Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-380
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
06 Jul 2017
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Hydrology and Earth System Sciences (HESS) and is expected to appear here in due course.
A new method for post-processing daily sub-seasonal to seasonal rainfall forecasts from GCMs and evaluation for 12 Australian catchments
Andrew Schepen1, Tongtiegang Zhao2, Quan J. Wang3, and David E. Robertson2 1CSIRO Land and Water, Dutton Park, 4102, Australia
2CSIRO Land and Water, Clayton, 3168, Australia
3Department of Infrastructure Engineering, The University of Melbourne, Parkville, 3010, Australia
Abstract. Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal time scales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. However, for hydrological applications, GCM forecasts are often biased and unreliable in uncertainty spread, and therefore calibration is 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) based on the Bayesian joint probability approach for calibrating daily forecasts and the Schaake Shuffle approach 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 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.

Citation: Schepen, A., Zhao, T., Wang, Q. J., and Robertson, D. E.: A new method for post-processing daily sub-seasonal to seasonal rainfall forecasts from GCMs and evaluation for 12 Australian catchments, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-380, in review, 2017.
Andrew Schepen et al.
Andrew Schepen et al.

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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 good-quality 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 improves forecast reliability and accuracy and outperforms other commonly used statistical methods.
Rainfall forecasts from dynamical global climate models (GCMs) require post-processing before...
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