Abstract
Squall lines are strong indicators of potential severe weather. Yet, automated positioning and tracking algorithms are not common. We propose three different ways to model and identify squall lines using radar images. The three methods are ellipse fitting, Hough transform, and the use of a genetic algorithm-based framework. They model a squall line as an ellipse, a straight line, and adjoining segments of arc respectively. We compare the advantages and limitations of each method in terms of speed, flexibility, stability and sensitivity to parameter settings. It is found that ellipse fitting is the most efficient, followed by Hough transform. Both methods lack flexibility and stability. The genetic algorithm-based framework is stable, has flexibility in modelling and analysis, but comes with a cost of efficiency. The proposed methods provide independent and objective information sources to assist weather forecast.
The authors are thankful to the Hong Kong Observatory for the provision of data and expert advices.
An erratum to this chapter is available at http://dx.doi.org/10.1007/11893011_163.
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Wong, K.Y., Yip, C.L. (2006). Comparison of Squall Line Positioning Methods Using Radar Data. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_35
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DOI: https://doi.org/10.1007/11893011_35
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