Chen Y-S, Verlinde J, Clothiaux E E, Ackerman A S, Fridlind A M, Chamecki M, Kollias P, Kirkpatrick M P, Chen B-C, Yu G, Avramov A
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA.
NASA Goddard Institute for Space Studies, New York, NY.
J Geophys Res Atmos. 2018 Jul 27;123(14):7444-7461. doi: 10.1029/2017JD028104. Epub 2018 Jun 20.
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.
对观测到的单层北极混合相云进行大涡模拟分析，以研究毫米波云雷达剖面多普勒谱宽的正向建模在模型评估中的价值。对观测到的谱宽的各个展宽项及其不确定性进行了量化，并与建模的展宽项进行了比较。当在正向模型中使用亚网格尺度模型的湍动能耗散率时，建模的湍流展宽比观测值窄。用亚网格尺度耗散率和数值耗散率估算的总耗散率，与反演得到的耗散率以及从模拟垂直风速功率谱推断出的耗散率都吻合得更好。微物理展宽的比较为模拟中的冰属性提供了另一种评估手段。为了从观测中准确反演耗散率以及每个展宽项，我们对之前提出的技术提出了一些改进建议。首先，我们表明用雷达采样体积滤波后的惯性子范围谱，比未滤波的-5/3定律，是从平均多普勒速度功率谱反演耗散率更好的基础模型。其次，我们证明，从观测到的平均多普勒速度中滤除湍流并去除层平均反射率加权平均下落速度很重要，以避免高估剪切展宽。最后，我们提供了一种量化反演耗散率不确定性的方法，该不确定性最终会传播到微物理展宽的不确定性中。
