OS Ocean Science OS Ocean Sci. 1812-0792 Copernicus Publications Göttingen, Germany 10.5194/os-2-213-2006 Study of the combined effects of data assimilation and grid nesting in ocean models – application to the Gulf of Lions Vandenbulcke L. 1 Barth A. 2 Rixen M. 3 Alvera-Azcarate A. 2 Ben Bouallegue Z. 1 Beckers J. M. 1 GeoHydrodynamics and Environment Research, Université de Liège, Belgium Ocean Circulation Group, University of South Florida, College of Marine Science. Florida 33701, USA NATO/SACLANT Undersea Research Centre, La Spezia, Italy 31 10 2006 2 2 213 222 Copyright: © 2006 L. Vandenbulcke et al. 2006 This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Generic License. To view a copy of this licence, visit https://creativecommons.org/licenses/by-nc-sa/2.5/ This article is available from https://os.copernicus.org/articles/2/213/2006/os-2-213-2006.html The full text article is available as a PDF file from https://os.copernicus.org/articles/2/213/2006/os-2-213-2006.pdf

Modern operational ocean forecasting systems routinely use data assimilation techniques in order to take observations into account in the hydrodynamic model. Moreover, as end users require higher and higher resolution predictions, especially in coastal zones, it is now common to run nested models, where the coastal model gets its open-sea boundary conditions from a low-resolution global model. This configuration is used in the "Mediterranean Forecasting System: Towards environmental predictions" (MFSTEP) project. A global model covering the whole Mediterranean Sea is run weekly, performing 1 week of hindcast and a 10-day forecast. Regional models, using different codes and covering different areas, then use this forecast to implement boundary conditions. Local models in turn use the regional model forecasts for their own boundary conditions. This nested system has proven to be a viable and efficient system to achieve high-resolution weekly forecasts. However, when observations are available in some coastal zone, it remains unclear whether it is better to assimilate them in the global or local model. We perform twin experiments and assimilate observations in the global or in the local model, or in both of them together. We show that, when interested in the local models forecast and provided the global model fields are approximately correct, the best results are obtained when assimilating observations in the local model.

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