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

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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.