An automatic satellite interpretation of tropical cyclone patterns using elastic graph dynamic link model
In the past decades, satellite interpretation was one of the vital methods for the determination of weather patterns all over the world, especially for the identification of severe weather patterns such as tropical cyclones (TC). The method is based on Dvorak Technique(8) which provides a means of the identification of the cyclone and its intensity. This is a kind of pattern-matching techniques and is based on some well-known TC templates for reference. Due to the high variation and complexity of cloud activities for the tropical cyclone patterns, meteorological analysts all over the world so far are still relying on subjective human justification for TC identification purposes. In this paper, an Elastic Graph Dynamic Link Model (EGDLM) is proposed to automate the satellite interpretation process and provides an objective analysis for tropical cyclones. The method integrates Dynamic Link Architecture (DLA) for neural dynamics and Active Contour Model (ACM) for contour extraction of TC patterns. Over 120 satellite pictures provided by National Oceanic and Atmospheric Administration (NOAA) were used to evaluate the system, and 145 tropical cyclone cases that appeared in the period between 1990 to 1998 were extracted for the study. An overall correct rate for TC classification was found to be above 95%. For hurricanes with distinct "eye" formation, the model reported a deviation within 3 km from the "actual eye" location, which was obtained from the reconnaissance aircraft measurements of minimum surface pressure.
elastic graph dynamic link model active contour model dvorak technique neural network elastic graph matching