Summary A new method of four-dimensional data assimilation based on Singular Value Decomposition (SVD) is proposed. In it, a set of
atmospheric states is obtained by integrating a numerical weather prediction model and simulated observations are taken and
calculated from the model variables. Then the SVD technique is used to create the base vectors from this coupled data set.
Finally, the analysis is obtained by projecting actual observation data into a space spanned by the base vectors. Using this
approach, the four-dimensional data assimilation becomes a simple linear inverse problem the linearization of the nonlinear
forward model is avoided, and the developments of the adjoint and background error covariance matrix are no longer needed.
Since the SVD technique is used here, the method is simply called 4DSVD. 相似文献
传统的海洋环境数据集成与共享存在共享模式单一、数据服务效果不明显等突出问题。随着云计算等技术的出现,数据共享模式发生了巨大的变化。提出基于云计算的海洋环境数据共享体系,通过基础设施即服务(infrastructure as a service,IaaS)、数据资源即服务(data as a service,DaaS)及软件即服务(software as a service,SaaS)实现海洋环境数据共享模式的转变。在海洋环境数据云中,用户既是数据的使用者也是数据的提供者,通过数据发布与发现、数据需求发布与发现等功能,解决数据共享中“用户-数据”之间的矛盾,并激励普通海洋科研工作者贡献自己的数据,保障数据资源有效、可持续利用。以南海海域为实验区,以表层漂流浮标数据及中尺度涡旋数据为实验数据,构建了原型系统验证该方法。 相似文献