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This paper describes anapproach for cloud parameter retrieval (radiometric cloud-fractionestimation) using the polarization measurements of the Global OzoneMonitoring Experiment-2 (GOME-2) onboard the MetOp-A/B satellites. The corecomponent of the Optical Cloud Recognition Algorithm (OCRA) is thecalculation of monthly cloud-free reflectances for a global grid (resolutionof 0.2° in longitude and 0.2° in latitude) to deriveradiometric cloud fractions. These cloud fractions will serve as a prioriinformation for the retrieval of cloud-top height (CTH), cloud-top pressure(CTP), cloud-top albedo (CTA) and cloud optical thickness (COT) with theRetrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. Thisapproach is already being implemented operationally for the GOME/ERS-2 andSCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRAalgorithm applied to the GOME-2 sensors.  Based on more than five years of GOME-2A data (April 2008 to June 2013),reflectances are calculated for  ≈  35 000 orbits. For eachmeasurement a degradation correction as well as a viewing-angle-dependent andlatitude-dependent correction is applied. In addition, an empiricalcorrection scheme is introduced in order to remove the effect of oceanic sunglint. A comparison of the GOME-2A/B OCRA cloud fractions with colocatedAVHRR (Advanced Very High Resolution Radiometer) geometricalcloud fractions shows a general good agreement with a mean difference of−0.15 ± 0.20.  From an operational point of view, an advantage of the OCRA algorithm is itsvery fast computational time and its straightforward transferability tosimilar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOsphericMonitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 andSentinel 5.  In conclusion, it is shown that a robust, accurate and fast radiometric cloud-fraction estimation for GOME-2 can be achieved with OCRA usingpolarization measurement devices (PMDs)

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B