A Development of Data Assimilation Error Estimation Method for a General Ocean Circulation Model

一般海洋环流模型数据同化误差估计方法的发展

• 批准号: 251251156
• 负责人: Dr. Andriy Vlasenko, Ph.D.
• 金额: --
• 依托单位: Institut für Meereskunde (IMee)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/251251156?language=en
• 关键词: Development; Data; Assimilation; Error; Estimation

The 4D-variational data assimilation procedure (4D-var DA) is a special routine used for correction of climate/weather forecasts by tuning the climate model parameters in a way that provides the best possible fit to the available observational data. Due to a number of reasons DA introduces its own inevitable methodological error which ultimately affects the accuracy of the model forecast. The existing methods designed for the reduction of this uncertainty require a lot of computational resources. This is the reason why their usage in many climate models is restricted by some simplified versions. It is proposed in this project to develop a conceptually novel, robust, and efficient nonlinear variational error estimation algorithm (NOVEEA) which can estimate the inaccuracy of the DA methods and can make the corresponding corrections quite efficient computationally. Specifically, it is planned to develop the NOVEEA as an application to geophysical climate forecasting systems.The advantage of the proposed method is that the computational algorithm is based on an abstract mathematical 4D-var DA problem which allows using it in a wider geophysical context. Another innovation of this project is an opportunity to provide an easy and practical way to calculate an inverse error covariance matrix used in the DA. In comparison with the existing methods the proposed procedure is more computationally efficient. As the expected deliverables of the project, it is planned to disseminate all theoretical results nationally and internationally, as well as to provide an open access to all computational software supporting the newly developed NOVEEA.

四维变分同化程序（4D-var DA）是一种特殊的程序，用于通过调整气候模式参数来校正气候/天气预报，以提供与现有观测数据的最佳拟合。由于多种原因，DA引入了自己不可避免的方法错误，最终影响模型预测的准确性。为减少这种不确定性而设计的现有方法需要大量的计算资源。这就是为什么它们在许多气候模式中的使用受到一些简化版本的限制。在这个项目中，提出了一个概念新颖，强大的，有效的非线性变分误差估计算法（NOVEEA），它可以估计的DA方法的不准确性，并可以使相应的校正相当有效的计算。具体来说，它计划开发的NOVEEA作为地球物理气候预报systems.The的优点所提出的方法的应用是，计算算法是基于一个抽象的数学4D-VAR DA问题，允许使用它在更广泛的地球物理背景下。该项目的另一个创新是提供了一个简单实用的方法来计算逆误差协方差矩阵中使用的DA的机会。与现有的方法相比，所提出的程序是计算效率更高。作为该项目的预期成果，计划在国内和国际上传播所有理论成果，并提供对支持新开发的NOVEEA的所有计算软件的开放访问。