CM SAF's cLoud, Albedo and RAdiation dataset (CLARA-A1) based on 28 years of global satellite observations

The climatological characterization of geophysical variables in the Earth-Atmosphere system and their associated processes require both atmospheric models and observational datasets. The observations can for example serve as reference for model tuning, as contributor to the analysis of the initial atmospheric state, as boundary constraints during the modelled evolution of the atmosphere, and as constraints for developments of model physics. They can further be used to analyse the long-term state of clouds and their spatial and temporal variability. As one of its main objectives, the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) uses space-based observations from meteorological satellites to provide datasets of geophysical parameters suitable for climate analysis and monitoring. For this, recurring reprocessing efforts ensure enhancements in quality and stability of the datasets using latest retrieval developments and (inter-)calibration information. In recent years, CM SAF activities have among others been directed to a three-decades spanning datasets derived from passive imager measurements of AVHRR, a satellite sensor family.

This presentation will introduce CLARA-A1 (CLoud, Albedo and RAdiation dataset, AVHRR-based, version 1): a global dataset of cloud properties, surface albedo and surface radiation products generated by CM SAF. The dataset spans the time period 1982 through 2009 and allows therefore for various climatological applications. We will give an overview on the CLARA-A1 geophysical products, which cover daily and monthly averages as well as monthly histograms (cloud properties only) on spatial lat/lon resolutions down to 0.25°. The dataset has comprehensively been characterized and documented (user manuals, validation reports, etc.) all publically available under www.cmsaf.eu. Along with the dataset, a satellite simulator for CLARA-A1 cloud properties is currently being developed which is soon ready for implementation in global and regional climate models.