Data assimilation schemes for ocean forecasting: state of the art

作者：Matthew Martin, Ibrahim Hoteit, Laurent Bertino, Andrew M. Moore · 年份：2025 · DOI：10.5194/sp-5-opsr-9-2025 · 被引用次数：9 · 研究领域：Oceanographic and Atmospheric Processes、Meteorological Phenomena and Simulations、Climate variability and models

Abstract. Data assimilation (DA) is a process for integrating models and observations into comprehensive and reliable estimates of the ocean state. It is used to produce near-real-time initial conditions (analyses) from which ocean forecasts are produced and to generate reconstructions of the past state of the ocean (reanalyses). Here we provide an overview of the methods currently used in ocean systems for assimilating satellite and in situ observations, together with a brief review of methods being developed which will be implemented in future operational systems, including the use of machine learning (ML) techniques that provide a way to improve their efficiency. A list of data assimilation software used by most of the global and regional operational ocean forecasting systems is provided, together with the availability of each software. A discussion of practical considerations for employing data assimilation software and techniques operationally is also given, including the types of observations which are commonly used, and the implementation choices made by existing operational systems at global and regional scales is summarised.