Kinematic and Dynamic Collision Statistics of Cloud Droplets From High-Resolution Simulations

We study the dynamic and kinematic collision statistics of cloud droplets for a range of flow Taylor microscale Reynolds numbers (up to 500), using a highly scalable hybrid direct numerical simulation approach. Accurate results of radial relative velocity (RRV) and radial distribution function (RDF) at contact have been obtained by taking advantage of their power-law scaling at short separation distances. Three specific but inter-related questions have been addressed in a systematic manner for geometric collisions of same-size droplets (of radius from 10 to 60 μm) in a typical cloud turbulence (dissipation rate at 400 cm2 s-3. Firstly, both deterministic and stochastic forcing schemes were employed to test the sensitivity of the simulation results on the large-scale driving mechanism. We found that, in general, the results are quantitatively similar, with the deterministic forcing giving a slightly larger RDF and collision kernel. This difference, however, is negligible for droplets of radius less than 30 μm. Secondly, we have shown that the dependence of pair statistics on the flow Reynolds number Rλ or larger scale fluid motion is of secondary importance, with a tendency for this effect to saturate at high enough Rλ leading to Rλ-independent results. Both DNS results and theoretical arguments show that the saturation happens at a smaller Rλ for smaller droplets. Finally, since most previous studies of turbulent collision of inertial particles concerned non-sedimenting particles, we have specifically addressed the role of gravity on collision statistics, by simultaneously simulating collision statistics with and without gravity. It is shown that the collision statistics is not affected by gravity when a \u3c ac, where the critical droplet radius ac is found to be around 30 μm for the RRV, and around 20 μm for the RDF. For larger droplets, gravity alters the particle-eddy interaction time and significantly reduces the RRV. The effect of gravity on the RDF is rather complex: gravity reduces the RDF for intermediate-sized droplets but enhances the RDF for larger droplets. In addition, we have studied the scaling exponents of both RDF and RRV, and found that gravity modifies the RDF scaling exponents for both intermediate and large particles, in a manner very similar to the effect of gravity on the RDF at contact. Gravity is shown to cause the scaling exponents for RDF and RRV to level off for large droplets, in contrast to diminishing exponents for non-sedimenting particles

Kinematic and Dynamic Pair Collision Statistics of Sedimenting Inertial Particles Relevant to Warm Rain Initiation

In recent years, direct numerical simulation (DNS) approach has become a reliable tool for studying turbulent collision-coalescence of cloud droplets relevant to warm rain development. It has been shown that small-scale turbulent motion can enhance the collision rate of droplets by either enhancing the relative velocity and collision efficiency or by inertia-induced droplet clustering. A hybrid DNS approach incorporating DNS of air turbulence, disturbance flows due to droplets, and droplet equation of motion has been developed to quantify these effects of air turbulence. Due to the computational complexity of the approach, a major challenge is to increase the range of scales or size of the computation domain so that all scales affecting droplet pair statistics are simulated. Here we discuss our on-going work in this direction by improving the parallel scalability of the code, and by studying the effect of large-scale forcing on pair statistics relevant to turbulent collision. New results at higher grid resolutions show a saturation of pair and collision statistics with increasing flow Reynolds number, for given Kolmogorov scales and small droplet sizes. Furthermore, we examine the orientation dependence of pair statistics which reflects an interesting coupling of gravity and droplet clustering

Towards an Integrated Multiscale Simulation of Turbulent Clouds on PetaScale Computers

The development of precipitating warm clouds is affected by several effects of small-scale air turbulence including enhancement of droplet-droplet collision rate by turbulence, entrainment and mixing at the cloud edges, and coupling of mechanical and thermal energies at various scales. Large-scale computation is a viable research tool for quantifying these multiscale processes. Specifically, top-down large-eddy simulations (LES) of shallow convective clouds typically resolve scales of turbulent energy-containing eddies while the effects of turbulent cascade toward viscous dissipation are parameterized. Bottom-up hybrid direct numerical simulations (HDNS) of cloud microphysical processes resolve fully the dissipation-range flow scales but only partially the inertial subrange scales. it is desirable to systematically decrease the grid length in LES and increase the domain size in HDNS so that they can be better integrated to address the full range of scales and their coupling. In this paper, we discuss computational issues and physical modeling questions in expanding the ranges of scales realizable in LES and HDNS, and in bridging LES and HDNS. We review our on-going efforts in transforming our simulation codes towards PetaScale computing, in improving physical representations in LES and HDNS, and in developing better methods to analyze and interpret the simulation results

Data from: Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: results from the ultraparameterized CAM

This data set contains the simulation outputs used in the study summarized below: We study the cloud response to a +4K surface warming in a new multiscale climate model that uses enough interior resolution to begin explicitly resolving boundary layer turbulence (i.e., ultraparameterization or UP). UP's predictions are compared against those from standard superparameterization (SP). The mean cloud radiative effect feedback turns out to be remarkably neutral across all of our simulations, despite some radical changes in both cloud microphysical parameter settings and cloud‐resolving model grid resolution. The overall low cloud response to warming is a positive low cloud feedback over land, a negative feedback (driven by cloud optical depth increase) at high latitudes, and weak feedback over the low‐latitude oceans. The most distinct effects of UP result from tuning decisions impacting high‐latitude cloud feedback. UP's microphysics is tuned to optimize the model present‐day, top‐of‐atmosphere radiation fluxes against CERES observations, by lowering the cloud ice‐liquid phase shift temperature ramp, adjusting the ice/liquid autoconversion rate, and increasing the ice fall speed. This reduces high‐latitude low cloud amounts and damps the optical depth feedback at high latitudes, leading to a slightly more positive global cloud feedback compared to SP. A sensitivity test that isolates these microphysical impacts from UP's grid resolution confirms that the microphysical settings are mostly responsible for the differences between SP and UP cloud feedback.This data set contains simulation data from "Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: results from the ultraparameterized CAM." All instructions and information are provided in the attached Readme.txt file. Please download the latest version for the full data set. Funding provided by: U.S. Department of EnergyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000015Award Number: DE-SC001254

DNS of hydrodynamically interacting droplets in turbulent clouds: parallel implementation and scalability analysis using 2D domain decomposition

Abstract The study of turbulent collision of cloud droplets requires simultaneous considerations of the transport of background air turbulence (i.e., geometric collision rate) and influence of droplet disturbance flows (i.e., collision efficiency). In recent years, this multiscale problem has been addressed through a hybrid direct numerical simulation (HDNS) approach (Ayala et al., J. Comp. Phys. 225 (2007) 51-73). This approach, while currently is the only viable tool to quantify the effects of air turbulence on collision statistics, is computationally expensive. In order to extend the HDNS approach to higher flow Reynolds numbers, here we developed a highly scalable implementation of the approach using 2D domain decomposition. The scalability of the parallel implementation was studied using several parallel computers, at 512 3 and 1024 3 grid resolutions with O(10 6 )-O(10 7 ) droplets. It was found that the execution time scaled with number of processors almost linearly until it saturates and deteriorates due to communication latency issues. To better understand the scalability, we developed a complexity analysis by partitioning the execution tasks into computation, communication, and * Corresponding author. Email address: [email protected] (Orlando Ayala) Preprint submitted to Computer Physics Communications September 18, 2013 data copy. Using this complexity analysis, we were able to predict the scalability performance of our parallel code. Furthermore, the theory was used to estimate the maximum number of processors below which the approximately linear scalability is sustained, showing that the 2D domain-decomposition implementation will make it feasible to run large HDNS simulations on scalable computers with O(100, 000) processors. The complexity analysis revealed that the pseudo-spectral simulation of background turbulent flow typically takes about 80% of the total execution time, except when the droplets are small (less than 5 µm in a flow with energy dissipation rate of 400 cm 2 /s 3 and liquid water content of 1 g/m 3 ), for which case the particle-particle hydrodynamic interactions becomes the bottleneck. The complexity analysis was also used to explore alternative methods to handle FFT calculations within the flow simulation and to advance droplets less than 5 µm in radius, for better computational efficiency. Finally, preliminary results are reported to shed light on the Reynolds number-dependence of collision kernel of noninteracting droplets