Climate Dynamics

, Volume 47, Issue 9–10, pp 2901–2921 | Cite as

Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models

  • Zilefac E. AsongEmail author
  • M. N. Khaliq
  • H. S. Wheater


Based on the Generalized Linear Model (GLM) framework, a multisite stochastic modelling approach is developed using daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. Temperature is modeled using a two-stage normal-heteroscedastic model by fitting mean and variance components separately. Likewise, precipitation occurrence and conditional precipitation intensity processes are modeled separately. The relationship between precipitation and temperature is accounted for by using transformations of precipitation as covariates to predict temperature fields. Large scale atmospheric covariates from the National Center for Environmental Prediction Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate these models for the 1971–2000 period. Validation of the developed models is performed on both pre- and post-calibration period data. Results of the study indicate that the developed models are able to capture spatiotemporal characteristics of observed precipitation and temperature fields, such as inter-site and inter-variable correlation structure, and systematic regional variations present in observed sequences. A number of simulated weather statistics ranging from seasonal means to characteristics of temperature and precipitation extremes and some of the commonly used climate indices are also found to be in close agreement with those derived from observed data. This GLM-based modelling approach will be developed further for multisite statistical downscaling of Global Climate Model outputs to explore climate variability and change in this region of Canada.


GLMs Extreme events Precipitation Stochastic modelling Temperature Weather generators 



The financial support from the Global Institute for Water Security and School of Environment and Sustainability is gratefully acknowledged. Thanks are due to Eva Mekis from Environment Canada for providing access to adjusted precipitation and temperature data used in this study. We also thank Yanping Li for shedding light on meso-scale meteorological processes in the Canadian Prairie Provinces and Sun Chun for the useful comments on an earlier version of this paper. The invaluable programming assistance of Gonzalo Sapriza Azuri is much appreciated. The indices of extreme events were computed using the STARDEX project FORTRAN routines. Finally, we thank Richard Chandler from University College London and an anonymous referee for very detailed and useful comments which helped improve the quality of the analyses presented in the paper.

Supplementary material

382_2016_3004_MOESM1_ESM.docx (267 kb)
Supplementary material 1 (DOCX 266 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Zilefac E. Asong
    • 1
    Email author
  • M. N. Khaliq
    • 1
  • H. S. Wheater
    • 1
  1. 1.Global Institute for Water Security and School of Environment and SustainabilityUniversity of SaskatchewanSaskatoonCanada

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