The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach

Journal article

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 %.

Authors: McGarragh, G; Poulsen, C; Thomas, G; Povey, A; Sus, O

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Copernicus Publications
• Journal: Atmospheric Measurement Techniques
• Volume: 11
• Issue: 6
• Pages: 3397-3431
• Publication date: 2018-06-13
• Acceptance date: 2018-04-17
• DOI: 10.5194/amt-11-3397-2018
• EISSN: 1867-1381