Surface Solar Radiation Flux and Cloud Radiative Forcing for the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP): A Satellite, Surface Observations, and Radiative Transfer Model Study

Abstract

This study presents surface solar radiation flux and cloud radiative forcing results obtained by using a combination of satellite and surface observations interpreted by means of a simple plane-parallel radiative transfer model called 2001. This model, a revised version of a model initially introduced by Gautier et al., relates calibrated radiance observations from space to incoming surface solar flux. After a description of the model, an evaluation is presented by comparison with a more complex model that the authors have developed, the Santa Barbara DISORT Atmospheric Radiative Transfer model (SBDART) based on the discrete ordinate model of Stamnes et al. This evaluation demonstrates this model's accuracy for instantaneous surface flux when used to retrieve daily (and monthly) surface solar flux. Limitations related to its lack of treatment of the bidirectional reflectance properties of clouds are also discussed and quantified by comparison with SBDART for instantaneous surface solar flux retrievals. The influence of satellite sensor calibration uncertainty is also examined in terms of surface solar flux. The model has been applied to hourly GOES data collected over the Atmospheric Radiation Measurement (ARM) program's central cloud and radiation testbed site in Oklahoma during a 14-month period to estimate hourly, daily, and monthly surface solar radiation flux. Comparisons of the model's results with surface measurements made from pyranometers located at the ARM site indicate good overall agreement. The best results are obtained for daily integrated clear skies with an rms error less than 10 W m−2 (or about 3% of the mean value) and a 2.8 W m−2 bias. These results indicate that the clear sky model is quite accurate and also that the threshold-based technique to detect cloudy conditions works well for the resolution of the satellite data used in this study. For partly cloudy conditions the comparisons show an rms error of about 20 W m−2 (or less than 7% of the mean) and a −2.5 W m−2 bias. The performance of the model degrades with cloud cover conditions with an rms error of 22 W m−2 (or 13% of the mean) and a bias of 13.9 W m−2 for overcast conditions. The results improve considerably for monthly average values with an rms error of about 11 W m−2 (or 4% of the mean) and a bias of 2.6 W m−2 for all conditions. The model has also been used to evaluate the cloud radiative forcing at the surface and results indicate large values of forcing for the spring and summer reaching daily values over 200 W m−2 in May.