Using naive Bayes classifier for classification of convective rainfall intensities based on spectral characteristics retrieved from SEVIRI

Abstract

This paper presents a new algorithm to classify convective clouds and determine their intensity, based on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The convective rainfall events at 15 min, 4 × 5 km spatial resolution from 2006 to 2012 are analysed over northern Algeria. The convective rain classification methodology makes use of the relationship between cloud spectral characteristics and cloud physical properties such as cloud water path (CWP), cloud phase (CP) and cloud top height (CTH). For this classification, a statistical method based on ‘naive Bayes classifier’ is applied. This is a simple probabilistic classifier based on applying ‘Bayes’ theorem with strong (naive) independent assumptions. For a 9-month period, the ability of SEVIRI to classify the rainfall intensity in the convective clouds is evaluated using weather radar over the northern Algeria. The results indicate an encouraging performance of the new algorithm for intensity differentiation of convective clouds using SEVIRI data.