Towards a Self-Configurable Weather Research and Forecasting System

Current weather forecast and visualization systems lack the scalability to support numerous customized requests for weather research and forecasting, especially at the time of natural disasters such as a hurricane landfall. Most of these systems provide somewhat generic forecasts for different types of users including meteorologists, business owners and emergency management officials. Such forecast while may be relevant to some specific group of users; to others it may not provide any useful information apart from the prediction of impending weather hazards. In other words, one size does not fit all. Weather data and its visualization indicating inclement weather conditions such as snow or ice storm, tornadoes and hurricanes need to be customized for the different type of users using such systems; thus, assisting them in ensuring effective preparatory and meticulous recovery plans. In this paper, we propose a self-configurable, user specific on-demand weather research and forecasting system that utilizes grid computing to facilitate scalable weather forecast data analysis and prediction.