陈官军,魏凤英. 2017. 基于低频振荡信号的我国南方冬半年持续性低温指数延伸期预报试验[J]. 气象学报, ():-, doi:10.11676/qxxb2017.024
基于低频振荡信号的我国南方冬半年持续性低温指数延伸期预报试验
Extended Range Forecast Experiment of Winter Persistent Low Temperature Indexes Basing on Intraseasonal Oscillation over Southern China
投稿时间:2016-08-03  最后修改时间:2016-11-18
DOI:10.11676/qxxb2017.024
中文关键词:  低频振荡信号  持续性低温指数  DERF2.0  延伸期预报
英文关键词:Low-frequency oscillation signals, Persistent low temperature indexes, DERF2.0, Extended range forecast
基金项目:公益性行业专项
作者单位E-mail
陈官军 中国气象科学研究院,中国人民解放军96164部队 aiolya_gj@163.com 
魏凤英 中国气象科学研究院 weify@camscma.cn 
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中文摘要:
      利用1961-2009年36°N以南、108°E以东中国大陆191个站点逐日最低温度和NCEP/NCAR Reanalysis 日平均格点资料,研究与区域持续性低温事件有关的大气低频振荡信号,寻找可以在一定程度上表征不同类型区域持续性低温事件的指数,并尝试结合DERF2.0系统的预报产品进行持续性低温指数的延伸期预报试验。结果表明:(1)本文研究范围内的区域持续性低温事件可以归纳为江北型、江南型和全区域型三类,其中江北型和江南型事件的环流背景差异体现在异常环流中心的纬度位置上,而全区域型事件属于增强型的江北型事件。(2)江北型和江南型区域平均最低温度时间序列的10-30d低频分量的位相和强度变化与区域持续性低温事件的发生有显著关系,可以作为表征区域持续性低温事件指数和预报量;(3)100°-120°E范围内850hPa温度场距平的前两个EOF主模态具有显著10-30d变化周期,并且其空间结构分别与江北型和江南型事件的典型环流特征相一致,前两个主模态时间系数能够作为持续性低温指数的预报因子;(4)检验结果表明, DERF2.0系统对上述预报因子有一定的预报能力。在延伸期预报时效内,利用统计学和动力学相结合的方法制作的持续性低温指数的预报效果好于模式直接预报的2米温度,该预报方法有助于提升区域持续性低温事件的延伸预报能力。
英文摘要:
      On the basis of daily NCEP/NCAR Reanalysis and observational data for 1961-2009, this study investigates the low frequency oscillation signals of regional persistent low temperature events (RPLTEs) to the south of 36°N in China and looking for indexes which can be used to characterize the RPLTEs. The indexes are used as the predictand in extended range forecast experiment based on the DERF2.0 hindcasts. Results show that the RPLTEs can be classified into three types of North of Yangtze River, South of Yangtze River and entire region. The types of North of Yangtze River and South of Yangtze River have their own key common circulation features that are distinguished by latitude of the anomalous circulation centers and characterized by low-frequency wave train propagating from northwest to southeast in Asia. 10~30d low-frequency components of the daily minimum temperature series of North of Yangtze River (T1) and South of Yangtze River (T2) are defined as the persistent low temperature indexes (RPLTIs). The phase and amplitude of the RPLTIs have close relationship with the RPLTEs and are used as the predictand in extended range forecast experiment. EOF1 of the 850hPa temperature anomalies between 100°~120°E coincided with the low-frequency mode of T1 while EOF2 coincided with that of T2. Projection of daily data onto the leading pair EOFs of 850hPa temperature anomalies yields principal component time series that serves as an effective filter for low-frequency oscillation without the need for bandpass filtering and making the principal component time series two effective predictors for real-time use. DERF2.0 hindcasts and stepwise regression statistical method are employed to explore extended range forecast (ERF) of RPLTIs. The forecast skill of this statistical-dynamical prediction is superior to the 2 meter temperature of DERF2.0 (direct model output) in real-time experiments.
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