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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 6 | Copyright
Atmos. Meas. Tech., 11, 3373-3396, 2018
https://doi.org/10.5194/amt-11-3373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Jun 2018

Research article | 13 Jun 2018

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors

Oliver Sus1, Martin Stengel1, Stefan Stapelberg1, Gregory McGarragh2, Caroline Poulsen3, Adam C. Povey4, Cornelia Schlundt1, Gareth Thomas3, Matthew Christensen2,3, Simon Proud2, Matthias Jerg1, Roy Grainger4, and Rainer Hollmann1 Oliver Sus et al.
  • 1DWD – Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach, Germany
  • 2Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
  • 3RAL Space – Rutherford Appleton Laboratory, Chilton, Didcot OX11 0QX, UK
  • 4National Centre for Earth Observation, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK

Abstract. We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02°.

By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa.

The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

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This paper presents a new cloud detection and classification framework, CC4CL. It applies a sophisticated optimal estimation method to derive cloud variables from satellite data of various polar-orbiting platforms and sensors (AVHRR, MODIS, AATSR). CC4CL provides explicit uncertainty quantification and long-term consistency for decadal timeseries at various spatial resolutions. We analysed 5 case studies to show that cloud height estimates are very realistic unless optically thin clouds overlap.
This paper presents a new cloud detection and classification framework, CC4CL. It applies a...
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