GRAPES全球模式初始化优化策略

结合GRAPES全球模式并行计算的特点,分析了GRAPES全球模式的初始化模块的耗时情况,重点讨论了IO初始化函数和物理过程初始化函数,设计了一系列针对程序初始化阶段的通信优化方案,通过采用集合通信和先到先服务的策略减少通信拥塞和通信热点,有效减少了初始化耗时,使程序初始化阶段性能提高15倍以上.根据实测结果优化方案取得了非常好的效果,大幅提高了GRPAES全球模式大规模多进程运行时的效率,使程序的稳定性和可扩展性得到一定的提高.     

On the development of the GRAPES――A new generation of the national operational NWP system in China

Numerical weather prediction (NWP) has become one of the most important means for weather fore-casts in the world. It also mirrors a nation's comprehensive strength in meteorology. In 2000, China Meteorological Administration (CMA) established the National Innovative Base for Meteorological Numerical Prediction in the Chinese Academy of Meteorological Sciences (CAMS), to work on developing a new generation of the national operational NWP system――Global/Regional Assimilation and PrEdiction System (GRAPES), to enhance meteorological services in China in the new century. In recent years, the GRAPES has witnessed a fast development. The GRAPES has been set up as an integration of the model framework, data assimilation, regional and global NWP system, which can be commonly used for both operation and research. In this paper, a brief review is made for illustrating the GRAPES system, including the advanced designs of the GRAPES, its diverse applications in multifields, and efficiencies of the regional and global GRAPES in operational applications based on hindcast results.     

基于GRAPES的综合临近预报系统（雨燕GRAPES-SWIFT）

在我国,台风、暴雨、雷雨大风、雷电、龙卷风、冰雹等灾害天气及其产生的次生自然灾害连年不断,近年来全球气候变暖的加剧,导致突发气象灾害发生更加频繁。本项目研究瞄准国家防灾减灾需求和气象灾害应急响应的需要,依托中尺度数值预报模式及其快速循环更新同化系统产品,融合新一代天气雷达、卫星、闪电定位仪、GPS／MET、风廓线雷达和自动气象站等探测资料信息,     

On the development of the GRAPES——A new generation of the national operational NWP system in China

Numerical weather prediction （NWP） has become one of the most important means for weather forecasts in the world. It also mirrors a nation＇s comprehensive strength in meteorology. In 2000, China Meteorological Administration （CMA） established the National Innovative Base for Meteorological Numerical Prediction in the Chinese Academy of Meteorological Sciences （CAMS）, to work on developing a new generation of the national operational NWP system-Global/Regional Assimilation and PrEdiction System （GRAPES）, to enhance meteorological services in China in the new century. In recent years, the GRAPES has witnessed a fast development. The GRAPES has been set up as an integration of the model framework, data assimilation, regional and global NWP system, which can be commonly used for both operation and research. In this paper, a brief review is made for illustrating the GRAPES system, including the advanced designs of the GRAPES, its diverse applications in multi-fields, and efficiencies of the regional and global GRAPES in operational applications based on hindcast results.     

Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES

The scientific design and preliminary results of the data assimilation component of the Global-Regional Prediction and Assimilation System （GRAPES） recently developed in China Meteorological Administration （CMA） are presented in this paper. This is a three-dimensional variational （3DVar） assimilation system set up on global and regional grid meshes favorable for direct assimilation of the space-based remote sensing data and matching the frame work of the prediction model GRAPES. The state variables are assumed to decompose balanced and unbalanced components. By introducing a simple transformation from the state variables to the control variables with a recursive or spectral filter, the convergence rate of iteration for minimization of the cost function in 3DVar is greatly accelerated. The definition of dynamical balance depends on the characteristic scale of the circulation considered. The ratio of the balanced to the unbalanced parts is controlled by the prescribed statistics of background errors. Idealized trials produce the same results as the analytic solution. The results of real data case studies show the capability of the system to improve analysis compared to the traditional schemes, Finally, further development of the system is discussed.     

GRAPES 奇异向量研究及其在暴雨集合预报中的应用

为了产生好的集合预报初始扰动, 并能够用有限的集合样本来模拟大气概率密度函数在相空间中的时间演变, 把奇异向量法引入非静力 GRAPES 中尺度模式中, 研究了 GRAPES 奇异向量的基础性问题和基于奇异 向量构造集合样本的方法。对2008 年7 月的一次西南涡移动带来的暴雨过程进行 GRAPES SVs 求解, 并进行集合预报试验。结果表明: 前 27 个 GRAPES SVs 反映了分析误差的主要信息; 要素的集合平均的均方根误差比控制预报具有更好的预报技巧, 且它们的集合离散度随时间逐渐增加, 反映了预报误差的主要信息; 从降水 Brier 评分和 ROC 技巧上可以看出该集合预报具有好的概率预报技巧, 能为暴雨预报提供一定的指导作用。