刘永柱,龚建东,张林,陈起英. 2019. 线性化物理过程对GRAPES全球切线性模式的影响[J]. 气象学报, (0):-, doi:10.11676/qxxb2019.013
线性化物理过程对GRAPES全球切线性模式的影响
Influence of linearized physical processes on the GRAPES global tangent linear model
投稿时间:2018-05-02  修订日期:2018-06-24
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:
作者单位E-mail
刘永柱 国家气象中心 liuyzh@cma.gov.cn 
龚建东 国家气象中心 gongjd@cma.gov.cn 
张林 国家气象中心 lzhang@cma.gov.cn 
陈起英 国家气象中心 chenqy@cma.gov.cn 
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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,来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),来比较非线性模式中的扰动演化和切线性模式中的扰动演化之间的纬向平均误差。然后以绝热版本的切线性模式为基础,通过冬夏两个个例实验来分别检验4个线性化物理过程的12小时预报效果。实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:
      Abstract:In order to improve the analysis and forecast effect of GRAPES global four-dimensional variational assimilation(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 the strong nonlinear term of 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 effects, 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 error 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 was examined through summer and winter case for 12 hour 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, deep cumulus convection and large scale condensation, the accuracy of the moisture and temperature increments in the TLM can be improved in tropics regions and at mid-high latitudes, so the analysis and forecast effect of GRAPES global 4DVAR can be improved.
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