In this paper, we describe our progress in identifying and tracking storms at multiple scales from satellite infrared (11-micron Band 4) and visible (Band 1) channels. Storms are identified by clustering the pixels in the input images using spatial-contiguity-enhanced K-means clustering. Identified clusters are then processed morphologically to yield self-consistent storms.

Identified storms (at all the scales) are tracked using a hybrid scheme that minimizes mean absolute error between frames of the input sequence of images and then smoothed temporally using Kalman filtering. This yields a grid of motion vectors at each pixel in the spatial domain.

The motion vector estimated from the sequence is used to nowcast the images. Comparison of the nowcasts with the observed values at the corresponding time gives a measure of skill of the nowcast.

Statistical properties are extracted for each cluster. The extracted properties are used as inputs to an automated decision tree training algorithm to identify regions of overshooting tops.

Results and measures of skill are demonstrated on a sequence of images from Oct. 12, 2001.