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

4d变分数据同化程序（4D-var DA）是一种特殊的程序，用于校正气候/天气预报，通过调整气候模式参数，使其尽可能与现有观测数据最拟合。由于多种原因，数据分析不可避免地引入了方法误差，最终影响了模型预测的准确性。现有的减少这种不确定性的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。本课题提出了一种概念新颖、鲁棒且高效的非线性变分误差估计算法（NOVEEA），该算法可以估计数据分析方法的不准确性，并可以相当有效地进行相应的计算修正。具体而言，计划将NOVEEA开发为地球物理气候预报系统的应用程序。所提出的方法的优点是，计算算法是基于一个抽象的数学4D-var DA问题，允许在更广泛的地球物理背景下使用它。该项目的另一项创新是提供了一种简单实用的方法来计算数据分析中使用的逆误差协方差矩阵。与现有的方法相比，该方法具有更高的计算效率。作为该项目的预期交付成果，计划在国内和国际上传播所有理论成果，并为支持新开发的NOVEEA的所有计算软件提供开放访问。

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.