The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

We present version 3 of the Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset, which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from AVHRR measurements recorded by the afternoon (post meridiem – PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling and shortwave and longwave broadband fluxes at the surface (bottom of atmosphere – BOA) and top of atmosphere (TOA). All fluxes were determined at the AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at the BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) shows a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at the BOA and TOA. The Cloud_cci AVHRR-PM version 3 (Cloud_cci AVHRR-PMv3) dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a digital object identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Data validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) with reference datasets derived from lidar, microwave and passive imager measurements. The evaluation results show very good overall agreement with matching spatial distributions and temporal variability and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial and temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged applications, such as process studies of the diurnal cycle of clouds and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.

The variability of convective cloud spans a wide range of temporal and spatial scales and is of fundamental importance for global weather and climate systems. Datasets from geostationary satellite instruments such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide high-time-resolution observations across a large area. In this study we use data from SEVIRI to quantify the diurnal cycle of cloud top temperature within the instrument's field of view and discuss these results in relation to retrieval biases. We evaluate SEVIRI cloud top temperatures from the new CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) dataset against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Results show a mean bias of +0.44 K with a standard deviation of 11.7 K, which is in agreement with previous validation studies. Analysis of the spatio-temporal distribution of these errors shows that absolute retrieval biases vary from less than 5 K over the southeast Atlantic Ocean up to 30 K over central Africa at night. Night- and daytime retrieval biases can also differ by up to 30 K in some areas, potentially contributing to biases in the estimated amplitude of the diurnal cycle. This illustrates the importance of considering spatial and diurnal variations in retrieval errors when using the CLAAS-2 dataset. Keeping these biases in mind, we quantify the seasonal, diurnal, and spatial variation of cloud top temperature across SEVIRI's field of view using the CLAAS-2 dataset. By comparing the mean diurnal cycle of cloud top temperature with the retrieval bias, we find that diurnal variations in the retrieval bias can be small but are often of the same order of magnitude as the amplitude of the observed diurnal cycle, indicating that in some regions the diurnal cycle apparent in the observations may be significantly impacted by diurnal variability in the accuracy of the retrieval. We show that the CLAAS-2 dataset can measure the diurnal cycle of cloud tops accurately in regions of stratiform cloud such as the southeast Atlantic Ocean and Europe, where cloud top temperature retrieval biases are small and exhibit limited spatial and temporal variability. Quantifying the diurnal cycle over the tropics and regions of desert is more difficult, as retrieval biases are larger and display significant diurnal variability. CLAAS-2 cloud top temperature data are found to be of limited skill in measuring the diurnal cycle accurately over desert regions. In tropical regions such as central Africa, the diurnal cycle can be described by the CLAAS-2 data to some extent, although retrieval biases appear to reduce the amplitude of the real diurnal cycle of cloud top temperatures. This is the first study to relate the diurnal variations in SEVIRI retrieval bias to observed diurnal cycles in cloud top temperature. Our results may be of interest to those in the observation and modelling communities when using cloud top properties data from SEVIRI, particularly for studies considering the diurnal cycle of convection.

The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation (COMET, https://doi.org/10.5676/EUM_SAF_CM/CFC_METEOSAT/V001) covering 1991–2015 has been recently released by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF). COMET is derived from the MVIRI and SEVIRI imagers aboard geostationary Meteosat satellites and features a Cloud Fractional Cover (CFC) climatology in high temporal (1 h) and spatial (0.05° × 0.05°) resolution. The CM SAF long-term cloud fraction climatology is a unique long-term dataset that resolves the diurnal cycle of cloudiness. The cloud detection algorithm optimally exploits the limited information from only two channels (broad band visible and thermal infrared) acquired by older geostationary sensors. The underlying algorithm employs a cyclic generation of clear sky background fields, uses continuous cloud scores and runs a naïve Bayesian cloud fraction estimation using concurrent information on cloud state and variability. The algorithm depends on well-characterized infrared radiances (IR) and visible reflectances (VIS) from the Meteosat Fundamental Climate Data Record (FCDR) provided by EUMETSAT. The evaluation of both Level-2 (instantaneous) and Level-3 (daily and monthly means) cloud fractional cover (CFC) has been performed using two reference datasets: ground-based cloud observations (SYNOP) and retrievals from an active satellite instrument (CALIPSO/CALIOP). Intercomparisons have employed concurrent state-of-the-art satellite-based datasets derived from geostationary and polar orbiting passive visible and infrared imaging sensors (MODIS, CLARA-A2, CLAAS-2, PATMOS-x and CC4CL-AVHRR). Averaged over all reference SYNOP sites on the monthly time scale, COMET CFC reveals (for 0–100% CFC) a mean bias of −0.14%, a root mean square error of 7.04% and a trend in bias of −0.94% per decade. The COMET shortcomings include larger negative bias during the Northern Hemispheric winter, lower precision for high sun zenith angles and high viewing angles, as well as an inhomogeneity around 1995/1996. Yet, we conclude that the COMET CFC corresponds well to the corresponding SYNOP measurements, and it is thus useful to extend in both space and time century-long ground-based climate observations.

A main field in biomedical optics research is diffuse optical tomography, where intensity variations of the transmitted light traversing through tissue are detected. Mathematical models and reconstruction algorithms based on finite element methods and Monte Carlo simulations describe the light transport inside the tissue and determine differences in absorption and scattering coefficients. Precise knowledge of the sample’s surface shape and orientation is required to provide boundary conditions for these techniques. We propose an integrated method based on structured light three-dimensional (3-D) scanning that provides detailed surface information of the object, which is usable for volume mesh creation and allows the normalization of the intensity dispersion between surface and camera. The experimental setup is complemented by polarization difference imaging to avoid overlaying byproducts caused by inter-reflections and multiple scattering in semitransparent tissue.