Publication Date:
2019-02-18
Description:
The vertical structure of clouds unresolved in large-scale weather prediction and climate models is controlled by an overlap assumption. When a binary representation (cloud or no cloud) of subgrid horizontal variability is replaced by a probability density function (PDF) treatment of cloud-related variables, a cloud occurrence overlap needs to be replaced by a PDF overlap. The PDF overlap can be quantified by a correlation length scale, z 0 , indicating how rapidly rank correlation of distributions at two levels diminishes with increasing level separation. In this study, we show that z 0 varies widely for different properties (e.g., number and mass mixing ratios) and different hydrometeor types (cloud liquid and ice, rain, snow, and graupel) and that corresponding fall speed, V f , is the primary factor controlling the degree of their vertical alignment, with vertical shear of the horizontal wind playing a smaller role. Linear and power law parametric relationships between z 0 and V f are derived using cloud-resolving simulations of convection under midlatitude continental and tropical oceanic conditions, as well as observations from vertically pointing dual-frequency radar profilers near Darwin, Australia. The functional form of z 0 -V f relationship is further examined using simple conceptual models that link variability in horizontal and vertical directions and provide insights into the role of V f and wind shear. Being based on a physical property (i.e., fall speed) of hydrometeors rather than artificially defined and model-specific hydrometeor types, the proposed parameterization of vertical PDF overlap can be applied to a wide range of microphysics treatments in regional and global models. ©2019. The Authors.
Print ISSN:
2169-897X
Electronic ISSN:
2169-8996
Topics:
Geosciences
,
Physics
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