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高原气象  2018, Vol. 37 Issue (4): 1042-1050    DOI: 10.7522/j.issn.1000-0534.2017.00090
论文     
基于DERF2.0模式1~52天最低温度逐日预报的检验评估
徐岩岩, 常军
河南省气候中心, 河南 郑州 450003
Evaluation of the Minimum Temperature Forecast of 1~52 Days Based on DERF2.0 Model
XU Yanyan, CHANG Jun
Climate Center of Henan Province, Zhengzhou 450003, Henan, China
 全文: PDF 
摘要: 利用国家气候中心第二代月动力延伸预报模式系统(DERF2.0)1983-2013年历史回算数据中20个全样本集合平均的最低温度数据,对3月中国中纬度地区模式最低温度1~52天逐日预报能力进行了评估。结果表明,通过计算绝对误差的绝对值AE、均方根误差RMSE和相关系数R发现前10天预报可信度较高,预报时效越短,可信度越高,超过10天之后预报可信度越来越低。为了分析模式误差的深层次原因,利用集合经验模态分解方法(EEMD)对数据进行分解,DERF2.0模式和观测数据的高频分量IMF1和趋势项R为主要分量,低频分量IMF2占的比重较少。分析发现,DERF2.0模式在10天之后预报能力下降的主要原因是高频分量IMF1的预报能力下降。通过分析历年的结果,发现各项方差贡献率年际变化不大,比较稳定,高频分量IMF1和低频分量IMF2方差贡献率和观测数据相比普遍偏低,趋势项R方差贡献率和观测数据相比每年都偏高。DERF2.0模式应该针对高频扰动继续加强研究,提高DERF2.0预报升温降温过程的能力。
关键词: DERF2.0模式逐日最低温度EEMD高频扰动方差贡献率    
Abstract: On the basis of the lowest temperature data for the average of 20 full sample of the National Climate Centre second-generation monthly Dynamic Extended Range Forecast operational system 2.0 (DERF2.0) from 1983 to 2013, the forecast capacity of the lowest temperature in mid-latitudes of China in march from 1 to 52 days was evaluated. The first ten days of the minimum temperature were more credible, and the shorter the forecast time was, the higher the credibility was, but over the 10 days, the credibility of the forecast was getting worse and worse by evaluating the absolute value of the absolute error (AE), root mean square error (RMSE) and correlation coefficient (R). In order to analyze the deep reasons of the model error, the data was decomposed by the ensemble empirical mode decomposition (EEMD). The high frequency component IMF1 and the trend item R were the main components, while the low frequency component IMF2 account for a small proportion in DERF2.0 data and observation data. The main reason for the decline in predictive ability after ten days was the decline in the predictive ability of the high frequency IMF1 by analyzing. In the DERF2.0 model, the variance contribution rate and observation data of the high frequency components of the high frequency components were generally low compared with the observed data. The contribution rate and observation data of the trend item R variance were higher than that of each year. The DERF2.0 model should focus on the study of high frequency disturbances to improve the ability of DERF2.0 in predicting the temperature changing process.
Key words: DERF2.0 model    minimum temperature of day    EEMD    high frequency components    ratio of variance
收稿日期: 2017-08-12 出版日期: 2018-08-22
:  P456.3  
基金资助: 河南省气象局气象科学研究项目(KQ201730,Z201406);河南省气象局气候与气候变化创新团队项目
作者简介: 徐岩岩(1988-),男,河南栾川人,工程师,主要从事气候预测方面研究.E-mail:xuyanyanpanshi@163.com
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徐岩岩, 常军. 基于DERF2.0模式1~52天最低温度逐日预报的检验评估[J]. 高原气象, 2018, 37(4): 1042-1050.

XU Yanyan, CHANG Jun. Evaluation of the Minimum Temperature Forecast of 1~52 Days Based on DERF2.0 Model. Plateau Meteorology, 2018, 37(4): 1042-1050.

链接本文:

http://www.gyqx.ac.cn/CN/10.7522/j.issn.1000-0534.2017.00090        http://www.gyqx.ac.cn/CN/Y2018/V37/I4/1042

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