资源环境科技发展态势分析平台

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.