Clustering technique to interpret Numerical Weather Prediction output products for forecast of Cloudburst

With the advent of digital computers and their continuous increasing processing power, the ‘Numerical Weather Prediction’ (NWP) models which solve a close set of equations of atmospheric model, have been adopted by most of the meteorological services to issue day to day weather forecasts. These forecasts are issued for public in general. But there are many limitations inherent to this technique viz. the actual weather event cannot be predicted directly by these models , so statistical regression techniques viz. Model Output Statistics (MOS) are used to derive the weather phenomenon from the NWP output products which itself requires long term consistent series of model forecasts. Due to the frequent revisions of the models, the long-term series of forecasts is not available. There is thus a strong need for searching alternative tools to MOS for interpretation of weather patterns provided by NWP models. Data mining is one such alternative that has been applied in this paper to interpret the forecast provided by European Center for Medium-range Weather Forecasting (ECMWF) model so as to infer the formation of cloudburst in advance. Keywords— Numerical Weather Prediction, Clustering, Cloudburst, Relative Humidity, Temperature, Convergence