Operational prediction of acoustic properties in the ocean using multi-model statistics

Abstract

Super-ensemble (SE) multi-model forecasts optimize local combination of individual models which is superior to individual models because they allow for local correction and bias removal. Multi-model statistics are applied to optimize the forecast skills from ocean models with different resolution or configuration, run operationally during the MREA04 field experiment off the West coast of Portugal. The method, based on a training/forecast cycle uses linear regression optimization. The performance and the limitations of the different super-ensemble combinations and the individual models are discussed. The SE method is shown to reduce errors in sound velocity significantly for 24 h forecasts.