THE IMPACT OF OBSERVATIONAL ERRORS ON OBJECTIVE ANALYSES

The impact of observational errors on objective analyses is investigat ed with mathematical analyses, analytical examples, and real data expe riments. Cases with observational errors at one or more stations are c onsidered. It is found that in the presence of observational errors, t he analysis error in an objective analysis scheme generally consists o f two parts: the signal fitting error and the noise contamination erro r. Although every objective analysis scheme has its own procedure(s) t o control the two errors, the procedures to suppress the noise contami nation error in one and two dimensions are shown to be relatively inef fective. It is shown that the extension of an objective analysis metho d to more dimensions significantly reduces the noise contamination. Ba sed on these results, higher dimensional versions of the least squares polynomial fitting (LSPF) methods and the Barnes scheme are examined. In both analytic and real data experiments, the 3D and 4D LSPF method s and the 3D Barnes scheme show an enhanced ability to filter observat ional noise.