An experimental coupled forecast system at the National Meteorological Center
ABSTRACT An experimental coupled ocean-atmosphere model forecast system has been implemented at the National Meteorological Center (NMC) for routine multi-season climate forecasts. The ocean initial conditions for the forecasts are provided by an ocean data assimilation system which uses a basin model of the Pacific Ocean integrated with a four-dimensional variational data assimilation system. Pacific basin ocean reanalyses for the period 1982–1992 provided both the initial conditions and the verification fields for the coupled model forecasts. The coupled model consists of a modified T40 version of NMC's operational medium range forecast model coupled to a Pacific basin ocean general circulation model. Hindcast experiments starting on the first of every month from October 1983–1993 have been made to establish the skill of this system in forecasting sea surface temperature variability in the tropical Pacific with lead times of up to several seasons. The system consistently out performs persistence forecasts even at short forecast lead times, and is able to forecast most of the sea surface temperature variability that occurred in the tropical Pacific during 1984–1993.
- Monthly Weather Review 01/2009; 137(9):2908-2930. · 2.76 Impact Factor
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ABSTRACT: El Niño-Southern Oscillation (ENSO) is by far the most energetic, and at present also the most predictable, short-term fluctuation in the Earth’s climate system, though the limits of its predictability are still a subject of considerable debate. As a result of over two-decades of intensive observational, theoretical and modeling efforts, ENSO’s basic dynamics is now well understood and its prediction has become a routine practice at application centers all over the world. The predictability of ENSO largely stems from the ocean–atmosphere interaction in the tropical Pacific and the low-dimensional nature of this coupled system. Present ENSO forecast models, in spite of their vast differences in complexity, exhibit comparable predictive skills, which seem to have hit a plateau at moderate level. However, mounting evidence suggests that there is still room for improvement. In particular, better model initialization and data assimilation, better simulation of surface heat and freshwater fluxes, and better representation of the relevant processes outside of the tropical Pacific, could all lead to improved ENSO forecasts.Journal of Computational Physics. 01/2008;
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ABSTRACT: A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared.Ocean Modelling 01/2006; · 2.63 Impact Factor