Performance of recalibration systems for GCM forecasts for southern Africa
Performance of recalibration systems for GCM forecasts for southern Africa
复制标题
南部非洲 GCM 预报重新校准系统的性能
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
S. Mason
中科院分区：
文献类型：
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作者：
M. Shongwe;W. Landman;S. Mason
为南部非洲开发了两种基于回归的方法，重新校准了ECHAM4.5大气环流模式（GCM）在南部夏季的输出，并在1989/90-2000/01的12年追溯期内评估了其性能。还开发了一个将近全球海表温度（SST）与区域降雨联系起来的线性统计模型。重新校准技术是模型输出统计（MOS），使用主成分回归（PCR）和典型相关分析（CCA），在统计上将GCM的存档记录与赤道以南非洲大部分地区的区域降雨联系起来。本文用线性统计模式和MOS模式对12 ~ 2月各雨区的极端干湿状况进行了定量预报。MOS技术优于原始GCM集合和线性统计模型。PCR-MOS和CCA-MOS模型都没有表现出明显的优越性，可能是因为这两种方法密切相关。本文进一步证明了在区域尺度上重新校准GCM预测以提高其在较小空间尺度上的技能的必要性。版权所有© 2006年皇家气象学会。
Two regression‐based methods that recalibrate the ECHAM4.5 general circulation model (GCM) output during austral summer have been developed for southern Africa, and their performance assessed over a 12‐year retroactive period 1989/90–2000/01. A linear statistical model linking near‐global sea‐surface temperatures (SSTs) to regional rainfall has also been developed. The recalibration technique is model output statistics (MOS) using principal components regression (PCR) and canonical correlation analysis (CCA) to statistically link archived records of the GCM to regional rainfall over much of Africa, south of the equator. The predictability of anomalously dry and wet conditions over each rainfall region during December–February (DJF) using the linear statistical model and MOS models has been quantitatively evaluated. The MOS technique outperforms the raw‐GCM ensembles and the linear statistical model. Neither the PCR‐MOS nor the CCA‐MOS models show clear superiority over the other, probably because the two methods are closely related. The need to recalibrate GCM predictions at regional scales to improve their skill at smaller spatial scales is further demonstrated in this paper. Copyright © 2006 Royal Meteorological Society.