Journal
ATMOSPHERIC RESEARCH
Volume 67-8, Issue -, Pages 367-380Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/S0169-8095(03)00068-1
Keywords
multiscale; storm identification; forecast
Categories
Ask authors/readers for more resources
We describe a recently developed hierarchical K-Means clustering method for weather images that can be employed to identify storms at different scales. We describe an error-minimization technique to identify movement between successive frames of a sequence and we show that we can use the K-Means clusters as the minimization template. A Kalman filter is used to provide smooth estimates of velocity at a pixel through time. Using this technique in combination with the K-Means clusters, we can identify storm motion at different scales and choose different scales to forecast based on the time scale of interest. The motion estimator has been applied both to reflectivity data obtained from the National Weather Service Radar (WSR-88D) and to cloud-top infrared temperatures obtained from GOES satellites. We demonstrate results on both these sensors. (C) 2003 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available