气候变化领域集成服务门户(CCPortal): A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

CCPortal

DOI	10.5194/hess-22-1615-2018
	A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments
	Schepen A.; Zhao T.; Wang Q.J.; Robertson D.E.
发表日期	2018
ISSN	1027-5606
起始页码	1615
结束页码	1628
卷号	22 期号:2
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.
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160094
作者单位	Schepen, A., CSIRO Land and Water, Dutton Park4102, Australia; Zhao, T., Department of Infrastructure Engineering, University of Melbourne, Parkville, 3010, Australia; Wang, Q.J., Department of Infrastructure Engineering, University of Melbourne, Parkville, 3010, Australia; Robertson, D.E., CSIRO Land and Water, Clayton, 3168, Australia
推荐引用方式 GB/T 7714	Schepen A.,Zhao T.,Wang Q.J.,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],2018,22(2).
APA	Schepen A.,Zhao T.,Wang Q.J.,&Robertson D.E..(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.Hydrology and Earth System Sciences,22(2).
MLA	Schepen A.,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".Hydrology and Earth System Sciences 22.2(2018).