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 ...

The use of meteorological satellites for rainfall estimation and monitoring was introduced as a way of augmenting conventional ground-based rainfall data for hydrological models and weather forecasting. Today the primary scope of satellite rainfall monitoring is to provide information on rainfall occurrence, amount and distribution over the globe for a number of applications such as meteorology at all scales, climatology, hydrology and environmental sciences. The uneven distribution of raingauges and weather radars and the lack of rainfall data over the oceans have always been a concern and until now the rainfall, as a prominent branch of the global hydrological cycle, is not well understood. In this sense the problem is not different from the determination of wind, pressure, temperature and humidity fields although precipitation is by far the most variable in space and time. Furthermore, unlike many other atmospheric phenomena, precipitation (or the lack of it) has a direct impact on human life (e.g. flash floods, Barrett & Michell, 1991). Therefore, satellite monitoring is used to address the key questions of spatial and temporal coverage, which cannot be achieved by other observing systems.Among the challenges that face the science and technology of satellite remote sensing the quantitative determination of rainfall from the variety of precipitating systems is one of the most difficult and is largely unsolved. Barrett & Martin (1981) and Kidder & Vonder Haar (1995) give excellent reviews of the methods available. Petty (1995) has examined the status of satellite rainfall estimation over land. A recent review by Levizzani (1998a) has covered results and future perspectives from the geostationary orbit. The perspecMeteorol. Appl. 8, 23-41 (2001) Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

Results of the split-window cloud retrieval method and the new Meteosat Second Generation cloud analysis method (MSG/CLA), have been compared for MODIS data over the west Atlantic Ocean. Very good agreement is obtained for the classification of optically thick ice and water clouds. Differences are found for thin cirrus, thin water clouds and at cloud edges. These differences are explained by the fact that MSG/CLA also uses spectral channels of 3.9, 6.2, and 8.7 mm in addition to the split-window, which provides information over and above the split-window observations. Some of the disagreement at cloud edges is interpreted as inter-channel miss-alignment. The analysis in this study also confirms that an optically thin water cloud can be correctly classified by the MSG/CLA method.

The objective of this study is to improve the characterization of satellite-derived atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction. AMVs tend to exhibit considerable systematic and random errors that arise in the derivation or the interpretation of AMVs as single-level point observations of wind. One difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. This study uses instead a simulation framework: geostationary imagery for Meteosat-8 is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these images. The forecast model provides the ''truth'' with a sophisticated description of the atmosphere. The study considers infrared and water vapor AMVs from cloudy scenes. This is the first part of a two-part paper, and it introduces the framework and provides a first evaluation in terms of the brightness temperatures of the simulated images and the derived AMVs. The simulated AMVs show a considerable global bias in the height assignment (60-75 hPa) that is not observed in real AMVs. After removal of this bias, however, the statistics comparing the simulated AMVs with the true model wind show characteristics that are similar to statistics comparing real AMVs with short-range forecasts (speed bias and root-mean-square vector difference typically agree to within 1 m s 21 ). This result suggests that the error in the simulated AMVs is comparable to or larger than that in real AMVs. There is evidence for significant spatial, temporal, and vertical error correlations, with the scales for the spatial error correlations being consistent with estimates for real data.

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