Bruce C. Douglas

Calculation of sea level time series from noncollinear GEOSAT altimeter data

Abstract Oceanic temporal variability is calculated from satellite altimeter data by differencing sea level measurements repeated at the same location. These differences yield variations independent of any permanent undulations of sea level. If satellite groundtracks are collinear (nested), altimeter measurements are repeated in a regular fashion continuously along a repeating groundtrack, and calculation of sea level variation is straightforward. For an orbit that repeats only at intersections (crossovers) of the groundtrack, the problem is more complicated, but results equal in accuracy to those obtainable from a collinear analysis can be achieved. For both collinear and crossover analyses, long wavelength error in the altitude of the satellite must be eliminated by using a model for the orbit error and adjusting each pass of data into a reference pass in its nest for the collinear case or into a reference grid of intersecting passes for nonnested tracks.

A comparison of gravity prediction methods on actual and simulated data

A comparison was made between Shepard’s method (inverse‐distance weighting) and collocation (linear filtering) for the purpose of predicting gravity anomalies. Tests were made with actual data from southern California and with simulated data created from buried point masses generated by a random number generator. The autocorrelation functions of the simulated and actual gravity data behaved very much alike. In general, the sophisticated collocation method did produce better results and very good variance estimates, compared with Shepard’s method, for simulated data. The advantage was less for actual data. The cost of the better results is the use of more computer time. The most important scientific conclusion of this study is that careful trend removal must be done and an adequate data sample obtained to produce truly optimal results from collocation. The variance estimates are much more sensitive to the form and calibration of the model autocorrelation function than are the prediction results.