We present a numerical solver for the incompressible Navier–Stokes equations that combines fourth-order-accurate discrete approximations and an adaptive tree grid (i.e. h-refinement). The scheme employs a novel compact-upwind advection scheme and a 4th-order accurate projection algorithm whereby the numerical solution exactly satisfies the incompressibility constraint. Further, we introduce a new refinement indicator that is tailored to this solver. We show tests and examples to illustrate the consistency, convergence rate and the application for the adaptive solver. The combination of the solver scheme and the proposed grid adaptation algorithm result in fourth-order convergence rates whilst only tuning a single grid-refinement parameter. The speed performance is benchmarked against a well-established second-order accurate adaptive solver alternative. We conclude that the present 4th order solver is an efficient design for problems with strong localization in the spatial-temporal domain and where a high degree of convergence for the solution statistics is desired.

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Adaptive grids hold an important advantage over the usage of static and pre-tuned grids, as the most challenging atmospheric scenarios are typically localized in the spatial and temporal domain. Examples include the radiative cooling of cumulus cloud interfaces and the diurnal cycle. We present results obtained with applying an adaptivegrid approach to a variety of modeling fidelities, ranging from turbulence resolving direct numerical simulations and large-eddy simulations to Reynolds-averaged techniques. Corresponding to existing literature, the overall analysis shows that the adaptive-grid approach may play an important role in the future of atmospheric modeling as it enables the required computational effort for running a model at an increased resolution to scale correspondingly with the complexity of the resolved physics. Which is necessarily favorable compared to the scaling of fixed-grid approaches. @en

It is well known that the representation of certain atmospheric conditions in climate and weather models can still suffer from the limited grid resolution that is facilitated by modern-day computer systems. Herein we study a simple one-dimensional analogy to those models by using a single-column model description of the atmosphere. The model employs an adaptive Cartesian mesh that applies a high-resolution mesh only when and where it is required. The so-called adaptive-grid model is described, and we report our findings obtained for tests to evaluate the representation of the atmospheric boundary layer, based on the first two GEWEX ABL Study (GABLS) inter-comparison cases. The analysis shows that the adaptive-grid algorithm is indeed able to dynamically coarsen and refine the numerical grid whilst maintaining an accurate solution. This is an interesting result as in reality, transitional dynamics (e.g. due to the diurnal cycle or due to changing synoptic conditions) are the rule rather than the exception.@en

We present a proof-of-concept for the adaptive mesh refinement method applied to atmospheric boundary-layer simulations. Such a method may form an attractive alternative to static grids for studies on atmospheric flows that have a high degree of scale separation in space and/or time. Examples include the diurnal cycle and a convective boundary layer capped by a strong inversion. For such cases, large-eddy simulations using regular grids often have to rely on a subgrid-scale closure for the most challenging regions in the spatial and/or temporal domain. Here we analyze a flow configuration that describes the growth and subsequent decay of a convective boundary layer using direct numerical simulation (DNS). We validate the obtained results and benchmark the performance of the adaptive solver against two runs using fixed regular grids. It appears that the adaptive-mesh algorithm is able to coarsen and refine the grid dynamically whilst maintaining an accurate solution. In particular, during the initial growth of the convective boundary layer a high resolution is required compared to the subsequent stage of decaying turbulence. More specifically, the number of grid cells varies by two orders of magnitude over the course of the simulation. For this specific DNS case, the adaptive solver was not yet more efficient than the more traditional solver that is dedicated to these types of flows. However, the overall analysis shows that the method has a clear potential for numerical investigations of the most challenging atmospheric cases.

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