Abstract. A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates) and extension of the temporal coverage.

Abstract. An 8-year record of satellite-based cloud properties named CLAAS (CLoud property dAtAset using SE-VIRI) is presented, which was derived within the EUMET-SAT Satellite Application Facility on Climate Monitoring. The data set is based on SEVIRI measurements of the Meteosat Second Generation satellites, of which the visible and near-infrared channels were intercalibrated with MODIS. Applying two state-of-the-art retrieval schemes ensures high accuracy in cloud detection, cloud vertical placement and microphysical cloud properties. These properties were further processed to provide daily to monthly averaged quantities, mean diurnal cycles and monthly histograms. In particular, the per-month histogram information enhances the insight in spatio-temporal variability of clouds and their properties. Due to the underlying intercalibrated measurement record, the stability of the derived cloud properties is ensured, which is exemplarily demonstrated for three selected cloud variables for the entire SEVIRI disc and a European subregion. All data products and processing levels are introduced and validation results indicated. The sampling uncertainty of the averaged products in CLAAS is minimized due to the high temporal resolution of SEVIRI. This is emphasized by studying the impact of reduced temporal sampling rates taken at typical overpass times of polar-orbiting instruments. In particular, cloud optical thickness and cloud water path are very sensitive to the sampling rate, which in our study amounted to systematic deviations of over 10 % if only sampled once a day. The CLAAS data set facilitates many cloud related applications at small spatial scales of a few kilometres and short temporal scales of a few hours. Beyond this, the spatiotemporal characteristics of clouds on diurnal to seasonal, but also on multi-annual scales, can be studied.

The Global Energy and Water Cycle Exchanges project (GEWEX) water vapor assessment’s (G-VAP) main objective is to analyze and explain strengths and weaknesses of satellite-based data records of water vapor through intercomparisons and comparisons with ground-based data. G-VAP results from the intercomparison of six total column water vapor (TCWV) data records are presented. Prior to the intercomparison, the data records were regridded to a common regular grid of 2° × 2° longitude–latitude. All data records cover a common period from 1988 to 2008. The intercomparison is complemented by an analysis of trend estimates, which was applied as a tool to identify issues in the data records. It was observed that the trends over global ice-free oceans are generally different among the different data records. Most of these differences are statistically significant. Distinct spatial features are evident in maps of differences in trend estimates, which largely coincide with maxima in standard deviations from the ensemble mean. The penalized maximal F test has been applied to global ice-free ocean and selected land regional anomaly time series, revealing differences in trends to be largely caused by breakpoints in the different data records. The time, magnitude, and number of breakpoints typically differ from region to region and between data records. These breakpoints often coincide with changes in observing systems used for the different data records. The TCWV data records have also been compared with data from a radiosonde archive. For example, at Lindenberg, Germany, and at Yichang, China, such breakpoints are not observed, providing further evidence for the regional imprint of changes in the observing system.

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 ...

A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28-yr period from 1982 until 2009 and consists of cloud, surface albedo and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. <br><br> The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid- to high-latitudes and over nearly all oceanic areas. <br><br> Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings wrt. to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2017 will allow a proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates) and extension of the temporal coverage