Abstract. The uncertainties in the cloud physical properties derived from satellite observations make it difficult to interpret model evaluation studi…Abstract. The uncertainties in the cloud physical properties derived from satellite observations make it difficult to interpret model evaluation studies. In this paper, the uncertainties in the cloud water path (CWP) retrievals derived with the cloud physical properties retrieval algorithm (CPP) of the climate monitoring satellite application facility (CM SAF) are investigated. To this end, a numerical simulator of MSG-SEVIRI observations has been developed that calculates the reflectances at 0.64 and 1.63 μm for a wide range of cloud parameter values, satellite viewing geometries and surface albedos using a plane-parallel radiative transfer model. The reflectances thus obtained are used as input to CPP, and the retrieved values of CWP are compared to the original input of the simulator. Cloud parameters considered in this paper refer to e.g. sub-pixel broken clouds and the simultaneous occurrence of ice and liquid water clouds within one pixel. These configurations are not represented in the CPP algorithm and as such the associated retrieval uncertainties are potentially substantial. It is shown that the CWP retrievals are very sensitive to the assumptions made in the CPP code. The CWP retrieval errors are generally small for unbroken single-layer clouds with COT > 10, with retrieval errors of ~3% for liquid water clouds to ~10% for ice clouds. In a multi-layer cloud, when both liquid water and ice clouds are present in a pixel, the CWP retrieval errors increase dramatically; depending on the cloud, this can lead to uncertainties of 40–80%. CWP retrievals also become more uncertain when the cloud does not cover the entire pixel, leading to errors of ~50% for cloud fractions of 0.75 and even larger errors for smaller cloud fractions. Thus, the satellite retrieval of cloud physical properties of broken clouds as well as multi-layer clouds is complicated by inherent difficulties, and the proper interpretation of such retrievals requires extra care.more
Abstract. Validating the accuracy and long-term stability of terrestrial satellite data products necessitates a network of reference sites. This paper…Abstract. Validating the accuracy and long-term stability of terrestrial satellite data products necessitates a network of reference sites. This paper documents a global database of more than 2000 sites globally which have been characterized in terms of their spatial heterogeneity. The work was motivated by the need for potential validation sites for geostationary surface albedo data products, but the resulting database is useful also for other applications. The database (SAVS 1.0) is publicly available through the EUMETSAT website (http://savs.eumetsat.int/, doi:10.15770/EUM_SEC_CLM_1001). Sites can be filtered according to different criteria, providing a flexible way to identify potential validation sites for further studies and a traceable approach to characterize the heterogeneity of these reference sites. The present paper describes the detailed information on the generation of the SAVS 1.0 database and its characteristics.more
This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track…This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks—NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF).more
Averaging a set of individual measurements can reduce the stochastic error but can introduce a sampling error particularly for irregularly sampled dat…Averaging a set of individual measurements can reduce the stochastic error but can introduce a sampling error particularly for irregularly sampled data. We present a general method to estimate the total error of an averaged quantity as a combination of the measurement error and the sampling error without knowledge about the true average value of the distribution. Our approach requires covariance matrices connecting the retrieved measurement values to an independent reference data set. These covariance matrices can be obtained from a representative validation data set. We confirm the validity of the method by estimating the temporal sampling error of monthly mean cloud fractional cover (CFC) data derived from the Spinning-Enhanced Visible and Infrared Imager radiometer onboard the METEOSAT Second Generation (MSG) spacecraft, operated by the European Organization for the Exploitation of Meteorological Satellites. The estimated sampling errors are then compared with the true sampling errors calculated from an hourly sampled complete data set. For this purpose, we use ten sampling scenarios. Some of them address typical sampling problems like systematic over- and undersampling as well as hourly, daily, and random data gaps. Two additional sampling scenarios are directly related to the satellite application facility on climate monitoring monthly mean CFC data record. These are used to estimate the worst case sampling errors of this data record. The estimated total and sampling errors agree well with corresponding calculated values. We derive the needed covariance matrices by analyzing synoptic observations of the cloud fraction which are MSG diskwide available, the majority of them over European land surfaces. The method is not limited to temporal averaging cloud fraction data. Moreover, it is a general method that is also applicable to temporal and spatial averaging of other parameters as long as appropriate covariance matrices are available.more