Dependence of Vertical Alignment of Cloud and Precipitation Properties on Their Effective Fall Speeds

Abstract

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, z0, indicating how rapidly rank correlation of distributions at two levels diminishes with increasing level separation. In this study, we show that z0 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, Vf, 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 z0 and Vf 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 z0‐Vf relationship is further examined using simple conceptual models that link variability in horizontal and vertical directions and provide insights into the role of Vf 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.

Role of Cloud Subgrid‐Scale Structure in Modulating Clouds Viewed by ISCCP, MODIS, and MISR Simulators

“…Larger values of L cw , as observed in the mid-and upper layers, imply that the parcels in convective cores are well organized and remain aligned even after ascending some distance. The lower, median, and upper quartiles are 0.9, 1.6, and 3.2 km for L cf and 0.5, 0.9, and 1.5 km for L cw , respectively, with the median values in good agreement with the global mean value of 2 km for L cf (Barker, 2008) and 1 km for L cw (Ovchinnikov et al, 2019). The parameter v exhibits pronounced differences above 400 hPa with large inhomogeneity produced by LIN and SAM1MOM and small inhomogeneity by M2005.…”

Atmospheric Instability Dominates the Long‐Term Variation of Cloud Vertical Overlap Over the Southern Great Plains Site

“…Although cloud radiative effect bias caused by above PDF overlap is proven to be quarter to half of the bias from cloud fraction overlap, the uncertainty in PDF overlap scheme still is nonnegligible (Wang, ). Recent study further points out that hydrometeor fall speeds can be used to improve the representation of vertical alignment of cloud and precipitation properties and develop PDF overlap parameterization in climate models (Ovchinnikov et al, ). Above studies and our result thus suggest that the effects of dynamical variables should be considered in the parameterizations of either cloud fraction overlap or PDF overlap to improve the calculation of radiative budget and further reduce the uncertainties in projection of future climate.…”

Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons

“…Unfortunately, in situ or remote sensing‐based aircraft observations of deep convective updrafts are costly, hazardous, and infrequent, having additional maximum aircraft altitude, endurance, and safety restrictions. Recently, profiling and scanning Doppler radar and/or multifrequency remote sensing techniques have also been suggested as partial solution to this absence of vertical air motion information within DCCs (e.g., Giangrande et al, 2016, 2013; Kollias et al, 2018; Kumar et al, 2015; May & Rajopadhyaya, 1996, 1999; North et al, 2017; Ovchinnikov et al, 2019; Ray et al, 1980; Williams, 2012). The modest spatiotemporal resolution O (1 km, <5 min), accuracy (often reported within 1–2 m/s in deep convective core regions for “instantaneous”/gate retrievals; e.g., Heymsfield et al, 2010), and availability for multiyear data sets are strengths for these methods.…”

“…Unfortunately, in situ or remote sensing-based aircraft observations of deep convective updrafts are costly, hazardous, and infrequent, having additional maximum aircraft altitude, endurance, and safety restrictions. Recently, profiling and scanning Doppler radar and/or multifrequency remote sensing techniques have also been suggested as partial solution to this absence of vertical air motion information within DCCs (e.g., Giangrande et al, 2016Giangrande et al, , 2013Kollias et al, 2018;Kumar et al, 2015;May & Rajopadhyaya, 1996, 1999North et al, 2017;Ovchinnikov et al, 2019; An emphasis for this study is on mature MCS updraft and downdraft properties in terms of draft core width, shape, and intensity characteristics. This will include select studies into the variability of those properties with changes in storm regional (midlatitude and tropical) and/or environmental controls.…”