  This work has been accepted by IEEE TGRS for publication. The majority of
optical observations acquired via spaceborne earth imagery are affected by
clouds. While there is numerous prior work on reconstructing cloud-covered
information, previous studies are oftentimes confined to narrowly-defined
regions of interest, raising the question of whether an approach can generalize
to a diverse set of observations acquired at variable cloud coverage or in
different regions and seasons. We target the challenge of generalization by
curating a large novel data set for training new cloud removal approaches and
evaluate on two recently proposed performance metrics of image quality and
diversity. Our data set is the first publically available to contain a global
sample of co-registered radar and optical observations, cloudy as well as
cloud-free. Based on the observation that cloud coverage varies widely between
clear skies and absolute coverage, we propose a novel model that can deal with
either extremes and evaluate its performance on our proposed data set. Finally,
we demonstrate the superiority of training models on real over synthetic data,
underlining the need for a carefully curated data set of real observations. To
facilitate future research, our data set is made available online
