Spatial statistics of marine boundary layer clouds

Abstract

[1] An analysis is presented of the structure functions and scalar spectra for 25 satellitederived marine stratocumulus cloud optical depth fields. The scenes, which cover a horizontal domain of 58 Â 58 km at a resolution of 28.5 m, are partitioned into two ensembles on the basis of cloud fraction. For the fully cloudy scenes, although there is wide scene-to-scene variability, both the average isotropic scalar spectrum and the average isotropic second-order structure function exhibit power law behavior over approximately two decades, with scale-invariant exponents equal to those expected for inertial-subrange passive tracer fluctuations. Higher-order structure functions show anomalous scaling that closely matches that observed for wind tunnel temperature fluctuations and for other fully cloudy observations. The partly cloudy scenes, while scaling, show different behavior. The average isotropic second-order structure function and average isotropic scalar spectrum have scale-invariant exponents that are significantly smaller than those of the fully cloudy scenes, and the analysis of the higher-order structure functions indicates that the field has much more intermittent fluctuations than the fully cloudy scenes. Fits to random cascade models for the fully cloudy scenes show that the increment statistics are consistent with an underlying log normal distribution. For the partly cloudy scenes a divergence of higherorder moments is predicted, indicating that the field fluctuations are necessarily derived from fat-tailed distributions and that there will be significant realization dependence of the measured statistics. In addition, the presence of long-range correlations in all the data predicts that single-point histograms of the field values will have significant scene-to-scene variability, or equivalently, the use of spatial averages in the approximation of the parameters of the single-point probability density function of the field will result in random fluctuations of the estimated parameters.

Structure Function Analysis of Water Vapor Simulated with a Convection-Permitting Model and Comparison to Airborne Lidar Observations

“…Under the assumption that power-law scaling is satisfied for all encompassed scales, a simple relation holds between the scaling exponent of the second-order structure function z 2 and the slope b in the doublelogarithmic representation of the Fourier spectrum (Lewis et al 2004): Under the assumption that power-law scaling is satisfied for all encompassed scales, a simple relation holds between the scaling exponent of the second-order structure function z 2 and the slope b in the doublelogarithmic representation of the Fourier spectrum (Lewis et al 2004):…”

Assessing the Impact of Meteorological History on Subtropical Cloud Fraction

“…Prior work indicates that the dynamical forcing of stratocumulus clouds occurs at scales larger than the mesoscale (Rozendaal and Rossow 2003;Lewis et al 2004), meaning it is feasible to assess meteorological impacts on cloud properties using large-scale datasets. Norris and Iacobellis (2005) combined surface observations with meteorological reanalysis data to show that the dominant parameters associated with cloud properties are vertical velocity, advection, and sea surface temperature (SST).…”

Global atmospheric chemistry: Integrating over fractional cloud cover

“…Specification of cloud optical properties and cloud‐fraction in each layer is not sufficient to calculate the radiative transfer (e.g., heating and photolysis rates) without knowledge of how these cloud layers overlap. The required three‐dimensional knowledge of the cloud distributions cannot be simply derived from nadir satellite images, and considerable research focuses on cloud observations and on how to best represent cloud fields in atmospheric models [ Faure et al , 2001; Lewis et al , 2004; Brooks et al , 2005; Cornet et al , 2005; Gordon et al , 2005; Willen et al , 2005]. These models generally define the vertical distribution of clouds using either the random overlap or the maximum‐random overlap scheme [e.g., Collins , 2001].…”