Critical to the quality of satellite retrievals, aside from calibration issues of the instruments, are the identification of cloud-free scenes, the highly accurate representation (or elimination) of surface reflectance effects and the realism of a-priori assumptions.
The identification and removal of cloudy scenes is usually based on combinations of spectral thresholds - including visible reflection and infrared blackbody temperatures. Nonetheless, detecting and removing all scenes with clouds can be challenge. Especially difficult is the detection of subpixel size clouds, whose reflection could be attributed to aerosol by mistake. This problem grows with the area of the satellite pixel and with the lack of simultaneous and co-located cloud-detecting spectral data (e.g. near-IR data for cirrus detection or far-IR data for a radiative background threshold). Techniques that import cloud-screen data from other sensors can introduce significant errors, especially if both data-sets are not co-located in time and space. Another problem arises from too stringent rules in the cloud-screening algorithm and the sub-sequent removal of aerosol scenes. A typical example is the misinterpretation of large dust particles off the African west-coast, which is commonly identified as low clouds (and which eventually leads to underestimates in aerosol optical depths for that region).
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