Enhanced ocean temperature forecast skills through 3‐D super‐ensemble multi‐model fusion

Abstract

[1] An innovative multi-model fusion technique is proposed to improve short-term ocean temperature forecasts: the threedimensional 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 multimodel 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 rootmean-square differences with observations are reduced by 57% and 35% respectively, making this new technique a suitable non-intrusive post-processing method for multimodel operational forecasting systems.

Development of super-ensemble techniques for ocean analyses: the Mediterranean Sea case

“…Krishnamurti's SE could uptake both Asian monsoon precipitation simulations and hurricane track-intensity forecasts. In his approach all observations have equal importance, so Lenartz et al (2010) applied this method for ocean wave forecasting, introducing a way to change the importance in the observation using data assimilation techniques (Kalman filter and particle filter) adapted to the super-ensemble paradigm. With this technique the regression weights change on a timescale corresponding to their natural characteristic time, discarding older information automatically, and rate of change is determined by the joint uncertainties of the weights, models and observations.…”

“…To assess and control these uncertainties, an ensemble approach can be used as shown, for example, by Kalnay and Ham (1989), where the simple ensemble mean is shown to have a smaller root mean square error (RMSE) than each contributing member. The assumption that different models may have complementary forecasting and analysis skills emerged from the pioneering work of Lorenz (1963), in which the notion of an ensemble forecast was first described, which was obtained by factorizing all the members' performances. Most common ensemble forecasts came from a single model running with a set of perturbed initial, lateral or vertical boundary conditions.…”

“…Only preliminary work has been carried out in ocean forecasting. The work of Rixen et al (2009) is a reference for temperature predictions; simulated ocean trajectory SE methods are described in Vandenbulcke et al (2009);and Lenartz et al (2010) present a SE technique with a Kalman filter to adjust three-dimensional model weights. Rixen et al (2008) introduced the concept of a hyper-ensemble, which combines atmospheric and oceanographic model outputs.…”

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

“…Moreover the spring stratification is a crucial phenomenon for the onset of phytoplankton blooms in this area (Mahadevan et al, 2012). Other studies quantifying the uncertainties in the ocean forecast for several oceanic fields (Lermusiaux et al, 2006) made use of superensemble techniques (Vandenbulcke et al, 2009;Lenartz et al, 2010;Pistoia, 2012;Scott et al, 2012) or have quantified the impact of medium-range atmospheric forecasting on the ocean (Drillet et al, 2009). An ensemble approach is also used in oceanography for estimating variability at a more climatic scale, for example in Zhu et al (2012), Xue et al (2012) and more recently in the Clivar Exchange special issue (http://www.clivar.org/node/1507).…”

Uncertainty estimation for operational ocean forecast products—a multi-model ensemble for the North Sea and the Baltic Sea

“…Weisheimer et al (2009) used five equally weighted coupled atmosphereocean circulation models to study Pacific SST by comparison with a previous-generation ensemble, DEMETER (Palmer et al 2004;Doblas-Reyes et al 2005), yielding a higher skill for the new multi-model ensemble. More weighted 3D multi-model ensembles have been developed for SST forecasts by applying BMA or a Kalman Filter over a learning period for determining the optimal weights between the models (Logutov and Robinson 2005;Raftery et al 2005;Lenartz et al 2010;Mourre et al 2012). The super-ensembles have been validated against in situ data of CTD, gliders, drifter, and scan fish.…”