基于BGM框架的短期集合预报扰动典型规律研究
投稿时间: 2017-12-10  最后修改时间: 2018-03-09  点此下载全文
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作者单位E-mail
陈超辉 气象海洋学院军事气象系 chenchaohui2001@163.com 
基金项目:南京大气科学联合研究中心基金资助项目(NJCAR2016ZD04;NJCAR2016MS02);国家重大基础研究计划资助项目(编号: 2017YFC1501800).
中文摘要:采用增长模培育(Breeding of Growing Modes, BGM)法开展有限区域模式短期集合预报研究,首先亟需解决的关键问题是集合预报扰动的发展及演变规律问题。因此论文结合经典的适时缩放培育思想,利用增长模培育法,基于WRF3.6模式(动力内核采用WRF-ARW),开发及构建了一个包含水平风场、垂直速度、位温扰动、位势扰动和水汽混合比共六个基本物理量的有限区域模式短期集合预报系统(WRF-EPS)。在此基础上,以2016年6月整月我国南方大范围暴雨为样例,针对集合预报扰动发展与演变的典型问题进行了探讨,试验结果表明:(1)模式大气物理量高、中、低三层的扰动增长可以分为两个阶段,第一阶段为扰动快速线性增长阶段,该阶段内扰动快速完成全部涨幅;第二阶段为非线性稳定阶段,从快速线性增长阶段过渡到非线性稳定阶段大约需要经过24小时。(2)各物理量的扰动增长率、场相关系数以及扰动增长模进入非线性稳定阶段的时间大致相同,但对于同一等压面不同物理量或同一物理量不同等压面,每个参数达到非线性稳定后的数值大小及演变规律存在差异,且随时间演变均伴有日内振荡现象。(3)对于扰动振幅相同但初始随机模态不同的初值集合,不同随机模态对扰动培育的影响主要发生在扰动的非线性稳定阶段,而在快速的线性增长阶段,它们之间的差异很小。(4)对于初始随机模态相同但扰动振幅不同的初值集合,不同扰动振幅对扰动演变的影响主要发生在扰动的快速线性增长阶段,而在非线性稳定阶段,它们之间的差异很小,并且不同初始振幅对扰动进入非线性稳定阶段的特征时间基本没有影响。
中文关键词:短期集合预报  初始扰动  线性增长  非线性增长  增长模培育
 
Typical characteristics of spatio-temporal evolution of initial perturbations in the short-range ensemble prediction system based on the breeding method
Abstract:When one makes short-range regional ensemble forecast, a critical problem first to be faced is what the typical characteristics of evolution of initial perturbations is in the short-range ensemble prediction system (EPS).Consequently,an short-range EPS based on the breeding of growing modes (formerly known as BGM)method has been developed with WRF3.6. The regular rescaling scheme has also been incorporated into this system. Meanwhile, the short-range EPS that has covered the uncertainties of the horizontal wind, the vertical velocity, the potential temperature, the geopotential height and the water vapor mixture ratio takes the large-range rainstorm in south China in June 2016 as an example to recognize the perturbations’ evolving mechanism. The results show that: (1) the perturbation growing process in the upper, middle and lower model atmosphere can be divided into two stages, one of which is the perturbations rapid linear growth and quickly completing the total increase of themselves in this phase, another of which is the nonlinear stable phase of perturbations growing and taking about 24 hours to transition from the fast linearly growing phase to the nonlinear stable phase. (2) The perturbations of each variable take approximately the same length of time to enter the nonlinear stable phase through the temporal evolution features of perturbation growth rate, correlation coefficient and the perturbation growing mode. Nonetheless, when the perturbations came into the nonlinear stable stage, the numerical values and evolving characteristics of each assessment parameter are different for the same pressure level with different physical variables or the same physical variable at different pressure levels. Moreover, there is a diurnal oscillation phenomenon of each assessment method with the time evolving at the nonlinear stable stage. (3)For the initial ensemble of different random patterns with the same size of disturbance amplitude, the impacts of different random patterns on perturbations breeding mainly yield differences in the nonlinear stable stage while the differences between each pattern are too small to distinguish in the fast linearly growing stage. (4) For the initial ensemble of the same random pattern but with different sizes of disturbance amplitude, the influences of different amplitudes on the evolution of perturbations mainly occurs in the fast linear growth phase, while the differences between each amplitude are quite small in the nonlinear stable phase. Additionally, the different size of amplitudes has no influence on the characteristic time scale of the perturbations getting into the nonlinear stable phase.
keywords:short-range ensemble prediction,initial perturbation,linear growth, nonlinear growth,breeding of growing modes
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