Alexis Berne

Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements

The validation of long term cloud datasets retrieved from satellites is challenging due to their worldwide coverage going back as far as the 1980s, among others. A trustworthy reference cannot be found easily at every location and every time. Mountainous regions represent especially a :::::: present :: a :::::::: particular problem since ground-based measurements are sparser ::::: sparse. Moreover, as retrievals from passive satellite radiometers are difficult in winter due to the presence of snow on the ground, it is particularly important to develop new ways to evaluate and to correct satellite datasets over elevated areas. 5 In winter for ground levels above 1000m (a.s.l.) in Switzerland, the cloud occurrence of the newly-released cloud property datasets of the ESA Climate Change Initiative Cloud_cci project (AVHRR-PM and MODIS-Aqua series) is 132 % to 217 % that of SYNOP observations, corresponding to between 24 % and 54 % of false cloud detections. Furthermore, the overestimations increase with the altitude of the sites and are associated with particular retrieved cloud properties. In this study, a novel post-processing approach is proposed to reduce the amount of false cloud detections in the satellite 10 datasets. A combination of ground-based downwelling longwave and shortwave radiation and temperature measurements is used to obtain a mask for :::::: provide ::::::::::: independent ::::::::: validation :: of : the cloud cover above :::: over 41 locations in Switzerland. An agreement of 85 % is obtained when the cloud cover is compared to surface synoptic observations (90 % within ± 1 okta difference). The obtained cloud mask has been :::::::: validation :::: data :: is :::: then co-located with the satellite observations and a decision tree ::::: model is trained to automatically detect the overestimations in the satellite ’s cloud masks. Cross-validated results show 15 that 62± 13 % of these overestimations can be identified by the model, reducing the systematic error in the satellite datasets :::: from :::::::::: 14.4± 15.5 :: % : to 4.3± 2.8 %, : . ::: The ::::::: amount :: of ::::: errors :: is :::::: lower, ::: and ::::::::::: importantly, :::: their ::::::::: distribution :: is ::::: more :::::::::::: homogeneous :: as :::: well. :::::: These ::::::::: corrections ::::::: happen : at the cost of an : a ::::: global : increase of 7± 2 % of missed clouds. Using this model, it is hence possible to significantly improve the cloud detection reliability in elevated areas in the Cloud_cci’s AVHRR-PM and MODIS-Aqua products. 20