Analogue correction of errors based on seasonal climatic predictable components and numerical experiments
Analogue correction of errors based on seasonal climatic predictable components and numerical experiments
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DOI：
10.7498/aps.58.7359
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Zheng Zhihai;Ren Hong-li;H. Jianping
中科院分区：
文献类型：
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作者：
Zheng Zhihai;Ren Hong-li;H. Jianping
基于利用历史相似信息来预测模型预测误差的思想，提出了一种新的基于预测分量的模拟误差修正方法(FACEPC)。该方法用于识别预测结果对初值相对不太敏感的可预测成分。然后，对于可预测分量，选择一种关联方案进行历史模拟选择和纠错。将该方法进一步应用于国家气候中心业务季节预报模式的试验。通过对不同区域选择合适的相似项和预报方案，交叉验证的结果表明，相对于系统误差修正，夏季降水和环流的预报技巧得分有了显著的提高，这在ENSO事件和可预报分量较多的地区表现得更加明显。特别是中国地区的技能得分也有了明显的提高，显示了其在业务季节预报中的潜在应用前景。此外，初步的敏感性实验表明，基于FACEPC的预测也明显地受到模拟选择因素和历史数据长度的影响。
Based on the idea of using historical-analogue information to predict the prediction errors of model, a new method named analogue correction of errors by predictable component （FACEPC） was developed. This method is adopted to identify the predicable components for which the prediction result is relatively not quite sensitive to the initial values. And then for predicable components, an associated scheme is chosen for historical-analogue selection and error correction. This method was further applied to experiments on operational seasonal prediction model of National Climate Center. By selecting suitable analogues and prediction schemes for different regions, the results from cross-validation indicate that the predictive skill scores of summer precipitation and circulation have got significant improvement relative to systematic error correction, which looks more obvious in ENSO episodes and over regions with more predictable components. Especially, the skill scores over China area have also been clearly improved, exhibiting its potential application perspective to operational seasonal prediction. Besides, preliminary sensitive experiments show that the FACEPC-based predictions are also obviously influenced by the analogue-selected factors and the length of historical data.