Assessing a Cloud Optical Depth Retrieval Algorithm with Model-Generated Data and the Frozen Turbulence Assumption

Abstract A cloud optical depth retrieval algorithm that utilizes time series of solar irradiance and zenith downwelling radiance data collected at a fixed surface site is assessed using model-generated cloud fields and simulated radiation measurements. To date, the retrieval algorithm has only been assessed using instantaneous cloud fields in which time series were mimicked via the frozen turbulence assumption. In this study, time series of radiation data are generated for use by the algorithm from a series of snapshots of an evolving and advecting cloud field, with values of optical depth retrieved for clouds occurring at the midpoint of the time series. This approach resembles conditions encountered in the field much better than those arising from the convenient frozen turbulence assumption. Values of optical depth are also retrieved for the same cloud field by employing the frozen turbulence approach. For the field of broken, shallow cumulus considered here, differences between the two sets of retrievals are small. This suggests that the encouraging results obtained thus far for this retrieval algorithm have not been secured falsely by the frozen turbulence assumption.

Assessing a Cloud Optical Depth Retrieval Algorithm with Model-Generated Data and the Frozen Turbulence Assumption Journal Articles