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System Design for Ocean Forecasting: Interdisciplinary Approach in Data Assimilation

Balla MAGGERO (Kenya Meteorological Services)

The scientific advances in ocean modelling, data assimilation and the observing systems over the past decade have made the grand challenge of ocean forecasting an achievable goal with the implementation of the first generation systems. Implementation of these components into a truly operational forecasting system introduces a number of unique constraints that lead to reduced performance. These constraints, e.g. the limitations in the coverage and quality of critical components of the observing systems in real-time as well as the constraints of completing forecast integrations within a fixed schedule are unavoidable components for any forecast system and additional strategies to achieve robustness and maximize performance.
Here, an approach on both fundamental dynamics and applications in data assimilation is presented. This technology, which blends measurements with dynamical models is emerging as a novel and powerful methodology. However, computational costs are challenges that needs to be overcome. The concepts are built upon an interdisciplinary system for assessing system performance. The end result is used to estimate uncertainties and feedbacks. This involves a full, interdisciplinary state vector and error covariance matrix. An idealized end-to-end system example is presented.