Abstract
Abstract. A super-ensemble methodology is proposed to improve the quality of short-term ocean analyses for sea surface temperature (SST) in the Mediterranean Sea. The methodology consists of a multiple linear regression technique applied to a multi-physics multi-model super-ensemble (MMSE) data set. This is a collection of different operational forecasting analyses together with ad hoc simulations, created by modifying selected numerical model parameterizations. A new linear regression algorithm based on empirical orthogonal function filtering techniques is shown to be efficient in preventing overfitting problems, although the best performance is achieved when a simple spatial filter is applied after the linear regression. Our results show that the MMSE methodology improves the ocean analysis SST estimates with respect to the best ensemble member (BEM) and that the performance is dependent on the selection of an unbiased operator and the length of training. The quality of the MMSE data set has the largest impact on the MMSE analysis root mean square error (RMSE) evaluated with respect to observed satellite SST. The MMSE analysis estimates are also affected by training period length, with the longest period leading to the smoothest estimates. Finally, lower RMSE analysis estimates result from the following: a 15-day training period, an overconfident MMSE data set (a subset with the higher-quality ensemble members) and the least-squares algorithm being filtered a posteriori.
Highlights
The limiting factors to short-term ocean forecasting predictability are the uncertainties in ocean initial conditions, atmospheric forcing (Pinardi et al, 2011), lateral boundary conditions tighter with numerical model representation and numerical inaccuracies
In this paper we develop a new multi-model super-ensemble (MMSE) method to estimate sea surface temperature (SST) as this is an important product of ocean analysis systems with multiple users
We developed a multi-model multi-physics super-ensemble methodology to estimate the best SST from different oceanic analysis systems
Summary
The limiting factors to short-term ocean forecasting predictability are the uncertainties in ocean initial conditions, atmospheric forcing (Pinardi et al, 2011), lateral boundary conditions tighter with numerical model representation and numerical inaccuracies. Most common ensemble forecasts came from a single model running with a set of perturbed initial, lateral or vertical boundary conditions. The implicit hypothesis is that forecast errors arise from inaccurate initial/boundary conditions, while the model is considered as being perfect. Feddersen et al (1999) reported that the low ensemble spread is likely to be produced by correlated models; only a set of different models is expected to reduce the model systematic error. Shukla et al (2000) proposed a combination of member predictions with similar forecast skills in order to further reduce Feddersen et al (1999) reported that the low ensemble spread is likely to be produced by correlated models; only a set of different models is expected to reduce the model systematic error. Shukla et al (2000) proposed a combination of member predictions with similar forecast skills in order to further reduce
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