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A stochastic operational forecasting system of the Black Sea: technique and validation
Vandenbulcke, L.; Barth, A. (2015). A stochastic operational forecasting system of the Black Sea: technique and validation. Ocean Modelling 93: 7-21. dx.doi.org/10.1016/j.ocemod.2015.07.010
In: Ocean Modelling. Elsevier: Oxford. ISSN 1463-5003, more
Peer reviewed article  

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Keyword
    Marine
Author keywords
    Operational oceanography; Ensemble forecast; Uncertainty; Dataassimilation; Black Sea

Authors  Top 
  • Vandenbulcke, L., more
  • Barth, A., more

Abstract
    In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The forecasting system also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the forecasting system is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the forecast errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model forecasts are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well.

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