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Siddhartha Mukherjee, Jerôme Schalkwijk, and Harmen J. J. Jonker

Abstract

The predictability horizon of convective boundary layers is investigated in this study. Large-eddy simulation (LES) and direct numerical simulation (DNS) techniques are employed to probe the evolution of perturbations in identical twin simulations of a growing dry convective boundary layer. Error growth typical of chaotic systems is observed, marked by two phases. The first comprises an exponential error growth as , with δ0 as the initial error, δ(t) as the error at time t, and Λ as the Lyapunov exponent. This phase is independent of the perturbation wavenumber, and the perturbation energy grows following a self-similar spectral shape dominated by higher wavenumbers. The nondimensional error growth rate in this phase shows a strong dependence on the Reynolds number (Re). The second phase involves saturation of the error. Here, the error growth follows Lorenz dynamics with a slower saturation of successively larger scales. An analysis of the spectral decorrelation times reveals two regimes: an Re-independent regime for scales larger than the boundary layer height and an Re-dependent regime for scales smaller than , which are found to decorrelate substantially faster for increasing Reynolds numbers.

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Jerôme Schalkwijk, Harmen J. J. Jonker, and A. Pier Siebesma

Abstract

A modeling framework is developed that extends the mixed-layer model to steady-state cumulus convection. The aim is to consider the simplest model that retains the essential behavior of cumulus-capped layers. The presented framework allows for the evaluation of stationary states dependent on external parameters. These states are completely independent of the initial conditions, and therefore represent an asymptote that might help deepen understanding of the dependence of the cloudy boundary layer on external forcings. Formulating separate equations for the lifting condensation level and the mixed-layer height, the dry and wet energetics can be distinguished. Regimes that can support steady-state cumulus clouds and regimes that cannot are identified by comparison of the dry and wet buoyancy effects. The dominant mechanisms that govern the creation and eventual depth of the cloud layer are identified. Model predictions are tested by comparison with a large number of independent large-eddy simulations for varying surface and large-scale conditions and are found to be in good agreement.

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Jerôme Schalkwijk, Harmen J. J. Jonker, A. Pier Siebesma, and Erik Van Meijgaard

Abstract

Since the advent of computers midway through the twentieth century, computational resources have increased exponentially. It is likely they will continue to do so, especially when accounting for recent trends in multicore processors. History has shown that such an increase tends to directly lead to weather and climate models that readily exploit the extra resources, improving model quality and resolution. We show that Large-Eddy Simulation (LES) models that utilize modern, accelerated (e.g., by GPU or coprocessor), parallel hardware systems can now provide turbulence-resolving numerical weather forecasts over a region the size of the Netherlands at 100-m resolution. This approach has the potential to speed the development of turbulence-resolving numerical weather prediction models.

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Jerôme Schalkwijk, Eric J. Griffith, Frits H. Post, and Harm J. J. Jonker

No abstract available.

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Jerôme Schalkwijk, Harmen J. J. Jonker, A. Pier Siebesma, and Fred C. Bosveld

Abstract

Results are presented of two large-eddy simulation (LES) runs of the entire year 2012 centered at the Cabauw observational supersite in the Netherlands. The LES is coupled to a regional weather model that provides the large-scale information. The simulations provide three-dimensional continuous time series of LES-generated turbulence and clouds, which can be compared in detail to the extensive observational dataset of Cabauw. The LES dataset is available from the authors on request.

This type of LES setup has a number of advantages. First, it can provide a more statistical approach to the study of turbulent atmospheric flow than the more common case studies, since a diverse but representative set of conditions is covered, including numerous transitions. This has advantages in the design and evaluation of parameterizations. Second, the setup can provide valuable information on the quality of the LES model when applied to such a wide range of conditions. Last, it also provides the possibility to emulate observation techniques. This might help detect limitations and potential problems of a variety of measurement techniques.

The LES runs are validated through a comparison with observations from the observational supersite and with results from the “parent” large-scale model. The long time series that are generated, in combination with information on the spatial structure, provide a novel opportunity to study time scales ranging from seconds to seasons. This facilitates a study of the power spectrum of horizontal and vertical wind speed variance to identify the dominant variance-containing time scales.

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