The classical Asian game of Go presents a difficult challenge for artifical intelligence programmers because of its vast search space and the difficulty of static board evaluation. One of the techniques humans employ while playing Go is spatial localization - they direct their attention toward the important points on the board and ignore the unimportant areas. This note describes using statistical covariance as a tool to execute spatial localization in our Go program, OREGO.

Estimates of dynamic ocean (or sea surface) topography based on satellite altimetry and gravity observations generally become degraded as they approach land. In this study, dynamic ocean topography for the northeast Pacific Ocean is computed independently of satellite observations using a high resolution model and seasonal climatologies of temperature, salinity, and wind stress. Comparisons with estimates based on satellite gravity and altimetry measurements show reasonable agreement in the deep ocean but are poor on continental margins where the ocean model estimates not only reveal significant seasonal differences, but are also shown to be reasonably accurate when compared with satellite altimetry and coastal tide gauge measurements. The dynamic ocean topography estimates provided here will permit more accurate calculations of the geoid and satellite altimeter absolute heights and currents.