Editorial Type:
Article
Article Type:
Research Article

A Postprocessing Method for Seasonal Forecasts Using Temporally and Spatially Smoothed Statistics

V. V. Kharin Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

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W. J. Merryfield Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

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G. J. Boer Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

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W.-S. Lee Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

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Print Publication:
01 Sep 2017
DOI:
https://doi.org/10.1175/MWR-D-16-0337.1
Page(s):
3545–3561
Restricted access

Abstract

A statistical postprocessing method for seasonal forecasts based on temporally and spatially smoothed climate statistics is introduced. The method uses information available from seasonal hindcasts initialized at the beginning of 12 calendar months. The performance of the method is tested within both deterministic and probabilistic frameworks using output from the ensemble of seasonal hindcasts produced by the Canadian Seasonal to Interannual Prediction System for the 30-yr period 1981–2010. Forecast skill improvements are found to be greater when forecast adjustment parameters estimated for individual seasons and at individual grid points are temporally and spatially smoothed. The greatest skill improvements are typically achieved for seasonally invariant parameters while skill improvements due to additional spatial smoothing are modest.

Denotes content that is immediately available upon publication as open access.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: V. V. Kharin, slava.kharin@canada.ca

Abstract

A statistical postprocessing method for seasonal forecasts based on temporally and spatially smoothed climate statistics is introduced. The method uses information available from seasonal hindcasts initialized at the beginning of 12 calendar months. The performance of the method is tested within both deterministic and probabilistic frameworks using output from the ensemble of seasonal hindcasts produced by the Canadian Seasonal to Interannual Prediction System for the 30-yr period 1981–2010. Forecast skill improvements are found to be greater when forecast adjustment parameters estimated for individual seasons and at individual grid points are temporally and spatially smoothed. The greatest skill improvements are typically achieved for seasonally invariant parameters while skill improvements due to additional spatial smoothing are modest.

Denotes content that is immediately available upon publication as open access.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: V. V. Kharin, slava.kharin@canada.ca
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