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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, 3397-3431, 2018
https://doi.org/10.5194/amt-11-3397-2018
© 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 2: The optimal estimation approach

Gregory R. McGarragh1, Caroline A. Poulsen2,3, Gareth E. Thomas2,3, Adam C. Povey4, Oliver Sus5, Stefan Stapelberg5, Cornelia Schlundt5, Simon Proud1, Matthew W. Christensen1,2,3, Martin Stengel5, Rainer Hollmann5, and Roy G. Grainger4 Gregory R. McGarragh et al.
  • 1Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
  • 2RAL Space, STFC, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UK
  • 3NCEO, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UK
  • 4National Centre for Earth Observation, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
  • 5Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany

Abstract. The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20%.

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Satellites are vital for measuring cloud properties necessary for climate prediction studies. We present a method to retrieve cloud properties from satellite based radiometric measurements. The methodology employed is known as optimal estimation and belongs in the class of statistical inversion methods based on Bayes' theorem. We show, through theoretical retrieval simulations, that the solution is stable and accurate to within 10–20% depending on cloud thickness.
Satellites are vital for measuring cloud properties necessary for climate prediction studies. We...
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