KEYWORDS: Satellites, Clouds, Solar radiation, Error analysis, Solar radiation models, Fractal analysis, Linear filtering, Radiometry, Statistical analysis, Data modeling
There are large errors in satellite estimation of downward surface solar radiation (DSSR) at hourly time scales. This is due to several factors including mismatch in the spatial scale of the satellite vs. point measurements from surface pyranometers; and most importantly, structural variability in cloud properties. The authors examined the temporal and spatial variability of UV-B erythemal irradiance under cloudy stratocumulus conditions in Hobart Australia. Three radiometers were deployed at distances under 5 km. Short-term statistics were analysed and related to estimates from a three-dimensional radiation/cloud model with fractal properties in the horizontal. Results indicate that accuracy in satellite-derived hourly solar radiation may be improved with several satellite scans per hour, ideally every 10 minutes. However ground validation is a problem because an hourly measurement of irradiance in cloudy conditions is not likely to represent well the regional average as estimated from satellite.
Previous studies have shown that the albedo of clouds with inhomogeneous liquid water fields is lower than that of homogeneous clouds with the same average liquid water content. This can lead to biases in the retrieval of cloud properties from satellite images with pixel sizes significantly greater than the photon mean free path length. In this work we present a three-dimensional multifractal cloud model for use in radiative transfer calculations. The model is based on aircraft measurements of liquid water
content taken during 98 flights over Tasmania, Australia. Monte Carlo
radiative transfer is used to calculate the optical properties of clouds that were constructed according to this model. The reflectance of the cloud not only varies with the fractal parameters and mean liquid water content, but also with the area size over which it is averaged, i.e. the pixel size used. An "effective optical depth" is defined as the optical depth of a homogeneous cloud with the same reflectance as the 3D-multifractal cloud, and is parameterized as a function of the mean optical depth and the pixel size. This parameterization allows for fast radiation calculations in the remote sensing of cloud properties, by the replacement of an inhomogeneous cloud with a plane-parallel homogeneous one.
In low spatial resolution remote sensing the plane parallel albedo bias caused by sub-pixel cloud inhomogeneities leads to underestimation of cloud properties. RF Cahalan et al. Have suggested the effective thickness approximation as a method of correcting this bias, assuming a single parameter fractal cloud model.. The magnitude of the reduction factor applied to the optical depth in this method is dependent on the cloud fractal parameter, determined from spatial liquid water distribution. We present here a study using in situ aircraft liquid water measurements in northern Tasmania, Australia, to first locally determine the cloud fractal parameter in local conditions, and then to test the satellite retrieval of cloud properties using these results. Four categories of cloud with different fractal parameters are identified and the retrieval method showed encouraging results, with further testing and refinement required.
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