AN IMPROVED METHOD FOR ANALYZING SPARSE AND IRREGULARLY DISTRIBUTED SST DATA ON A REGULAR GRID - THE TROPICAL PACIFIC-OCEAN

An improved method for interpolating sparsely sampled climatological d ata onto a regular grid is shown. The method uses the spatial and temp oral covariance of the field, along with the sparse data, to fill the full grid. This improves on similar methods that have recently been de veloped by eliminating the development of features that are not suffic iently supported by the data (i.e., overfitting). Statistical tests ar e used to tune the method to represent as much variability as the spat ial-temporal information will support without overfitting. The method is further improved by a data-checking procedure that detects and remo ves suspect data. The method is developed and evaluated by interpolati ng tropical Pacific sea surface temperature (SST) monthly anomalies to a regular grid for the 1856-1995 period. Ship data averaged to 5 degr ees squares are used as input and are interpolated to a complete 1 deg rees grid. Comparing the results to interpolations using other methods shows this method's quantitative improvements where satellite data ar e available fcr validation. Comparisons in the presatellite era show s harper and stronger anomaly patterns with this method, compared to ano ther method developed for use with sparse data. Also shown are several periods when data are so sparse that only very weak SST anomalies may be reliably reconstructed in the tropical Pacific (i.e., before 1870 and 1915-25). In future research, the global SST and possibly other cl imatological fields will be gridded using improved methods.