Assimilation of satellite altimeter data in a primitive-equation model of the Azores–Madeira region

Abstract

Abstract The aim of this study is to implement satellite altimetric assimilation into a high-resolution primitive-equation ocean model and check the validity and sensitivity of the results. Beyond this paper, the remote objective is to get a dynamical tool capable of simulating the surface ocean processes linked to the air–sea interactions as well as to perform mesoscale ocean forecasting. For computational cost and practical reasons, this study takes place in a 1000 by 1000 sq km open domain of the Canary basin. The assimilation experiments are carried out with the combined TOPEX/POSEIDON and ERS-1 data sets between June 1993 and December 1993. The space–time domain overlaps with in situ data collected during the SEMAPHORE experiment and thus enables an objective validation of the results. A special boundary treatment is applied to the model by creating a surrounding recirculating area separated from the interior by a buffer zone. The altimetric assimilation is done by implementing a reduced-order optimal interpolation algorithm with a special vertical projection of the surface model/data misfits. We perform a first experiment with a vertical projection onto an isopycnal EOF representing the Azores Current vertical variability. An objective validation of the model's velocities with Lagrangian float data shows good results (the correlation is 0.715 at 150 dbar). The question of the sensitivity to the vertical projection is addressed by performing similar experiments using a method for lifting/lowering of the water column, and using an EOF in Z -coordinates. Some comparisons with in situ temperature data do not show any significant difference between the three projections, after five months of assimilation. However, in order to preserve the large-scale water characteristics, we felt that the isopycnal projection was a more physically consistent choice. Then, the complementary character of the two satellites is assessed with two additional experiments which use each altimeter data sets separately. There is an evidence of the benefit of combining the two data sets. Otherwise, an experiment assimilating long-wavelength bias-corrected CLS altimetric maps every 10 days exhibits the best correlation scores and emphasizes the importance of reducing the orbit error and biases in the altimetric data sets. The surface layers of the model are forced using realistic daily wind stress values computed from ECMWF analyses. Although we resolve small space and time scales, in our limited domain the wind stress does not significantly influence the quality of the results obtained with the altimetric assimilation. Finally, the relative effects of the data selection procedure and of the integration times (cycle lengths) is explored by performing data window experiments. A value of 10 days seems to be the most satisfactory cycle length.