<p>Deep convective clouds, such as Towering Cumulus (TCu) and Cumulonimbus (Cb) clouds, may pose a serious risk to aviation. For a responsible replacement of human observers at airport weather stations in the Netherlands by automated observation systems of present weather, a Probability Of Detection (POD) of at least 80 % and a False Alarm Rate (FAR) of no more than 20 % is required. Therefore, the POD (58 %) and FAR (70 %) of the present KNMI automated radar-based Cb/TCu cloud detection method are not considered satisfactory. In this study, satellite derived cloud physical properties and High Resolution re&amp;#64258;ectances in the Visible (HRV) as well as weather radar data are used to develop a new Cb/TCu cloud detection method. The cloud physical properties are derived from the visible and near-infrared channels of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard a Meteosat Second Generation (MSG) satellite. The detection method is constructed for a MSG box area around Schiphol airport for the daytime summer period. METeorological Aerodrome Reports (METAR) of Cb and TCu clouds are used as ’ground<br /> truth’. The Cb/TCu cloud detection method is performed in two steps. First, a Convective Cloud Mask (CCM) is constructed to produce a hazard map. This map includes pixels which represent potential convective cloud pixels based on a thresholding technique. For the hazard map, the level of risk, which indicates the probability of presence of Cb/TCu clouds at and in the vicinity of the<br /> airport, is determined using a logistic regression model. Predictors for the model have been derived from the cloud physical properties, HRV re&amp;#64258;ectance and weather radar data. The frequent selection of the HRV derived predictors in the forward stepwise selection method revealed the importance of high resolution satellite data. Therefore, a cloud optical thickness has been derived from the<br /> HRV re&amp;#64258;ectance. The CCM shows a two-third decrease for the non-events (no Cb/TCu clouds), while over 95 % of the yes-events (Cb/TCu clouds) remain.<br /> The predicted probabilities from the logistic regression model show good Reliability and Resolution and positive skill over sample climatology. Using the Critical Success Index (CSI) and the Bias, a probability threshold is determined<br /> to convert predicted probabilities into predicted group memberships. Combining the results from the CCM with the results from the &amp;#64257;nal logistic regression model, a POD of 65.2 % and a FAR of 35.4 % are obtained for a maximum CSI and a bias of no more than 5 %. These veri&amp;#64257;cation scores show a substantial improvement with respect to the scores from the present automated Cb/TCu cloud detection method.</p>