Use of Reduced Gaussian Grids in Spectral Models

Abstract

Integrations of spectral models are presented in which the ?Gaussian? grid of points at which the nonlinear terms are evaluated is reduced as the poles are approached. A maximum saving in excess of one-third the number of points covering the globe is obtained by requiring that the grid length in the zonal direction does not exceed the grid length at the equator, and that the number of points around a latitude circle enables the use of a fast Fourier transform. Tests are reported for Eulerian and semi-Lagrangian barotropic models, mostly at T106 resolution, and a summary is given of experiments based on the T106 primitive-equation model used for operational forecasting at ECMWF. The results show that such a reduced grid can be used for short- and medium-range prediction (and presumably also for climate studies) with no significant loss of accuracy compared with use of a conventional grid, which is uniform in longitude. The saying in computational time is between 20% and 25% for the T1O6 forecast model. There are also potential reductions in the memory requirement of the model and in the storage needed for the archiving of model results.