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Hironobu Iwabuchi and Tadahiro Hayasaka

Abstract

Cloud remote sensing techniques are conventionally based on the independent pixel approximation (IPA). Here, three-dimensional (3D) radiative effects on IPA-based retrieved optical thickness from a visible-wavelength moderate-resolution (about 1 km) sensor are investigated. A Monte Carlo 3D radiative transfer model and a lognormal spectral cloud model are used to simulate monochromatic radiance reflected from overcast boundary layer cloud. A characterization of statistical properties of the optical thickness by the mean (M) and variance (S 2) of the logarithm of the optical thickness is proposed, where S represents a degree of cloud inhomogeneity. Biases in retrieved values of the two parameters with the IPA are defined as ΔM and ΔS 2 and attributed to neglect of net horizontal radiative transport in the IPA. Sensitivities of ΔM and ΔS 2 are tested with respect to geometrical roughness, M, S, mean geometrical thickness, spectral exponent of optical thickness fluctuation, ground surface reflectance, and bidirectional angles. The 3D radiative effects are sensitive to the geometrical roughness of cloud top rather than internal inhomogeneity of the extinction coefficient. The bias ΔM is negative in forward scattering viewing geometry due to cloud-side shadowing, while positive in backscattering viewing geometry due to side illumination. It is found that ΔM is proportional to S 2 and large for a dense cloud. On the other hand, ΔS 2 largely depends on the solar zenith angle; smoothing is exhibited for high solar elevation and roughening for low solar elevation. The smoothing and roughening phenomena are found to be almost independent of the inhomogeneity parameter. An optically thick cloud exhibits more roughening, while for a geometrically thick cloud both smoothing and roughening are enhanced. It is suggested that for the bias removal some empirical assumptions are required in geometrical and microphysical properties of cloud, which should be studied with in situ observation data.

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