Radiative transfer through broken clouds: Observations and model validation

Abstract

Stochastic radiative transfer is investigated as a method of improving shortwave cloud-radiation parameterizations by incorporating the effects of statistically determined cloud-size and cloud-spacing distributions. Ground-based observations from 16 days at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) site are used to derive a statistical description of scattered clouds. The data are ingested into a stochastic, shortwave radiative transfer model. The typical cloud-base height of the most prevalent cloud type, fair-weather cumulus, is 1100 m. Low cloud-fraction conditions are common, with observed cloud liquid water paths between 20 and 80 g m-2. Cloud-fraction amounts calculated using ceilometer data compare reasonably well with those reported in weather logs. The frequency distribution of cloud size can be described by a decaying exponential: the number of clouds decreases significantly with increasing cloud size. The minimum detectable cloud size is 200 m and the largest observed cloud is approximately 4 km. Using both a stochastic model and a plane-parallel model, the predicted radiation fields are compared and evaluated against an independent observational dataset. The stochastic model is sensitive to input cloud fraction and cloud field geometry. This model performs poorly when clouds are present in adjacent model layers due to random overlapping of the clouds. Typically, the models agree within 30 W m-2 for downwelling shortwave radiation at the surface. Improvement in the observations used to calculate optical depth will be necessary to realize fully the potential of the stochastic technique.