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Paola Malanotte-Rizzoli and Roberta E. Young


The major objective of oceanic data assimilation studies has been thus far to obtain a four-dimensional realization (space plus time) of the oceanic flow simultaneously consistent with the observations and the model dynamics. In these latest years, however, the forecasting of oceanic motions has emerged as a legitimate and important goal per se. In particular, the operational prediction of mesoscale flows and frontal systems has been the objective of recent assimilation applications in various regional systems of the World Ocean. One such effort focused on the short-term prediction of the Gulf Stream system in the DAMEE GSR (Data Assimilation and Model Evaluation Experiments Gulf Stream Region) sponsored by the U.S. Navy. The objective of DAMEE GSR phases I and II was 1–2-week forecast experiments. Phase III extended the suite of case studies by adding a 2-month-long assimilation experiment to assess the impact of long-term assimilations on model performance and forecasting skill.

In this paper the authors report the results of DAMEE GSR phase III but broaden the perspective by addressing two further issues, namely, the model sensitivity to the choice of the initial fields and the frequency of intermittent data assimilation. Two versions of the OTIS-3 (Optimum Thermal Interpolation System) of the U.S. Navy Fleet Numerical Oceanography Center were available, providing slightly different distributions of temperature and salinity over the entire Gulf Stream system. They are referred to as OTIS-3a, available with biweekly frequency from 4 May 1988 through 28 December 1988, in the context of a different assimilation work; and OTIS-3b, provided by the DAMEE GSR phase III effort, for the 2-month period 4 May–4 July 1988, with a slightly irregular frequency, weekly on the average. The main results can be summarized as follows.

The intermittent assimilation of the OTIS-3b datasets with average weekly frequency profoundly improves the model forecasting skill. Without assimilation the model never beats persistence. With the assimilation, the model-predicted Gulf Stream north wall is in excellent agreement with the verification infrared (IR) north wall, remaining always within the error bar of the IR north wall estimate, ±15km.

Two types of sensitivity experiments to the initial conditions were carried out: first, reconstruction of the initial fields with the two different OTIS-3a and OTIS-3b datasets but with the same initialization method; second, reconstruction of the initial fields with the same OTIS-3a dataset but with two different initialization methods. The results show that the initial velocity field is much more crucial in affecting the model evolution and hence its predictive skill as it determines the stability properties of the Gulf Stream jet. Hence, it is very important to use the same dynamical initialization for velocity when starting from different distributions of temperature and salinity, as the jet profiles thus obtained will be very similar in structure and strength. This identical dynamical initialization will allow meaningful comparisons of experiments that start from slightly different density distributions.

Finally, we compare weekly assimilations of OTIS-3b with biweekly and monthly assimilations of OTIS-3a, initialized with the same procedure. The authors conclude that a weekly assimilation of the global OTIS-3 dataset is not necessary and that a biweekly assimilation is equally effective in improving the model predictive skill.

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