Disclaimer: This material is being kept online for historical purposes. Though accurate at the time of publication, it is no longer being updated. The page may contain broken links or outdated information, and parts may not function in current web browsers. Visit https://espo.nasa.gov for information about our current projects.


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

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.

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)