An innovative multi-model fusion technique is proposed to improve short-term ocean temperature forecasts: the three-dimensional super-ensemble. In this method, a Kalman Filter is used to adjust three-dimensional model weights over a past learning period, allowing to give more importance to recent observations, and take into account spatially varying model skills. The predictive performance is evaluated against SST analyses, CTD casts and gliders tracks collected during the Ligurian Sea Cal/Val 2008 experiment. Statistical results not only show a very significant bias reduction of this multi-model forecast in comparison with the individual models, their ensemble mean and a single-weight-per-model version of the super-ensemble, but also the improvement of other pattern-related skills. In a 48-h forecast experiment, and with respect to the ensemble mean, surface and subsurface root-mean-square differences with observations are reduced by 57% and 35% respectively, making this new technique a suitable non-intrusive post-processing method for multi-model operational forecasting systems.
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