There exist several methods of estimating gridded fields of divergence from irregularly spaced observations. In the finite differencing scheme, objective analysis yields gridded fields of data from which spatial derivative estimates are made. In the triangle and pentagon methods, derivative estimates are made directly from observed data, which is then analyzed to a grid using objective analysis. Previous studies suggest that the triangle method provides estimates superior to the finite differencing scheme. Using analytic observations, this study evaluates the relative effectiveness of the finite differencing and triangle methods as well as the pentagon method. For the evaluation, a real, declustered station network is used. Error statistics are evaluated over the inner one-half of the region.

Results confirm, as in previous studies, that the triangle method provides a better result than the finite differencing method. However, in three of the four wavelengths considered, the rmse values before analysis are lower for the pentagon method than the triangle method, related to the triangle method's linearity assumption. However, after the irregularly spaced divergence values are analyzed, rmse values show that the triangle method offers the best analysis. Because of the large pentagon areas relative to the triangle areas, the pentagon method suffers in the analysis.