Finding Structures in Weather Maps

We describe an eeective computational method for extracting structural information from spatial data sets. For instance, the structural information such as the location, shape, size, and movement about pressure cells and troughs are important for weather prediction. We are interested in extracting these structures and representing them at aggregate levels where salient geometric features are highlighted. The computational challenge is to quantify these features at multiple levels of granularity and nd them incrementally. We introduce two types of spatial adjacency relations for spatial objects and present an eeective algorithm for extracting and abstracting structural objects using the relational information. An application to nding local pressure cells in the weather data sets is demonstrated. We thank Chris Bailey-Kellogg for constructive comments on an early version of the paper, and Chris and Ivan Ordonez for providing the basic SAL computational environment to experiment with many of the ideas developed in this paper.