85th AMS Annual Meeting

Thursday, 13 January 2005: 4:15 PM
Data Mining for Tropical Cyclone Intensity Prediction
Jiang Tang, George Mason University, Fairfax, VA; and R. Yang and M. Kafatos
Poster PDF (67.1 kB)
Tropical cyclone (TC) intensity prediction is far more challenging than the corresponding TC track prediction is. One of the reasons is the lack of understanding of the coupling relationships of the physical processes controlling TC intensification. Data mining technique provides potential tools for relationship exploration from ever-increasing amount of hurricane data. The data mining tools or algorithms require us to extract process-related features first from raw observational data such as the TC best track data, TRMM data for 3D rainfall and 2D surface information. After fed by these features, the data mining tools or algorithms can search for hidden relationships among the features by means of visualization or logical inferences.

In this article, we will describe the data, the definition of features from the data, tools for data exploration, and potential primary results.

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