Altimeter data and geoid error in mesoscale ocean prediction: Some results from a primitive equation model

Abstract

A primitive equation ocean model has been applied to the study of mesoscale ocean dynamics and prediction using sea surface height information derived from a satellite‐borne altimeter. Results from a model of the Gulf of Mexico were directly compared with the altimeter data from GEOS 3 and Seasat reported by Marsh et al. (1984) and in situ hydrographic data analyzed by Maul and Herman (1985). In the eastern Gulf the amplitude and position of relative maxima of sea surface height variability were found to be similar for model, altimeter, and in situ data. In the central Gulf the model and the altimeter‐derived sea surface height variability maps were similar but differed considerably from the in situ results. The mean sea surface in the model and from the in situ data also agreed well in the eastern Gulf, but there was little agreement in the central Gulf. While there is no geoid adequate for determining an independent altimetric estimate of the mean dynamic height of the sea surface in the Gulf by differencing the geoid with the altimeter‐derived mean sea surface, the model dynamic mean sea surface can be subtracted from the mean surface of Marsh et al. (1984) to obtain a new estimate of the Gulf geoid, which may be the best geoid available in that region for several years. A benchmark experiment (“truth”) was integrated to statistical equilibrium and compared with results from four experiments in which the model was initialized with fields modified from archived benchmark data. The experiments differed only in the initialization fields. Each experiment was integrated for 100 days with inflow transport remaining constant throughout the integration. The idealized experiments were initialized geostrophically (1) with the exact sea surface and pycnocline height fields, (2) with only the exact sea surface heights and a pycnocline assumed to compensate it such that the deep pressure perturbations vanished, (3) just as in case 2 but with a geoid error component added on small spatial scales, and (4) just as in case 3 except the geoid error model included an additional contribution in strong geoid gradient regions. The sequence of four numerical experiments showed that (1) geostrophic initialization with exact sea surface and pycnocline height information provided accurate forecasts to 100 days, (2) even when only the sea surface height information was provided to the numerical model for geostrophic initialization and deep pressure perturbations assumed to vanish at the initial time, the forecasts were superior to persistence or climatology over the 100‐day forecast period, and (3) errors in the geoid on spatial scales comparable to the grid resolution of the model did not seriously degrade the forecast, even in dynamically active regions with large gradients in the geoid height where instability processes were observed to occur.

Observed and simulated kinematic properties of Loop Current rings

“…The second and related issue is the use of Lagrangian observations for updating prognostic models. In character, such data are similar to sea surface topographic data derived from satellites ['see tturlburt, 1986; Thompson, 1986;Kindle, 1986]. The Lagrangian data contain information on the motion of representative parcels but no information on what is happening nearby.…”

The 26‐ and 50‐day oscillations in the western Indian Ocean: Model results

“…The Indian Ocean model was adapted from an earlier rectangular domain, primitive equation formulation for the Gulf of Mexico by Hurlbutt and Thompson [1980] and extended [Wallcraft and Thompson, 1984] to include irregular coastlines. Benchmark experiments using that model have been described by Thompson [1986] and by Hurlburt [1984].…”

Statistical inference of weakly correlated subthermocline fields from satellite altimeter data

“…The same phenomenon is seen in reverse in Figure 2a where the error in P l decreases at first when the initial error is 50% in p•, 0% in p2. In Figure 2a one might be tempted to attribute this to selective damping of the error by the ocean model as found by Thompson [1986]. To avoid this, the decorrelation space scales in the error and in the temporal variability of the true field were made approximately the same.…”

“…The error is largest near and slightly after eddy-shedding events such as depicted in Figure 5a. The timing of these events is shown by large tick marks at the top Thompson [1986] to study the impact of errors in altimeter data and the geoid on mesoscale ocean prediction in the Gulf of Mexico. In this case a large eddy in P2 is associated with a large eddy in p l which is laterally displaced by 220 km.…”