基于多个参考态更新的动力相似预报方法及应用

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 关键词： 相似-动力模式 参考态 相似更新周期 月预报

A new method of dynamical analogue prediction based on multi-reference-state updating and its application

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.