Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024028 |
Statistical Postprocessing of Water Level Forecasts Using Bayesian Model Averaging With Doubly Truncated Normal Components | |
Baran, Sandor1; Hemri, Stephan2; El Ayari, Mehrez1 | |
2019-05-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:5页码:3997-4013 |
文章类型 | Article |
语种 | 英语 |
国家 | Hungary; Switzerland |
英文摘要 | Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state-of-the-art hydrological ensemble prediction models are usually driven with meteorological ensemble forecasts. Hence, biases and dispersion errors of the meteorological forecasts cascade down to the hydrological predictions and add to the errors of the hydrological models. The systematic parts of these errors can be reduced by applying statistical postprocessing. For a sound estimation of predictive uncertainty and an optimal correction of systematic errors, statistical postprocessing methods should be tailored to the particular forecast variable at hand. Former studies have shown that it can make sense to treat hydrological quantities as bounded variables. In this paper, a doubly truncated Bayesian model averaging (BMA) method, which allows for flexible postprocessing of possibly multimodel ensemble forecasts of water level, is introduced. A case study based on water levels for a gauge of river Rhine reveals a good predictive skill of doubly truncated BMA compared both to the raw ensemble and the reference ensemble model output statistics approach. Using rolling training periods, BMA considerably outerperforms ensemble model output statistics. However, this gap shrinks drastically when using analog-based training periods. |
英文关键词 | Bayesian model averaging Box-Cox transformation ensemble model output statistics ensemble postprocessing probabilistic forecasting truncated normal distribution |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000474848500021 |
WOS关键词 | PRECIPITATION FORECASTS ; PROBABILISTIC FORECASTS ; OUTPUT STATISTICS ; EMOS MODEL ; ENSEMBLE ; PREDICTION ; CALIBRATION ; ECMWF ; UNCERTAINTY ; ALGORITHMS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/183129 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Debrecen, Fac Informat, Debrecen, Hungary; 2.MeteoSwiss, Fed Off Meteorol & Climatol, Zurich, Switzerland |
推荐引用方式 GB/T 7714 | Baran, Sandor,Hemri, Stephan,El Ayari, Mehrez. Statistical Postprocessing of Water Level Forecasts Using Bayesian Model Averaging With Doubly Truncated Normal Components[J]. WATER RESOURCES RESEARCH,2019,55(5):3997-4013. |
APA | Baran, Sandor,Hemri, Stephan,&El Ayari, Mehrez.(2019).Statistical Postprocessing of Water Level Forecasts Using Bayesian Model Averaging With Doubly Truncated Normal Components.WATER RESOURCES RESEARCH,55(5),3997-4013. |
MLA | Baran, Sandor,et al."Statistical Postprocessing of Water Level Forecasts Using Bayesian Model Averaging With Doubly Truncated Normal Components".WATER RESOURCES RESEARCH 55.5(2019):3997-4013. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论