Tracking of storm fronts in weather radar imagery

摘要

Tracking of storm fronts in weather imagery is important for several weather-related applications. Coastal-area weather radars provide coverage up to 200-250 miles into the ocean, and thus can help with tracking of storm-fronts to support forecasting in those areas. Another application where tracking of storm fronts can be of assistance is clutter/rain classification. Specifically, the path of a tracked event can be used to decide if the particular event corresponds to precipitation or clutter. For instance, clutter usually appears to be a relatively static event. Precipitation can be modeled as a mixture of localized functions, each changing in terms of shape, position, and intensity. Tracking of precipitation events can be performed via tracking of the localized function parameters. In this paper, the modeling of rain events using Radial Basis Function neural networks (RBFNN) is studied. In the recent past, such techniques have been used for forecasting. Although effective, these techniques have been found to be computationally expensive. In this work, we evaluate the feasibility of modeling rain events using RBFNN in an efficient manner, and we propose modifications to existing techniques to achieve this goal.