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Multi-model Downscaling Ensemble Prediction in National Climate Center
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摘要: 多模式集合和降尺度技术是提升模式预测能力的有效工具。该文对国家气候中心多模式解释应用集成预测 (MODES) 技术与业务应用现状进行了综合介绍。MODES采用欧洲中期天气预报中心、东京气候中心、美国国家环境预报中心和中国气象局国家气候中心4个气候业务季节预测模式输出场，利用EOF迭代、变形的典型相关分析、最优子集回归和高相关回归集成4种统计降尺度方法以及等权平均、经典超级集合等集成方法进行全国月及季节降水和气温预测。目前对MODES进行了夏季回报检验和约1年的实时业务应用。回报检验和业务应用表明，MODES对气温有较好的预测能力 (月预测平均PS评分为76)，对降水有一定预测技巧 (月预测平均PS评分为68)，具有短期气候预测业务应用价值。Abstract: Dynamic model is the dominant tool for the seasonal prediction operation in most climate prediction centers of the world. But now, for any single model, the predictability to seasonal precipitation and temperature is quite limited. Therefore, two kinds of techniques (i.e., multi-model ensemble and downscaling) are developed efficiently to access better prediction ability. Multi-model ensemble can reduce model error and then bring higher prediction skills. Meanwhile, as the model predictability of circulation is better than that of precipitation and temperature, downscaling improves the prediction of temperature and precipitation via regional model or statistic methods.Due to the complex physical mechanism, the seasonal prediction to China climate is much a challenge. China National Climate Center (NCC) develops a new kind of prediction technique combining multi-model ensemble and downscaling. At present, the output variables from four seasonal models from WMO GPCs (including ECMWF, TCC, NCEP and NCC) are used as predictors and four statistic downscaling methods (EOF-ITE, BP-CCA, Optical Subset Regression, Regress Ensemble of High Correlation Factors) are used to set prediction model. Every model output and every downscaling method are used so that 16 model-downscaling components are available. Besides, two methods (equal-weighted average, classic super-ensemble) are employed to access the ensemble result, respectively. As an index showing the prediction ability, the mean PS scores are computed for the reforecast of recent five years for every model-downscaling and ensemble component. The component with highest mean PS score is chosen as the best prediction result.In NCC, the Multi-model Downscaling Ensemble Prediction System (MODES) are set up to realize the above ideas and the operational application of monthly and seasonal temperature with precipitation over China. Reforecast and operational application are carried out. The present reforecast and operational application for seasonal climate indicates that MODES has achieved quite good prediction skills for temperature and also improved precipitation prediction. The real-time application for monthly climate prediction for from September 2012 to July 2013 is assessed with NCC traditional PS methods. For monthly mean temperature, MODES holds the mean and median PS score of 76 and 81, respectively, showing much good prediction ability. Meanwhile, for the monthly precipitation of MODES, the mean and median PS scores are both 68, higher than mean scores of the operational prediction product of NCC. The reforecast and operational application indicate that MODES is a useful tool for the short-term climate operation prediction.
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Key words:
- multi-model ensemble;
- statistical downscaling;
- MODES
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图 1 HCRE方法预报流程图
Fig. 1 Diagram of the downscaling method of regress ensemble of high correlation factors
图 2 MODES夏季气温后报平均PS评分
(起报时间为3月，其中降尺度方法后报时间为2003—2012年；集成方法后报时间为2008—2012年)
Fig. 2 The mean PS skill of the reforecast of summer temperature of MODES
(forecast start reference is March, the reforecast period is 2003—2012 for the downscaling methods and 2008—2012 for the ensemble one, respectively)
图 3 MODES最优夏季气温 (a) 和降水 (b) 后报距平符号一致率空间分布
Fig. 3 The spatial distribution of the same sign rate of prediction anomalies of temperature (a) and precipitation (b) in summer
图 4 2011—2012年冬季气温距平与降水距平百分率实况与预测 (a) 气温实况，(b) 气温预测, (c) 降水实况, (d) 降水预测
Fig. 4 The observation and prediction of temperature anomaly and precipitation anomaly percentage during the winter of 2011—2012 (a) temperature observation, (b) temperature prediction, (c) precipitation observation, (d) precipitation prediction
表 1 国家气候中心MODES现有业务气候模式数据
Table 1 The accessible data of operational climate models at NCC
机构 模式 预测时间长度 业务应用起始时间 模式数据原始分辨率 历史回报时间 ECMWF System4 7个月 2011年 1.5°×1.5° 1981—2010年 NCEP CFS V2 9个月 2011年 1°×1° 1982—2010年 TCC MRI-CGCM 3~7个月 2009年 2.5°×2.5° 1979—2008年 NCC CGCM V1 11个月 2005年 2.5°×2.5° 1983—2003年 
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表 2 MODES在2012年9月—2013年7月月预测业务应用PS评分
Table 2 PS skill of the monthly operational prediction of MODES from September 2012 to July 2013
时间 PS评分 气温 降水 2012-09 69 44 2012-10 70 69 2012-11 44 77 2012-12 95 60 2013-01 56 65 2013-02 95 84 2013-03 90 64 2013-04 73 73 2013-05 83 68 2013-06 85 73 2013-07 81 68
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