王 娜,方建刚,崔 巍,肖科丽,王 琦.陕西夏季旱涝环流特征及预测方法[J].干旱地区农业研究,2014,32(1):246~251
陕西夏季旱涝环流特征及预测方法
The drought and flood circulation characteristics and forecast method in summer of Shaanxi Province
  
DOI:10.7606/j.issn.1000-7601.2014.01.041
中文关键词:  夏季降水  环流  分型  相似模型  陕西
英文关键词:precipitation in summer  circulation  divided type  similarity model  Shaanxi Province
基金项目:国家重点基础研究发展规划基金项目(2010CB833406);陕西省气象局研究型业务重点科研项目(2013Z-6)
作者单位
王 娜1,方建刚1,崔 巍1,肖科丽2,王 琦1 (1.陕西省气候中心 陕西 西安 710014 2.陕西省气象学会 陕西 西安 710016) 
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中文摘要:
      对陕西省76站夏季降水量作EOF展开,分解出夏季降水的前2个模态:全省一致型、西北-东南反相,细分可分为全省一致多,全省一致少,降水西北多、东南向少,降水西北少、东南向多4种分布特征。由于1月的环流型对于夏季降水有很好的指示意义,建立了相似预测模型,用每年1月北半球500 hPa月平均环流与上述4种环流型计算相关系数,相关系数最大所对应的雨型即当年夏季降水趋势预测。对1961—2010年50年的回报效果分析,平均距平符号一致率为57%,其中全省一致多一致率为70.4%,一致少为71.3%,降水西北多、东南向少为58.5%,降水西北少、东南向多为44.9%,对于降水有一定的预测能力,其中对于全省一致旱涝有很好的预测能力。
英文摘要:
      Based on the summer rainfall data at 76 Shaanxi stations, the precipitation characters were investigated with EOF method. It showed that there were two dominant precipitation modes in summer: A consistent pattern in whole province and a opposite pattern of northwest to southeast. It could be divided more detail of four distribution characteristics as consistent more in whole province, cons istent less in whole province, more precipitation in northwest and less in southeast, less precipitation in northwest and more in southeast. A similar prediction model had been established based on good indication of the circulation patterns in January for the summer rainfall. The correlation coefficients of the January 500 hPa mean circulation with four patterns were calculated. The maximum correlation coefficient corresponding the rainfall pattern would be selected to be the forecast precipitation trend in summer. It was shown by the hindcasting results of the 50 years (1961—2010), the average anomaly sign consistency rate was 57%, the average anomaly sign consistency rate of four kinds of distribution was 70.4%, 71.3%, 58.5% and 44.9%, respectively. It had a specified predictive capability for the precipitation forecast and better predictive capability for more or less precipitation in whole Shaanxi Province.
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