Feature Extraction of Rain Cell Using Weather Radar Imagery in Tropical Regions

In-depth metadata about the spatial characteristics of rain fields is now available from weather radar data that was previously unavailable from traditional rain gauge networks. This knowledge is crucial for improving our comprehension of hydrology and rainfall patterns systems. This study focuses on the geometric characteristics of rain cells that formed in Peninsular Malaysia’s tropical climate. The center location, area, maximum rainfall intensity, mean gridded intensity, major radius length, minor radius length, and orientation of the rain cell were all extracted. This approach for pixel-based depth characterization of radar reflectivity images, particularly under unique circumstances brought on by the influence of the tropical monsoon region, will add novelty to this research. The features that are extracted, provide information that can be incorporated into a classifier model for rainfall-based machine learning in a future study. With the help of this model, upcoming convective and stratiform rainfall types can be predicted, as these features provide the input data needed to construct the Artificial Neural Network (ANN) model for the subsequent study.