Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-16-0652.1 |
How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts? | |
Zhao, Tongtiegang1; Bennett, James C.1; Wang, Q. J.1; Schepen, Andrew2; Wood, Andrew W.3; Robertson, David E.1; Ramos, Maria-Helena4 | |
2017-05-01 | |
发表期刊 | JOURNAL OF CLIMATE |
ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2017 |
卷号 | 30期号:9 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia; USA; France |
英文摘要 | GCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called "coherence.'' This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw forecasts are not significantly positively correlated with observations, QM tends to produce negatively skillful forecasts. Even when there is significant positive correlation, QM cannot ensure reliability and coherence for postprocessed forecasts. Therefore, QM is not a fully satisfactory method for postprocessing forecasts where the issues of bias, reliability, and coherence pre-exist. Alternative postprocessing methods based on ensemble model output statistics (EMOS) are available that achieve not only unbiased but also reliable and coherent forecasts. This is shown with one such alternative, the Bayesian joint probability modeling approach. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000399680500007 |
WOS关键词 | MODEL OUTPUT STATISTICS ; REGIONAL CLIMATE MODEL ; BIAS-CORRECTION ; MULTIMODEL ENSEMBLES ; CALIBRATION ; WEATHER ; RATIONALE ; RAINFALL ; SUCCESS ; SYSTEM |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
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/20332 |
专题 | 气候变化 |
作者单位 | 1.CSIRO Land & Water, Clayton, Vic, Australia; 2.CSIRO Land & Water, Dutton Pk, Qld, Australia; 3.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA; 4.Irstea, Hydrosyst & Bioproc Res Unit, Antony, France |
推荐引用方式 GB/T 7714 | Zhao, Tongtiegang,Bennett, James C.,Wang, Q. J.,et al. How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?[J]. JOURNAL OF CLIMATE,2017,30(9). |
APA | Zhao, Tongtiegang.,Bennett, James C..,Wang, Q. J..,Schepen, Andrew.,Wood, Andrew W..,...&Ramos, Maria-Helena.(2017).How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?.JOURNAL OF CLIMATE,30(9). |
MLA | Zhao, Tongtiegang,et al."How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?".JOURNAL OF CLIMATE 30.9(2017). |
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