Abstract
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 %.
Highlights
Remote sensing of clouds from satellites is vitally important for advancing our understanding of the Earth and its climate
The ORAC retrieval algorithm is based on the optimal estimation approach for atmospheric inverse problems described by Rodgers (2000), in which the input state to a forward model is optimized to obtain the best match between real measurements and simulated measurements computed with a forward model while being constrained by a priori knowledge of the state
This paper describes the optimal estimation component of the Community Cloud retrieval for Climate (CC4CL) based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm
Summary
Remote sensing of clouds from satellites is vitally important for advancing our understanding of the Earth and its climate. It is possible to use CO2-slicing results to estimate the cloud top pressure for the thermal methods discussed above to improve the uncertainty in the cloud radiating temperature and the retrieved optical thickness and effective radius (Cooper et al, 2003). Forward modeling using the solar retrieved optical thickness and effective radius for a cloud with its top placed at the thermally retrieved cloud top pressure may produce simulated radiances that are significantly different than the observed radiances This inconsistency could have significant impacts on broadband flux computations for radiation studies. Objective information content analyses for cloud retrievals from satellite imagers, using visible, near-infrared and thermal infrared channels simultaneously, have been presented (L’Ecuyer et al, 2006; Cooper et al, 2006), including a technique for determining the optimal set of channels using the optimal estimation framework. Preprocessing is responsible for cloud masking and classification and the retrieval methodology described in this paper will assume the properties of liquid water or ice cloud based on the cloud classification
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