刘永柱,龚建东,张林,陈起英. 2019. 线性化物理过程对GRAPES 4DVAR同化的影响[J]. 气象学报, 77(2):196-209, doi:10.11676/qxxb2019.013
线性化物理过程对GRAPES 4DVAR同化的影响
Influence of linearized physical processes on the GRAPES 4DVAR
投稿时间:2018-05-02  修订日期:2018-07-17
DOI:10.11676/qxxb2019.013
中文关键词:  线性化物理过程  切线性模式  GRAPES模式  四维变分同化
英文关键词:Linearized physical processes  Tangent linear model  GRAPES model  Four-dimensional variational assimilation
基金项目:公益性行业(气象)科研专项(GYHY201506003)、重大自然灾害监测预警与防范(2018YFC1506705)、中国气象局数值预报中心GRAPES发展专项(GRAPES-FZZX-2018)。
作者单位
刘永柱 中国气象局数值预报中心/国家气象中心, 北京, 100081 
龚建东 中国气象局数值预报中心/国家气象中心, 北京, 100081 
张林 中国气象局数值预报中心/国家气象中心, 北京, 100081 
陈起英 中国气象局数值预报中心/国家气象中心, 北京, 100081 
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中文摘要:
      线性化物理过程能够改善四维变分同化中极小化收敛的稳定性和增加极小化过程中对大气物理过程和动力更加精确的描述,它是四维变分同化中非常重要的一部分。通过在GRAPES全球模式中研究线性化物理过程,尤其是两个湿线性化物理过程,改善切线性模式预报精度,来提高GRAPES全球四维变分同化的分析和预报效果。线性化物理过程的开发首先需要简化原非线性化物理过程中的强非线性项,然后对线性化物理过程进行规约化,以抑制切线性扰动的异常增长。目前GRAEPS全球模式中的线性化物理过程主要包括次网格尺度地形参数化、垂直扩散、积云深对流和大尺度凝结。线性化物理过程预报精度的检验方法是通过选择合适大小的初始扰动(同化分析增量),来比较非线性模式和切线性模式中的扰动演化的纬向平均误差。然后以绝热版本的切线性模式为基础,通过冬、夏两个个例试验来分别检验4个线性化物理过程的12 h预报效果。试验结果表明,通过添加次网格地形参数化和垂直扩散两个干线性化物理过程方案,可以有效抑制住绝热版本切线性模式低层扰动的异常增长,大幅度改善切线性模式预报效果。通过添加积云深对流和大尺度凝结两个湿线性化物理过程,可以在热带区域和中、高纬度地区提高切线性模式中湿变量和温度变量的近似精度,提高GRAPES全球四维变分同化的分析和预报效果。
英文摘要:
      The linearized physical processes can improve the convergence stability of the four-dimensional variational assimilation (4DVAR) minimization, and increase the significant description of the atmospheric physical processes and dynamics during the minimization. It is a very important component of the 4DVAR. In order to improve the analysis and forecast effect of GRAPES global 4DVAR,a set of linearized physical parameterizations have been developed for the GRAPES global model to improve the accuracy of the tangent linear model (TLM), especially the two moist linearized physical parameterizations. The development of the linearized physical parameterizations requires the simplification of those strong nonlinear terms in the non-linear physical parameterizations and the regulation of the linearized physical parameterizations,and reduces the abnormal growth of the tangent linear perturbation. At present,the following linearized physical processes are described in GRAEPS global model:Subgrid-scale orographic effect, vertical diffusion, deep cumulus convection and large scale condensation. The test method for the TLM accuracy with the linearized physical parameterizations is to compare the zonal mean errors between the perturbation evolution in the nonlinear model including full physics and the perturbation evolution in the TLM including the linearized physical parameterizations. It is shown that for finite size perturbations (analysis increments), the inclusion of physics improves the fit to the non-linear model. Then based on the adiabatic TLM, the effect of these linearized physical processes is examined for summer and winter cases for 12 h forecasts. The experimental results show that by adding two dry linearized physical processes (vertical diffusion and subgrid-scale orographic effects), the abnormal growth near the surface in the adiabatic TLM can be effectively suppressed, and the accuracy of the tangential linear mode can be greatly improved. By adding two moist linearization physics processes, i.e., deep cumulus convection and large-scale condensation, the accuracy of the moisture and temperature increments in the TLM can be improved in the tropics and middle to high latitudes, and thus the analysis and forecast effect of GRAPES global 4DVAR can be improved.
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