The present study aims to investigate the potential of the random forests ensemble
classification and regression technique to improve rainfall rate assignment during day, night …

Abstract Using Advanced Very High Resolution Radiometer data, 16 pixel× 16 pixel sample
areas are classified into one of ten output classes using a probabilistic neural network …

In this work, a new rainfall estimation technique based on the high spatial and temporal
resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the …

In this paper a cloud detection algorithm applied to the MSG-SEVIRI (Meteosat Second
Generation-Spinning Enhanced Visible and Infrared Imager) data is described. In order to …

This paper describes how the combination of a satellite-derived cloud classification with
surface observations can improve analysis of cloud-base height. A cloud-base retrieval that …

The detection of clouds and the analysis of cloud frequency play an important role for
operational weather prediction as well as for climate-ecological studies. A threshold …

The sum and difference histogram approach is applied to the assessment of the textural
features of Meteosat images and the resulting textural parameters are used to classify the …

Aerosol optical characteristics of Asian dust are studied by combining Global Ozone
Monitoring Experiment (GOME) data, with Geostationary Meteorological Satellite (GMS-5) …

For the estimation of rainfall in northern Algeria, a new method is proposed in this study. It
based on the k nearest weighted neighbours (Wk NN) classification algorithm using the …

In this case study we compare cloud fractional cover measured by radiometers on polar
satellites (AVHRR) and on one geostationary satellite (SEVIRI) to ground-based manual …