Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data

The core information for this publication's citation.: 
Guillaume, A., B. Kahn, Q. Yue, E. J. Fetzer, S. Wong, G. J. Manipon, H. Hua, and B. D. Wilson (2018), Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data, J. Atmos. Sci., 75, 2187-2197, doi:10.1175/JAS-D-17-0111.1.
Abstract: 

A method is described to characterize the scale dependence of cloud chord length using cloud-type classification reported with the 94-GHz CloudSat radar. The cloud length along the CloudSat track is quantified using horizontal and vertical structures of cloud classification separately for each cloud type and for all clouds independent of cloud type. While the individual cloud types do not follow a clear power-law behavior as a function of horizontal or vertical scale, a robust power-law scaling of cloud chord length is observed when cloud type is not considered. The exponent of horizontal length is approximated by b ’ 1.66 6 0.00 across two orders of magnitude (;10–1000 km). The exponent of vertical thickness is approximated by b ’ 2.23 6 0.03 in excess of one order of magnitude (;1–14 km). These exponents are in agreement with previous studies using numerical models, satellites, dropsondes, and in situ aircraft observations. These differences in horizontal and vertical cloud scaling are consistent with scaling of temperature and horizontal wind in the horizontal dimension and with scaling of buoyancy flux in the vertical dimension. The observed scale dependence should serve as a guide to test and evaluate scale-cognizant climate and weather numerical prediction models.

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