Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery

• Main Author: Paduru, Anirudh
• Format: Others
• Published: ScholarWorks@UNO 2009
• Subjects: Radial Basis Function Neural Network; Forecasting; Nowcasting; Tracking; Rain Events; Non-Rain Events; Gaussian Functions; Mahalanobis Distance; Pyramidal Synthesis; Cascade Synthesis; Coarse Resolution; 3D Elevations
• Online Access: http://scholarworks.uno.edu/td/1097; http://scholarworks.uno.edu/cgi/viewcontent.cgi?article=2078&context=td

Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed.