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Inference of Cloud Optical Depth from Aircraft-Based Solar Radiometric...

The core information for this publication's citation.: 
Barker, H. W., A. Marshak, W. Szyrmer, A. Trishchenko, J.-P. Blanchet, and Z. Li (2002), Inference of Cloud Optical Depth from Aircraft-Based Solar Radiometric Measurements, J. Atmos. Sci., 59, 2093-2111.
Abstract: 

A method is introduced for inferring cloud optical depth ␶ from solar radiometric measurements made on an aircraft at altitude z. It is assessed using simulated radiometric measurements produced by a 3D Monte Carlo algorithm acting on fields of broken boundary layer clouds generated from Landsat imagery and a cloud-resolving model. The method uses upwelling flux and downwelling zenith radiance measured at two solar wavelengths where atmospheric optical properties above z are very similar but optical properties of the surface–atmosphere system below z differ. This enables estimation of cloud reflectance into nadir for upwelling diffuse flux and, finally, ␶ above z. An approximate one-dimensional radiative Green’s function is used to roughly account for horizontal transport of photons in all, even broken, clouds. This method is compared to its surface-based counterpart and shown to be superior. Most notably, the aircraft-based approach deals easily with inhomogeneous land surfaces, is less susceptible to poor sampling, and need not account for aerosol below z.

The algorithm appears as though it will have little difficulty inferring high-resolution time series of ␶ Շ 40 for most (single layer) clouds. For larger values of ␶, biases emerge; particularly, underestimation for the statistically infrequent interiors of cumuliform clouds as photon leakage through cloud sides is not addressed. For the cumuliform and stratiform clouds used here, mean bias errors for retrieved ␶ are ϳ1 (or ϳ15%) and ϳ0.3 (or ϳ3%), respectively. For stratiform clouds with textured bases, performance is likely to improve slightly for flights just up from mean cloud base.

Research Program: 
Radiation Science Program (RSP)