Articles | Volume 22, issue 2
https://doi.org/10.5194/hess-22-1615-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-22-1615-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2018
Research article |
| 01 Mar 2018
| 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 Schepen
CORRESPONDING AUTHOR
CSIRO Land and Water, Dutton Park 4102, Australia
Tongtiegang Zhao
Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia
Quan J. Wang
Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia
David E. Robertson
CSIRO Land and Water, Clayton 3168, Australia
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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...
Special issue