Shallow cumulus clouds (ShCu) measurements are crucially important in evaluating Large-Eddy Simulations (LES) and ShCu-parameterizations in numerical weather and climate models. However, these data still mainly consist of one-dimensional profile data, often sampled by lidars or radars. A new method for adding multi-dimensional information is to use networks of multiple hemispheric cameras, which remotely observe ShCu in unprecedented spatial details constantly at high temporal frequency. These cameras provide a large field of view, enabling us to observe whole ShCu-life cycles. Thus, these networks strongly complement existing ground-based instruments. To objectively estimate camera networks' accuracy, we have to test them against virtual LES-cloud fields, that act as ground truth. However, for this purpose virtual camera projections of these cloud fields are needed.Our study aims to generate such projections by combining radiative transfer theory with open-source path-tracing. With these projections, we emulate our camera network, currently installed at the Jülich Observatory for Cloud Evolution (JOYCE), Germany as part of the ongoing SOCLES project. As input, we use LES-cloud fields. Via the emulated camera images, we reconstruct the cloud fields back in the same way the camera network does it from real-world images. However, by using artificial images over real-world images, we have the advantage of already knowing the whole cloud field. This knowledge enables us to statistically analyze and optimize our network. Concretizing this, here are our research objectives:Objectively estimate the efficiency of our camera networkAnalyze the capability of our camera network by investing how much of a cloud shell is on average visibleOptimize the camera network, using our new insightsOur camera network emulation works well in this workflow. For the selected days, about 70% of the mutually visible cloud grid boxes were rightly reconstructed by our artificial camera network. About 53% of a ShCu-cloud shell is averagely visible by a single stereo camera pair of our network at a single time point. With increasing distance between the two cameras of such stereo camera pairs, fewer cloud shell areas are detected. In fact, for every extra kilometer, about 3.3% of a cloud shell is lost on average.

This study utilizes the wealth of observational data collected during the recent MOSAiC drift experiment to constrain and evaluate 190 daily Large-Eddy Simulations (LES) of Arctic boundary layers and clouds at turbulence-resolving resolutions. A standardized approach is adopted to tightly integrate field measurements into the experimental configuration. Covering the full drift represents a step forward from single-case LES studies, and allows for a robust assessment of model performance against independent data under a broad range of atmospheric conditions. A homogeneously forced Eulerian domain is simulated, initialized with radiosonde and value-added cloud profiles. Prescribed boundary conditions include various measured surface characteristics. Time-constant composite forcing is applied, primarily consisting of subsidence rates sampled from reanalysis data. The simulations run for multiple hours, allowing turbulence and mixed-phase clouds to spin up while still facilitating direct comparison to MOSAiC data. Key aspects such as the vertical thermodynamic structure, cloud properties, and surface energy fluxes are satisfactorily reproduced and maintained. Specifically, the model captures the bimodal distribution of atmospheric states that is typical of Arctic climate. Selected days are investigated more closely to assess the model’s skill in maintaining the observed boundary layer structure. The sensitivity to various aspects of the experimental configuration and model physics is tested. The model input and output are available to the scientific community, supplementing the MOSAiC data archive. The close agreement with observed meteorology justifies the use of LES data for gaining further insight into Arctic processes and their role in Arctic climate change.

The complex spatial and temporal structure of cumulus clouds complicates their representation in weather and climate models. Classic meteorological instrumentation struggles to fully capture these features. Networks of multiple high-resolution hemispheric cameras are increasingly used to fill this data gap, and provide information on this missing multi-dimensional spatial information. In this study, a path-tracing algorithm is used to generate virtual camera images of resolved clouds in Large-Eddy Simulations (LES). These images are then used as a camera network simulator, allowing reconstructions of three-dimensional cloud edges from the model output. Because the actual LES cloud field is fully-known, the combined path-tracing and reconstruction method can be statistically analyzed. The method is applied to LES realizations of summertime shallow cumulus at the Jülich Observatory for Cloud Evolution (JOYCE), Germany, which also routinely operates a camera network. We find that the Blender path-tracing method allows accurate reconstruction of up to 70% of the visible cloud edges, depending on camera distance and accuracy thresholds. Additionally, we conducted sensitivity tests and find that our method remains consistent and independent of changes in its hyperparameters. The sensitivity of the stereo reconstruction algorithm to cloud optical thickness is investigated, finding a cloud boundary placement error of approximately 182 m. This error can be considered typical for cloud boundary reconstruction using stereo camera imagery in general. The results provide proof of principle for future use of the method for evaluating LES clouds against real camera imagery, and for further optimizing the configuration of such camera networks.

In this study the spatial structure of Trade Wind shallow cumulus populations is investigated as diagnosed from large-domain high resolution cloud-resolving simulations. The main objective is to establish how inter-cloud spacing depends on cloud size, information that is crucial for understanding cloud-radiation interaction and spatial organization, and for informing grey zone parametrizations. A high-resolution cloud-resolving ICON simulation of Caribbean shallow convective cloud fields is used, based on the NARVAL South field campaign. The size statistics of the simulated cloud population are found to compare well to those derived from available satellite images. Four expressions for the nearest neighbor spacing are analyzed, including classic definitions but also novel ones. We find that the dependence of cloud spacing on cloud size strongly depends on this definition. The relation is exponential for the spacing between clouds of similar size, while it is logarithmic for the spacing between clouds of any size. Further analysis suggests that the logarithmic dependence is caused by the abundance of closely-spaced small clouds. The exponential size-dependence is argued to reflect the mesoscale dynamics driving the horizontal size of large convective cells. The implications of the obtained results are briefly discussed.

Linking large-eddy simulations to local observations of clouds enables us to investigate clouds and microphysical processes and to improve our understanding as well as the respective representation in coarser models. Insights gained with large-eddy simulations can be applied within the development and evaluation of parameterizations for larger scale models, to bridge the gap between those models and the detailed local observations. In this study, various approaches for large-eddy simulations around local observations are explored and investigated with respect to remote sensing observations of clouds and their representation. By adding more ‘realism’ to the simulations and defining a well constrained setup, the representation of the daily variability at a mid-latitude site could be improved and shows promising results for continued research in the future. Especially the shown potential to investigate representativeness of column measurements will provide new insights into the analysis and construction of observational experiments.

Recent insights into the spatial organization of atmospheric convection have emphasized the importance of its correct representation in Earth System Models (ESM). This study explores new opportunities created when combining a thermal population model on a horizontal microgrid with a decentralized vertical transport model. To this purpose the recently proposed BiOMi population model (Binomials on Microgrids) is used. BiOMi mimicks a population of independent but interacting convective thermals, with their birth, movement and life cycle described as Bernoulli processes. Simple rules of interaction are introduced to reflect observed physical behavior in single cumulus clouds, such as pulsating growth and environmental deformation. Under these rules, thermals can congregate and form longer-lived coherent clusters or chains that resemble cumulus clouds. The formation and evolution of these clusters is a form of self-organization that retains convective memory. Through an online clustering method the microgrid is coupled to a spectral EDMF convection scheme, providing the cluster size distribution it needs as input. This way, the inherently 3D structure of organized convection can in principle be captured in reduced but efficient form. The system is fully decentralized in that central top-down bulk closures are avoided. The main science objective of this study is to provide proof of concept of decentralized frameworks of this kind. To this purpose the BiOMi-EDMF scheme as implemented in the DALES circulation model is tested for various LASSO cases of shallow convection at the ARM SGP site. We find that the scheme achieves stable and realistic diurnal quasi-equilibria (as shown in the figure), and that the associated self-organizing patterns on the microgrid are realistic. Impacts of spatial organization and convective memory on the parameterized transport will be investigated.

Late springtime Arctic mixed-phase convective clouds over open water in the Fram Strait as observed during the recent ACLOUD field campaign are simulated at turbulence-resolving resolutions. The main research objective is to gain more insight into the coupling of these cloud layers to the surface, and into the role played by interactions between aerosol, hydrometeors and turbulence in this process. A composite case is constructed based on data collected by two research aircraft on 18 June 2017. The boundary conditions and large-scale forcings are based on analysis data, while the case is designed to freely equilibrate towards the observed thermodynamic state. The results are evaluated against a variety of independent aircraft measurements. The observed cloud macro- and microphysical structure is well reproduced, consisting of a stratiform cloud layer in mixed-phase fed by surface-driven convective transport in predominantly liquid phase. A 3D volume rendering of the simulated liquid clouds is shown in the Figure. Comparison to noseboom turbulence measurements suggests that the simulated cloud-surface coupling is realistic. A joint-pdf analysis of relevant conserved state variables is then conducted, suggesting that locations where the mixed-phase cloud layer is strongly coupled to the surface by convective updrafts act as “hot-spots” for invigorated turbulence, cloud and aerosol interactions. A mixing-line analysis reveals that the turbulent mixing is similar to warm convective cloud regimes, but is accompanied by hydrometeor transitions that are unique for mixed-phase cloud systems. Distinct fingerprints in the joint-pdf diagrams also explain i) the typical ring-like shape of ice mass in the outflow cloud deck, ii) its slightly elevated buoyancy, and iii) an associated local minimum in CCN. The obtained modeling results advocate the application of this analysis method also to observational datasets.

A clustering method is applied to high resolution simulations of shallow continental convection to investigate the size dependence of coherent structures in the convective boundary layer. The study analyses the geometry of the clusters, along with their profiles of vertical velocity and total water. The main science goal is to assess various assumptions often used in spectral mass-flux convection schemes. Novel aspects of the study methodology include i) a newly developed clustering algorithm, and ii) an unprecedentedly large number of simulations being analysed. In total 26 days of LASSO simulations at the ARM-SGP site are analyzed, yielding roughly one million individual clusters. Plume-like surface-rooted coherent convective clusters are found to be omnipresent, the depth of which is strongly dependent on cluster size. The largest clusters carry vertical structures that are roughly consistent with the classic buoyancy-driven rising plume model, while smaller clusters feature considerable variation in top height. The cluster area is found to strongly vary with height and size, with small clusters losing mass and large clusters gaining mass below cloud base. Similar size dependence is detected in kinematic and thermodynamic properties, being strongest above cloud base but much weaker below. Finally the efficiency of the top-hat approach in flux parameterization is investigated, found to be 80-85 \% including a weak but well-defined dependence on cluster size. Implications of the results for spectral convection scheme development are briefly discussed.

Understanding cloud-circulation coupling in the Trade wind regions, as well as addressing the grey zone problem in convective parameterization, requires insight into the genesis and maintenance of spatial patterns in cumulus cloud populations. In this study a simple toy model for recreating populations of interacting convective objects as distributed over a two-dimensional Eulerian grid is formulated to this purpose. Key elements at the foundation of the model include i) a fully discrete formulation for capturing binary behavior at small population sample sizes, ii) object demographics for representing life-cycle effects, and iii) a prognostic number budget allowing for object interactions and co-existence of multiple species. A primary goal is to optimize the computational efficiency of this system. To this purpose the object birth rate is represented stochastically through a spatially-aware Bernoulli process. The same binomial stochastic operator is applied to horizontal advection of objects, conserving discreteness in object number. Implied behavior of the formulation is assessed, illustrating that typical powerlaw scaling in the internal variability of subsampled convective populations as found in previous LES studies is reproduced. Various simple applications of the BiOMi model (Binomial Objects on Microgrids) are explored, suggesting that well-known phenomena from nature can be captured at low computational cost. These include i) subsampling effects in the convective grey zone, ii) stochastic predator-prey behavior, iii) the down-scale turbulent energy cascade, and iv) simple forms of spatial organization and convective memory. Consequences and opportunities for convective parameterization in next-generation weather and climate models are discussed.

Multi-plume convection schemes based on discretized size densities can be easily made scale-aware by size filtering the parametrized plumes to fit the model resolution. Combined with their capability to smoothly transition from shallow to deep convection while being both numerically robust and computationally viable, these bin macrophysics models are well suited to the needs of high resolution NWP and regional climate modeling. One such approach is the ED(MF)n scheme which has previously been evaluated in a single column model against maritime cumulus LES. In this presentation we will confront the ED(MF)n scheme with long-term Doppler lidar observations of shallow cumulus days from the ARM-SGP site in Oklahoma. To do so the ED(MF)n scheme is implemented and tested in a non-hydrostatic model on a microgrid of 8x8 or 16x16 gridpoints. By using a ~10 km resolution this system behaves as a collection of weakly interacting single column models. We will compare the modeled relationship of vertical velocity to plume size against the relationship found in both the Doppler lidar observations and the LASSO LES run routinely at the ARM-SGP site. Next we examine which effect the assumed cloud-size distribution and entrainment as a function of plume size have on the resulting vertical transport.

In this study a spectral model for convective transport is coupled to a thermal population on a horizontal microgrid, with the goal of exploring new ways of representing impacts of spatial organization in cumulus cloud fields. The thermals are considered the smallest building block of convection, with thermal life cycle and movement represented through binomial functions. Thermals interact through two simple rules, reflecting pulsating growth and environmental deformation. Long-lived thermal clusters thus form on the microgrid, exhibiting scale growth and spacing that represent simple forms of spatial organization and memory. Size distributions of cluster number are diagnosed from the microgrid through an online clustering algorithm, and provided as input to a spectral multi-plume Eddy-Diffusivity Mass Flux (EDMF) scheme. This yields a decentralized transport system, with the thermal clusters acting as independent but interacting nodes that carry information about spatial structure. The main objectives of this study are i) to seek proof of concept of this approach, and ii) to gain insight into impacts of spatial organization on convective transport. Single-column model experiments demonstrate satisfactory skill in reproducing two observed cases of continental shallow convection at the ARM SGP site. Metrics expressing self-organization and spatial organization match well with large-eddy simulation results. We find that in this coupled system, spatial organization impacts convective transport primarily through the scale break in the size distribution of cluster number. The rooting of saturated plumes in the subcloud mixed layer plays a key role in this process.