Abstract
This paper presents an approach using the GEneralized Nonlinear Retrieval Analysis (GENRA) tool and general inverse theory diagnostics including the maximum likelihood solution and the Shannon information content to investigate the performance of a new spectral technique for the retrieval of cloud optical properties from surface based transmittance measurements. The cumulative retrieval information over broad ranges in cloud optical thickness (τ), droplet effective radius (re), and overhead sun angles is quantified under two conditions known to impact transmitted radiation; the variability in land surface albedo and atmospheric water vapor content. Our conclusions are: (1) the retrieved cloud properties are more sensitive to the natural variability in land surface albedo than to water vapor content; (2) the new spectral technique is more accurate (but still imprecise) than a standard approach, in particular for τ between 5 and 60 and re less than approximately 20 μm; and (3) the retrieved cloud properties are dependent on sun angle for clouds of from 5 to 10 and re < 10 μm, with maximum sensitivity obtained for an overhead sun.
Highlights
The radiative energy incident at the Earth’s surface comes from two sources, the Sun and the atmosphere
We investigate positive and negative 3% systematic offsets in the measurement for both retrieval wavelengths in the standard method; the 1,600 nm retrieval wavelength in the slope method is assumed to be unaffected by systematic error because it is normalized by transmittance at 1,565 nm (Section 2)
We quantify the variance in transmittance resulting from ±30% variability in atmospheric water vapor content and spectral surface albedo spanning soil to vegetated surface types for a broad range of cloud optical thickness and droplet effective radius
Summary
The radiative energy incident at the Earth’s surface comes from two sources, the Sun and the atmosphere. They investigated the impacts of measurement uncertainty on the retrieved cloud properties from the new spectral slope algorithm compared to the standard technique applied to cloud reflectance. They showed that the spectral slope method had smaller uncertainties over a broader range in cloud optical thickness values. In our results (Section 5), we demonstrate the expected cloud retrieval performance to these measurement and modeling errors (Sections 5.1–5.2), compute retrieval biases and Shannon information content over a broad range in cloud optical thickness and droplet effective radius (Section 5.2.1), investigate non-Gaussian behavior in the retrieved distributions (Section 5.2.2), and investigate the impacts of sun angle on the error characteristics and retrieved cloud products (Section 5.2.3).
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