DOI: 10.5194/hess-22-1615-2018
Scopus记录号: 2-s2.0-85043228150
论文题名: A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments
作者: Schepen A ; , Zhao T ; , Wang Q ; J ; , Robertson D ; E
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期: 2 起始页码: 1615
结束页码: 1628
语种: 英语
Scopus关键词: Bayesian networks
; Calibration
; Catchments
; Climate models
; Processing
; Rain
; Runoff
; Weather forecasting
; Bayesian modelling
; Global climate model
; Hydrological forecasting
; Postprocessing methods
; Rainfall forecasts
; Seasonal forecasting
; Seasonal forecasts
; Statistical techniques
; Forecasting
; catchment
; climate modeling
; climate prediction
; ensemble forecasting
; general circulation model
; global climate
; numerical method
; rainfall
; streamflow
; weather forecasting
; Australia
英文摘要: 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. © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79372
Appears in Collections: 气候变化事实与影响
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作者单位: CSIRO Land and Water, Dutton Park, Australia; Department of Infrastructure Engineering, University of Melbourne, Parkville, Australia; CSIRO Land and Water, Clayton, Australia
Recommended Citation:
Schepen A,, Zhao T,, Wang Q,et al. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments[J]. Hydrology and Earth System Sciences,2018-01-01,22(2)