HMM with spatiotemporal averages of observed rainfall as a predictor is shown to exhibit skill (in terms of reduced RMSE) at the station level, particularly for the Punjab region. The skill is largest for dry-day counts, moderate for seasonal rainfall totals, and very small for the number of extreme wet days.

Corresponding author address: Andrew Robertson, IRI, Earth Institute at Columbia University, 230 Monell, 61 Route 9W, P.O. Box 1000, Palisades, NY 10964-8000. E-mail: awr@iri.columbia.edu
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Journal of Hydrometeorology

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