Data coming from meteorological radars is of the utmost importance for the diagnosis and monitoring of precipitation systems and their possible associated severe phenomena. The echoes caused by objectives that are not meteorological introduce errors in the information. Therefore, it is necessary to detect their presence before using this data. This paper presents four supervised classification techniques based on different models which seek to give an answer to this problem. In addition, as an important part of this work, resampling techniques were implemented on the training set in order to further asses the results. Resampling methods are an indispensable tool in modern statistics. Those techniques provide additional information about the model of interest by repeatedly drawing samples from then data.
Based on data from a C-band Dual-PolarizationDoppler weather radar located in Anguil and from a previous expert’s manual classification, four supervised classification methods with different degrees of flexibility in their structure were implemented: Lineal Model, Quadratic Model, Logistic Model and Bayes Naive Model. Finally, the results of each of them were assessed and compared. Although difficulties were encountered in classifying boundary zones between classes, the results obtained were adequate, showing the best performance in the least flexible model, the linear one. It is considered necessary to keep working in this line of research in order to include more cases in the analysis and allow a better inference on the results.