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This study examines if SEVIRI retrieved cloud properties have sufficient accuracy for climate research and process studies over Europe. We analyse a one-year dataset of COT, CLWP and droplet effective radius retrievals at a 15 minutes sampling frequency. The SEVIRI cloud property retrievals are compared to microwave radiometer observed LWP and pyranometer derived COT for the three CloudNET stations. The CloudNET research project, supported by the European Commission, provides quasi continuously measurements of ground-based cloud properties for the development and implementation of cloud remote sensing algorithms. The accuracy of the ground-based measurements is superior to current satellite remote sensing techniques, which makes these observations the appropriate data source for assessing the accuracy of SEVIRI retrievals. The accuracy of the cloud property retrievals is evaluated for daily and annual variations in illumination geometry. Radiative transfer simulations are used to quantify and understand these differences. Uncertainties caused by the different footprints of the ground based and satellite retrieved cloud properties are investigated. Based on a large set of MODIS cloud liquid water path fields the magnitude of these uncertainties is quantified and an optimal sampling strategy is proposed.
The results from both the validation with CloudNET observations and the sensitivity study indicate that cloud property retrievals over northern Europe at low solar zenith angles from METEOSAT-8/SEVIRI have a higher sensitivity to errors than from NOAA/AVHRR. This indicates that at high latitudes present geostationary satellites, such as METEOSAT-8, are less suited than polar satellites for the analysis of seasonal variations in cloud properties. It will be shown that a portion of the observed differences between ground and space based observations is explained by the different footprints of the ground and space based instruments. These biases can be reduced by selecting the most representative pixels for validation, taking into account wind speed and direction and the altitude of the clouds. A method for optimizing the collocation of ground and satellite observations is proposed.
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