  Rainfall prediction helps planners anticipate potential social and economic
impacts produced by too much or too little rain. This research investigates a
class-based approach to rainfall prediction from 1-30 days in advance. The
study made regional predictions based on sequences of daily rainfall maps of
the continental US, with rainfall quantized at 3 levels: light or no rain;
moderate; and heavy rain. Three regions were selected, corresponding to three
squares from a $5\times5$ grid covering the map area. Rainfall predictions up
to 30 days ahead for these three regions were based on a support vector machine
(SVM) applied to consecutive sequences of prior daily rainfall map images. The
results show that predictions for corner squares in the grid were less accurate
than predictions obtained by a simple untrained classifier. However, SVM
predictions for a central region outperformed the other two regions, as well as
the untrained classifier. We conclude that there is some evidence that SVMs
applied to large-scale precipitation maps can under some conditions give useful
information for predicting regional rainfall, but care must be taken to avoid
pitfall
