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基于TIGGE数据的我国寒潮自动识别预报方法
Automatic Identification Forecast for Cold Wave in China Based on TIGGE Ensemble Data
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摘要: 利用1951—2006年中央气象台寒潮天气过程数据以及NCEP/NCAR 500 hPa高度场等逐日再分析资料，通过客观聚类方法与主观对比分析确定寒潮爆发的典型形势场，结合寒潮过程特征量阈值，建立了基于TIGGE集合预报产品的寒潮自动识别客观预报方法，并利用TIGGE集合预报数据对2008年1月和2009年1月两次寒潮天气过程进行预报试验。结果表明:利用500 hPa高度距平场进行聚类分析, 一方面可以消除环流季节特征对划分结果的影响，另一方面也突出了寒潮这种强天气的异常扰动表现；基于集合预报产品的寒潮自动识别预报方法浓缩了集合预报产品信息，可直接为预报员提供寒潮发生的概率预报, 从而在集合预报产品与我国实际灾害性天气之间建立了直接联系。Abstract: The existing cluster products of ensemble system are produced by the classification on the ensemble members and are linked with certain weather process. An automatic identification forecast model for cold wave is proposed and the application of TIGGE data is improved. Based on the cold wave activity data provided by China Central Meteorological Office and reanalysis data from NCEP/NCAR, several different cluster analysis methods cooperated with subjective analysis have been used to find the typical synoptic pattern of 500 hPa geopotential height field of cold wave in China. When the cluster analysis is made on the 500 hPa geopotential height field, the results dont match subjective analysis. The cause is that the disturbance of 500 hPa geopotential height is weak compared to the basic circulation, so the different distance used by the cluster method mainly reflects the seasonal variation rather than the dissimilarities of the synoptic feature. Then the cluster based on the abnormal field of 500 hPa geopotential height is made to reduce the influence of seasonal variation and give prominence to the circulation disturbance of the cold wave. The cluster analysis results in three typical abnormal distributions of 500 hPa geopotential height during cold wave: Meridional positive-negative-positive distribution, zonal negative-positive-negative-positive and positive-negative-positive distribution. Based on the typical synoptic pattern and the threshold of the physical characteristic during the cold wave process, the objective cold wave forecast model is established. The capability of the objective cold wave forecast model are confirmed by the forecast experiments using the data from 1991 to 2006, although the result also shows the objective method makes false alarm by forecasting weak cold process sometimes. Finally two forecast experiments are conducted using the TIGGE ensemble data of the cold wave process in Jan 2008 and Jan 2009. The result indicates that by using the objective cold wave forecast model, the information of the ensemble products is concentrated, and the method could provide the forecaster with probability of the cold wave occurrence, and it consequently builds a direct relation between the ensemble forecast system and the cold wave in China. Due to the limitation of the TIGGE data, just two forecast experiments are carried out. More cases are needed in order to study the efficiency of the objective cold wave forecast model and the prediction performance of TIGGE ensemble data more thoroughly.
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Key words:
- cold wave;
- ensemble forecast;
- cluster analysis
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图 1 聚类分析得到的寒潮爆发500 hPa距平场典型形势 (a) 第1类，(b) 第2类，(c) 第3类
Fig. 1 Three types of the cold wave resulted from the cluster analysis at 500 hPa anomaly filed (a)type Ⅰ,(b)type Ⅱ,(c)type Ⅲ
图 2 2008年1月10日500 hPa高度场 (a) 和500 hPa高度距平场 (b)(单位:dagpm)
Fig. 2 500 hPa geopotential height (a) and its anomaly (b) on 10 Jan 2008(unit: dagpm)
图 3 NNC集合预报对2008年1月10日寒潮过程自动识别预报结果
Fig. 3 Results of the automatic identification forecast for cold wave on 10 Jan 2008 based on NMC ensemble forecast products
图 4 ECNWF集合预报对2008年1月10日寒潮过程自动识别预报结果
Fig. 4 Results of the automatic identification forecast for cold wave on 10 Jan 2008 based upon ECMWF ensemble forecast products
图 5 2008年1月10日20:00 500 hPa高度场实况及对应的ECNWF168h预报场 (单位:dagpm)
Fig. 5 The geopotential height of 500 hPa on 10 Jan 2008 and the corresponding 168-hour forecast from ECMWF(unit；dagpm)
图 6 ECNWF集合预报对2009年1月21—25日寒潮过程自动识别概率预报结果
Fig. 6 Results of the automatic identification forecast for cold wave on 21-25 Jan 2009 based upon ECMWF ensemble forecast products
表 1 Ward聚类5种类型寒潮发生的月份统计
Table 1 Monthly distribution of 5 types of the cold wave by Ward cluster
表 2 NMC集合预报寒潮强冷空气自动识别概率预报结果
Table 2 The probability forecast results of the automatic identification forecast for cold wave on 10 Jan 2008 based upon NMC ensemble forecast products
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