Articles | Volume 22, issue 2
https://doi.org/10.5194/hess-22-1615-2018
https://doi.org/10.5194/hess-22-1615-2018
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 Schepen, Tongtiegang Zhao, Quan J. Wang, and David E. Robertson

Viewed

Total article views: 4,337 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,972 1,263 102 4,337 63 83
  • HTML: 2,972
  • PDF: 1,263
  • XML: 102
  • Total: 4,337
  • BibTeX: 63
  • EndNote: 83
Views and downloads (calculated since 06 Jul 2017)
Cumulative views and downloads (calculated since 06 Jul 2017)

Viewed (geographical distribution)

Total article views: 4,337 (including HTML, PDF, and XML) Thereof 4,153 with geography defined and 184 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (preprint)

Latest update: 19 Apr 2024
Download
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.