An improved statistical analogue downscaling procedure for seasonal precipitation forecast

Original Paper
First Online:

Abstract

Seasonal forecasting can be highly valuable for water resources management. Hydrological models (either lumped conceptual rainfall-runoff models or physically based distributed models) can be used to simulate streamflows and update catchment conditions (e.g. soil moisture status) using rainfall records and other catchment data. However, in order to use any hydrological model for skillful seasonal forecasting, rainfall forecast at relevant spatial and/or temporal scales is required. Together with downscaling, general circulation models are probably the only tools for making such seasonal predictions. The Predictive Ocean Atmosphere Model for Australia (POAMA) is a state-of-the-art seasonal climate forecast system developed by the Australian Bureau of Meteorology. Based on the preliminary assessment on the performance of existing statistical downscaling methods used in Australia, this paper is devoted to develop an analogue downscaling method by modifying the Euclidian distance in the selection of similar weather pattern. Such a modification consists of multivariate Box–Cox transformation and then standardization to make the resulted POAMA and observed climate pattern more similar. For the predictors used in Timbal and Fernadez (CAWCR Technical Report No. 004, 2008), we also considered whether the POAMA precipitation provides useful information in the analogue method. Using the high quality station data in the Murray Darling Basin of Australia, we found that the modified analogue method has potential to improve the seasonal precipitation forecast using POAMA outputs. Finally, we found that in the analogue method, the precipitation from POAMA should not be used in the calculation of similarity. The findings would then help to improve the seasonal forecast of streamflows in Australia.

Keywords

Bias correction POAMA Quantile–quantile (Q–Q) transformation Standardization Statistical downscaling 
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Notes

Acknowledgments

This research is supported by the Water Information Research and Development Alliance (WIRADA) between the Australian Bureau of Meteorology (BoM) Water Division and the CSIRO Water for a Healthy Country Flagship Program. The authors wish to acknowledge useful discussions from CSIRO colleagues QJ Wang, Enli Wang and Hongxing Zheng and BoM collaborators Narendra Kumar Tuteja, Betrand Timbal, Daehyok Shin and Sri Srikanthan. We would like also express our thanks to the editor, an Associate Editor and two anonymous referees for their valuable comments which helped to improve the quality of this paper.

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Copyright information

© Crown Copyright as represented by: Simon Barry 2012

Authors and Affiliations

  1. 1.CSIRO MathematicsInformatics and StatisticsWembleyAustralia

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