On the forward modeling of radar Doppler spectrum width from LES: Implications for model evaluation.

Large-eddy simulations of an observed single-layer Arctic mixed-phase cloud are analyzed to study the value of forward modeling of profiling millimeter-wave cloud radar Doppler spectral width for model evaluation. Individual broadening terms and their uncertainties are quantified for the observed spectral width and compared to modeled broadening terms. Modeled turbulent broadening is narrower than the observed values when the turbulent kinetic energy dissipation rate from the subgrid-scale model is used in the forward model. The total dissipation rates, estimated with the subgrid-scale dissipation rates and the numerical dissipation rates, agree much better with both the retrieved dissipation rates and those inferred from the power spectra of the simulated vertical air velocity. The comparison of the microphysical broadening provides another evaluative measure of the ice properties in the simulation. To accurately retrieve dissipation rates as well as each broadening term from the observations, we suggest a few modifications to previously presented techniques. First, we show that the inertial subrange spectra filtered with the radar sampling volume is a better underlying model than the unfiltered -5/3 law for the retrieval of the dissipation rate from the power spectra of the mean Doppler velocity. Second, we demonstrate that it is important to filter out turbulence and remove the layer-mean reflectivity-weighted mean fall speed from the observed mean Doppler velocity to avoid overestimation of shear broadening. Finally, we provide a method to quantify the uncertainty in the retrieved dissipation rates, which eventually propagates to the uncertainty in the microphysical broadening.

Use of cloud radar Doppler spectra to evaluate stratocumulus drizzle size distributions in large-eddy simulations with size-resolved microphysics.

A case study of persistent stratocumulus over the Azores is simulated using two independent large-eddy simulation (LES) models with bin microphysics, and forward-simulated cloud radar Doppler moments and spectra are compared with observations. Neither model is able to reproduce the monotonic increase of downward mean Doppler velocity with increasing reflectivity that is observed under a variety of conditions, but for differing reasons. To a varying degree, both models also exhibit a tendency to produce too many of the largest droplets, leading to excessive skewness in Doppler velocity distributions, especially below cloud base. Excessive skewness appears to be associated with an insufficiently sharp reduction in droplet number concentration at diameters larger than ~200 µm, where a pronounced shoulder is found for in situ observations and a sharp reduction in reflectivity size distribution is associated with relatively narrow observed Doppler spectra. Effectively using LES with bin microphysics to study drizzle formation and evolution in cloud Doppler radar data evidently requires reducing numerical diffusivity in the treatment of the stochastic collection equation; if that is accomplished sufficiently to reproduce typical spectra, progress toward understanding drizzle processes is likely.

A framework based on 2-D Taylor expansion for quantifying the impacts of sub-pixel reflectance variance and covariance on cloud optical thickness and effective radius retrievals based on the bi-spectral method.

The bi-spectral method retrieves cloud optical thickness (τ) and cloud droplet effective radius (r e ) simultaneously from a pair of cloud reflectance observations, one in a visible or near infrared (VIS/NIR) band and the other in a shortwave-infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved τ and r e . In the literature, the retrievals of τ and r e are often assumed to be independent and considered separately when investigating the impact of sub-pixel cloud reflectance variations on the bi-spectral method. As a result, the impact on τ is contributed only by the sub-pixel variation of VIS/NIR band reflectance and the impact on r e only by the sub-pixel variation of SWIR band reflectance. In our new framework, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the τ and r e retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the τ and r e retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VIS/NIR band influences the r e retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in r e retrievals, leading to a potential contribution of positive bias to the r e retrieval. We test our framework using synthetic cloud fields from a large-eddy simulation and real observations from MODIS. The predicted results based on our framework agree very well with the numerical simulations. Our framework can be used to estimate the retrieval uncertainty from sub-pixel reflectance variations in operational satellite cloud products and to help understand the differences in τ and r e retrievals between two instruments.

Dissipation of marine stratiform clouds and collapse of the marine boundary layer due to the depletion of cloud condensation nuclei by clouds.

When the production of cloud condensation nuclei in the stratocumulus-topped marine boundary layer is low enough, droplet collisions can reduce concentrations of cloud droplet numbers to extremely low values. At low droplet concentrations a cloud layer can become so optically thin that cloud-top radiative cooling cannot drive vertical mixing. Under these conditions, model simulations indicate that the stratocumulus-topped marine boundary layer collapses to a shallow fog layer. Through this mechanism, marine stratiform clouds may limit their own lifetimes.