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Abstract. The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) -a crucial parameter to estimate the thermal cloud radiative forcing -can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system.In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements Published by Copernicus Publications on behalf of the European Geosciences Union. U. Hamann et al.: Remote sensing of cloud top pressure/height from SEVIRIthan to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 km to 0.6 km. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SE-VIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate ...

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International audienceTo support the MEGHA-Tropiques space mission, cloud mask and cloud type classification are needed at high spatial and time resolutions over the tropical belt for water vapour and precipitation analysis. For this purpose, visible and infrared radiance data from geostationary satellites (GEO) are used with a single algorithm initially developed by SAFNWC (Satellite-Application-Facility-for-Nowcasting) for Meteosat-Second-Generation. This algorithm has been adapted by SAFNWC to the spectral characteristics and field of view of each satellite. Retrieved cloud cover characteristics (cloud mask, classification and top pressure) are evaluated over four months in summer of 2009 against CALIOP lidar observations from the CALIPSO polar-orbiting satellite. To better identify atmospheric and instrumental issues, separate analyses are performed over land and ocean, for 0130 AM and 0130 PM CALIPSO overpasses and for each GEO. Both mean cloud cover occurrence and instantaneous cloud cover statistics are compared. We found that each classification has specific features, which depend on observed cloud regimes and instrument capabilities. Most important, a common behaviour of the GEOs against CALIOP depending on cloud types is observed. We found that GEO cloud occurrence is lower by about 10% than CALIOP, with the largest biases over land during daytime and the smallest over ocean during daytime. Further detailed analysis reveals specific discrepancies in the retrieved cloud types. As expected, high-level clouds are detected more frequently by the lidar. We show that, over ocean when the optical thickness of detected high-level clouds is limited to greater than 0.1 in the comparisons, multi-spectral radiometry performs very similarly. However, the most significant difference is attributed to non-detection of low-level clouds that are often broken, which causes a reduction of up to 20% in low-level cloud fraction and even 30% in some regions. Other significant differences are seen over land, where mid-level clouds are not detected or are misclassified

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