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In this study, a substantially improved version of the Meteosat cirrus detection algorithm (MeCiDA2) will be presented, which now allows application to the full earth disc visible by the Meteosat satellite. As cirrus clouds have an influence on the radiation budget of the earth, their optical properties and their global coverage has to be monitored at the global scale using instruments aboard geostationary satellites. Since MeCiDA was optimised for the area of Europe only, various changes were necessary to handle the variable conditions found over the full Meteosat disc. Required changes include the consideration of the viewing angle dependency and of the sensitivity of the 9.7 μm channel to the ozone column. To this end, a correction is implemented that minimises the influence of the variability of the stratospheric ozone. The evaluation of the proposed improvements is carried out by using MeCiDA applied to MODIS (moderate resolution imaging spectrometer) data to address viewing angledependent cirrus detection, and by additionally comparing it to the cloud optical properties MOD06 cirrus product. The new MeCiDA version detects less cirrus than the original one for latitudes larger than 40°, but almost the same amount elsewhere. MeCiDA's version for MODIS is more sensitive than that for SEVIRI (spinning enhanced visible and infrared imager) with cirrus occurrences higher by 10 %, and the new MeCiDA provides almost the same cirrus coverage (±0.1) as given by the cloud phase optical properties from MODIS for latitudes smaller than 50°. Finally, the influence of sub-pixel clouds on the SEVIRI cirrus detection has been examined: more than 60% of the undetected SEVIRI cirrus pixels have a cirrus coverage smaller than 0.5. [ABSTRACT FROM AUTHOR]

The article presents a study which provides ground-based observations of contrails and cirrus clouds caused by air traffic. The study visually analysed a six-month series of high-level clouds and contrails occurrence using an all-sky camera. It observed a contrail occurrence of 21% and a cirrus occurrence of 27% at daytime to 48% at nighttime. It also compared images gathered with data from advanced very high resolution radiometers (AVHRR) and SEVIRI satellites.

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 than 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 SEVIRI 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 the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed. [ABSTRACT FROM AUTHOR]