Inferring Optical Depth of Broken Clouds above Green Vegetation Using Surface...

The core information for this publication's citation.: 
Barker, H. W., and A. Marshak (2001), Inferring Optical Depth of Broken Clouds above Green Vegetation Using Surface Solar Radiometric Measurements, J. Atmos. Sci., 58, 2989-3006.
Abstract: 

A method for inferring cloud optical depth ␶ is introduced and assessed using simulated surface radiometric measurements produced by a Monte Carlo algorithm acting on fields of broken, single-layer, boundary layer clouds derived from Landsat imagery. The method utilizes a 1D radiative transfer model and time series of zenith radiances and irradiances measured at two wavelengths, ␭1 and ␭ 2 , from a single site with surface albedos ␣ ␭1 Ͻ ␣ ␭ 2. Assuming that clouds transport radiation in accordance with 1D theory and have spectrally invariant optical properties, inferred optical depths ␶Ј are obtained through cloud-base reflectances that are approximated by differencing spectral radiances and estimating upwelling fluxes at cloud base. When initialized with suitable values of ␣ ␭1, ␣ ␭ 2, and cloud-base altitude h, this method performs well at all solar zenith angles. Relative mean bias errors for ␶Ј are typically less than 5% for these cases. Relative variances for ␶Ј for given values of inherent ␶ are almost independent of inherent ␶ and are Ͻ50%. Errors due to neglect of net horizontal transport in clouds yield slight, but systematic, overestimates for ␶ Շ 5 and underestimates for larger ␶. Frequency distributions and power spectra for retrieved and inherent ␶ are often in excellent agreement. Estimates of ␶ depend weakly on errors in h, especially when h is overestimated. Also, they are almost insensitive to errors in surface albedo when ␣ ␭1 is underestimated and ␣ ␭ 2 overestimated. Reversing the sign of these errors leads to overestimation of ␶, particularly large ␶. In contrast, the conventional method of using only surface irradiance yields almost entirely invalid results when clouds are broken.

Though results are shown only for surfaces resembling green vegetation (i.e., ␣ ␭1 K ␣ ␭ 2), the performance of this method depends little on the values of ␣ ␭1, and ␣ ␭ 2. Thus, if radiometric data have sufficient signal-tonoise ratios and suitable wavelengths can be found, this method should yield reliable estimates of ␶ for broken clouds above many surface types.

Research Program: 
Radiation Science Program (RSP)