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Volume 22, issue 2 | Copyright

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 22, 1615-1628, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Mar 2018

Research article | 01 Mar 2018

A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

Andrew Schepen1, Tongtiegang Zhao2, Quan J. Wang2, and David E. Robertson3 Andrew Schepen et al.
  • 1CSIRO Land and Water, Dutton Park 4102, Australia
  • 2Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia
  • 3CSIRO Land and Water, Clayton 3168, Australia

Abstract. Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

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Short summary
Rainfall forecasts from dynamical global climate models (GCMs) require post-processing before use in hydrological models. Existing methods generally lack the sophistication to achieve calibrated forecasts of both daily amounts and seasonal accumulated totals. We develop a new statistical method to post-process Australian GCM rainfall forecasts for 12 perennial and ephemeral catchments. Our method produces reliable forecasts and outperforms the most commonly used statistical method.
Rainfall forecasts from dynamical global climate models (GCMs) require post-processing before...