The YOGA-2012 dataset is the result of LES simulations driven by the Regional Atmospheric Climate Model (RACMO, see van Meijgaard et al., 2008), that span a full year of weather conditions over Cabauw, the Netherlands. The set-up, characterization and validation of the simulation runs are described in Schalkwijk et al. (2014). This report serves as a quickstart guide to the dataset itself.The dataset is available of two runs: YOGA-2012 and YOGA-HR-2012, which differ in resolution and domain size. There are a number of other differences between these two runs:• YOGA is performed using GALES version 5.0.8 and YOGA-HR using v5.3.6. Most of the differences between these two versions are extra options which were not used. The code was generally optimized and speed up, and the option to run on a grid spanning a number other than 2 N cells in the horizontal direction was added. This option was necessary to run at the higher resolution of YOGA-HR, since the decreased time-steps made a year-long run of 256 3 cells at this resolution unfeasible (we ended up running YOGA-HR at 192 2 × 180). There are also some minor bugfixes in the statistics routines, which fix the writing of fielddumps, for instance, such that those are available for YOGA-HR but not for YOGA.• Another major difference between YOGA and YOGA-HR, is the timing. After analyzing YOGA, we noticed that the timing was offset: at any time t, YOGA was forced by hourly data from RACMO that is averaged between t − 1 hr and t. We realized that this made YOGA representative of time t = t − 1/2 hr. For instance, if the sun came up (Q net suddenly increases), at time t 0 in RACMO, then this was 1

A HISTORY OF GLOBAL NUMERICAL WEATHERPREDICTION. The spectacular development of computational resources in the past decades has had a profound impact on the field of numerical weather and climate modeling. It has facilitated significant improvements in the description of key physical processes such as radiative transfer and has led to more accurate flow solvers. In addition, it has enabled sophisticated data-assimilation and ensemble-prediction schemes, both of which have turned out to be vital for improved prediction skill. Parallel to these developments, the increased computational power has led to a gradual but steady refinement of the computational grid. This has allowed models to resolve an increasingly large portion of the atmospheric scales of motion visualized in Fig. 1. The unresolved scales need to be approximated in a statistical way through statistical parameterizations, inevitably involving uncertain The historic evolution of computational grids is illustrated in the top panel of Fig. 2, which shows how the spatial scales treated by operational global numerical weather prediction (NWP) models have evolved in time. The range of resolved scales is visualized by a horizontal bar, with the largest scale (domain size) at the right and the smallest scale (resolution) at the left. The width of the bar is therefore a key measure of computational cost. Due to ever-increasing computational resources, operational NWP models have undergone an exponential increase in horizontal resolution. This growth started in 1974 with the model of the National Meteorological Center (NMC) at 300-km resolution (denoted N74; see Shuman 1989) and continued up to the resolution of 16 km that is now used by the latest version of the European Centre for Medium-Range Weather Forecasts model (E79-E10; see e.g., Simmons et al. 1989; European Centre for Medium-Range Weather Forecasts 2014). The red bars illustrate the computational breakthroughs by Miura et al. (M06;, who simulated the global weather for one week at 3.5-km resolution, and by Miyamoto et al. (M13;, who simulated 12 h at 0.87-km resolution some years later. While such exceptional cases cannot be performed on a Operational global NWP models are presently on the verge of using resolutions finer than the depth of the troposphere, L Trop (10 km) (see Fig. 1). This implies that they are beginning to resolve the vertical convective overturning by cumulus clouds, but still need its partial parameterized representation. This obstacle, known as the "gray zone" or "Terra Incognita" (Wyngaard 2004) is like the proverbial "chasm" that cannot be crossed in small steps. Ideally the representation of convective overturning at these resolutions should be distributed smoothly (i.e., as a function of resolution), between the subgrid parameterizations on the one hand and explicit simulation on the other (Molinari and Dudek 1992;Wyngaard 2004;Arakawa et al. 2011). This can be achieved by making parameterizations "scale aware," but a general framework for such an approach is presently lackin...

No abstract available.

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 d(t) ' d 0 e Lt , with d 0 as the initial error, d(t) as the error at time t, and L 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 z i and an Re-dependent regime for scales smaller than z i , which are found to decorrelate substantially faster for increasing Reynolds numbers.

A mixed-layer model is used to study the response of stratocumulus equilibrium state solutions to perturbations of cloud controlling factors which include the sea surface temperature, the specific humidity and temperature in the free troposphere, as well as the large-scale divergence and horizontal wind speed. In the first set of experiments, we assess the effect of a change in a single forcing condition while keeping the entrainment rate fixed, while in the second set, the entrainment rate is allowed to respond. The role of the entrainment rate is exemplified from an experiment in which the sea surface temperature is increased. An analysis of the budget equation for heat and moisture demonstrates that for a fixed entrainment rate, the stratocumulus liquid water path (LWP) will increase since the moistening from the surface evaporation dominates the warming effect. By contrast, if the response of the entrainment rate to the change in the surface forcing is sufficiently strong, enhanced mixing of dry and warm inversion air will cause a thinning of the cloud layer. If the entrainment warming effect is sufficiently strong, the surface sensible heat flux will decrease, as opposed to an increase which will occur for a fixed entrainment rate. It is argued that the surface evaporation will always increase for an increase in the sea surface temperature, and this change will be enlarged if the entrainment rate increases. These experiments aid the interpretation of results of similar simulations with single-column model versions of climate models carried out in the framework of the CFMIP-GCSS Intercomparison of Large-Eddy and Single-Column Models (CGILS) project. Because in a large-scale models, the entrainment response to changes in the large-scale forcing conditions depends on the details of the parameterization of turbulent and convective transport, intermodel differences in the sign of the LWP response may be well attributable to differences in the entrainment response.