One issue that could benefit from input from the field of statistics is the question of what method to use to interpolate irregularly spaced data to a regular grid in a manner that preserves the statistics of the field of interest (cf., NRC, 1991b). For example, satellite data generally consist of high-resolution data within measurement swaths, separated by hundreds or thousands of kilometers for which there are no data between swaths. Most interpolation methods smooth the data and minimize spatial gradients. It is desirable to retain as much of the full range of spatial scales as possible in the gridded fields.
Another issue that oceanographers are concerned with and that statisticians could contribute to is determining a method of identifying “interesting” events in the data that warrant a more detailed analysis. With small data sets, this can be accomplished by simply examining all of the data by various graphical techniques. For large satellite data sets or numerical model output, it is highly desirable to develop automated methods of locating such features. This can be done (with some success) for specific events with easily characterized features, but it is difficult when features are difficult to characterize concisely or do not possess simple characterizations.