摘要
为了更有效地利用历史资料中的相似性信息提高数值模式预报水平,提出了一种新的动力相似预报方法———多参考态更新(MRSU)法.该方法基于“更新”观点,通过引入相似更新周期(PAU)的概念,将整个预报时段按PAU分成若干小的子时段,在预报进行到PAU时重新选取多个参考态,并采用超平面近似法将相似动力模式产生的多个预报估计成最佳预报向量,这样就形成了“选取估计”的循环,不断重复这一过程直到完成整个时段的预报.进一步将简化的MRSU方法应用于T63全球谱模式.月预报试验结果表明,与控制试验相比,MRSU法对逐日和月平均环流能有效提高预报技巧、减小预报误差,其中后者更为显著一些.
A new method named multi-reference-state updating （MRSU） pertaining to the dynamical analogue prediction, is developed on the basis of previous studies on analogue-dynamical models, in order to further effectively utilize the available information of historical observation data. In this scheme, according to a new idea of ＂updating＂, it is required that multi-reference states are renewedly selected on the period of analogue updating in the process of the analogue-dynamical model integration, and optimal forecast vectors are estimated from multi-forecasts produced by analogue-dynamical model by employing the hyperplane approximation method. Such ＂selection-estimation＂ cycles are repeatedly operated until the whole forecasts are completed. Furthermore, the simplified MRSU is applied to the T63 global spectral model, and the results of monthly forecast experiments show that for the daily and monthly mean circulation, the MRSU can effectively reduce forecast errors and improve forecast skill compared with the control forecasts.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2006年第8期4388-4396,共9页
Acta Physica Sinica
基金
国家自然科学基金(批准号:40233031
40575036
40375025)资助的课题.~~
关键词
相似-动力模式
参考态
相似更新周期
月预报
analogue-dynamical model, reference state, period of analogue updating, monthly forecast
