In this paper we introduce a setup to investigate aeolian saltation and surface dynamics on a centimetre spatial resolution and a sub second temporal resolution. We develop a Lagrangian saltation model and a high-resolution surface model, which we couple to each other and to a turbulence resolving large eddy simulation model. The simulated transport takes place primarily in the form of aeolian streamers, bursts of elongated transport structures parallel to the wind field, which result in a mass flux signal that is highly heterogeneous both in space and time. The temporal frequency responses up to 1 Hz of the mass flux and wind field share the same characteristics, which indicates a coupling between the two. The system can be in equilibrium, during which the stress profiles induced by the particles, the turbulent fluxes and the imposed large scale pressure gradient balance each other. A bimodal shape is found in the mass flux profile, in which we can distinguish an upper and lower saltation layer. The upper layer is associated with a transitional phase between transport by saltation and suspension that exists in the aeolian streamers. Furthermore, The setup is able to simulate ripples, although future research is needed to investigate the mechanisms that influence the final shape of the ripples.

The present study aims to assess and advance the prediction skill of solar radiation in high resolution weather forecasts by large-eddy simulations (LES). The GPU-Resident Atmospheric Simulation Platform (GRASP) was used to simulate the atmosphere around the Cabauw experimental site for atmospheric research (CESAR) in the Netherlands. Large-scale boundary conditions were provided by coupling the simulation to a general circulation model (GCM). Radiative tendencies were calculated using two di_erent implementations of the Rapid Radiative Transfer Method for GCMs (RRTM-G): one runs in advance of the simulation using pre-calculated atmospheric _elds, the other employs dynamically updated _elds during the simulation. Both con_gurations generated simulations of every day in 2016, which were compared to each other and validated using observations from the Baseline Surface Radiation Network (BSRN). This study revealed that the implementation of interactive radiation altered cloud representation in the simulations, in most cases causing clouds to rise. This process correlated with an increase in turbulence kinetic energy of up to 2 m2/s2 locally. The clouds that were raised tended to break up more often between 5 and 7 km altitude, leading to a decrease in average cloud fraction and increase in short-wave down-welling radiation. The results suggest that the implementation of interactive radiation enabled the development of cloud top entrainment instabilities, which could be responsible for the cloud breakup in these cases. Regardless of the chosen implementation of the radiative transfer method, large errors are made in the prediction of surface solar radiation. GRASP produced root mean squared errors (RMSE) of 122.4 W/m2 and 115.7 W/m2 using prescribed and interactive radiation, respectively, while the large-scale model used to provide the initial and boundary conditions to the simulation produced an RMSE of 87.3 W/m2. The large error in GRASP's prediction of surface solar radiation can partly be attributed to conversion errors made during GRASP's initialization of the thermodynamic state, which lead to erroneous diagnoses of the liquid water content in the atmosphere.

Offshore wind energy is considered as a powerful form of renewable energy generation. It plays an important role in accelerating the world’s transition towards sustainable energy sources and reducing carbon emissions by fossil fuels. This study focuses on the advancement of service planning, related to this renewable energy source, by researching high resolution metocean modelling. The research aims to assess and advance the modelling performance of typical metocean parameters by using atmospheric large-eddy simulations coupled to a spectral wave model. The GPU-Resident Atmospheric Simulation Platform (GRASP) coupled to Simulating WAves Nearshore (SWAN) was used to simulate the atmospheric- and oceanic conditions in the Gemini wind farm, located in Dutch waters. Large-scale boundary- and initial conditions were provided by the fifth generation of ECMWF’s ReAnalysis (ERA5). Relevant metocean parameters were modelled using two different coupling configurations. The one-way coupled simulation concerns the forcing of SWAN by GRASP friction velocities, for an accurate representation of the one-way momentum exchange to the ocean surface. The two-way coupled simulation concerns the momentum exchange of the friction velocity and roughness length. To accurately represent the sea surface roughness, the parameterization of Taylor and Yelland (2001) was used in this study. Both coupled configurations were used to simulate the first two months of 2017, which were subsequently validated using the available observations.
This study revealed that both coupled simulations caused a reduced value for the roughness length in wind- and wave wake conditions. Furthermore, a spatially averaged reduction in the sea state is observed due to the wake effect, where the magnitude of this wave deficit follows the line of a typical wind turbine thrust curve. The effect is however small compared to a realistic significant wave height. Besides, the two-way coupled simulations provided higher average roughness lengths in comparison to the one-way coupled simulations. This resulted in higher friction velocities and drag coefficients for the two-way coupled simulations, which subsequently reduced the time- and slab averaged wind profiles.
Moreover, the modelling performance of SWAN improves when it is forced by GRASP friction velocities instead of ERA5 wind fields. In addition, the established two-way coupled simulation is proven to be an enhancement for the spectral wave model in comparison to the one-way coupled configuration. The performance of the atmospheric large-eddy simulation could also benefit from the two-way coupled configuration. However, it is sensitive to the implemented roughness length parameterization.

In this study we are specifically interested in the role of convective momentum transport in a commercial fine-scale LES model that is used for wind predictions in the wind-energy sector. With this model, forced with the ECMWF IFS, a year-long of daily forecasts are run over Cabauw, the Netherlands, at a resolution of 40m in the horizontal and a resolution in the vertical of 8m that decreases with height to 80m. At this location atmospheric conditions range from stable to convective boundary layers, from clear sky to overcast days. The number of days with cumulus convection are surprisingly large: 144 out of 365 days.

Focusing on daytime hours, we separate days with and without cumulus convection, and with small and large cloudiness. For these different categories we show how the normalised momentum flux, and what this implies for drag in the lowest kilometre of the atmosphere. Here we are mindful of differences in background winds between the categories. A similar exercise is performed by separating cumulus days on surface buoyancy flux and cloud depth; four groups of increasing convection have been obtained.

It is found that in the presence of cumulus convection, the momentum transport is less linear than on overcast days. Clear-sky days show more drag in the lower half of the mixed layer compared to the cumulus case. Near cloud base, the drag is similar to that of cumulus days.

Separating cumulus days on convection we found that with decreasing buoyancy flux, the normalised momentum transport behaves less linear: in general cumulus days with the largest convection showed linear behaviour in the sub-cloud layer, cumulus days with the lowest convection showed a profile that tends to increase the wind speed near the surface and shows drag near cloud base.

Finally, using sensitivity experiments in which we remove the latent heating effect on buoyancy, we that the presence of moist convection specifically, change momentum mixing near and above cloud base, but not in the sub-cloud layer underneath.

Clouds are chaotic, difficult to predict, but above all, magnificent natural phenomena. There are different types of clouds: stratus, a layer of clouds that may produce drizzle, cirrus, clouds in the higher parts of the atmosphere, and cumulus, clouds that arise in convective updrafts. Thermals, rising air that is often used by birds and gliders to gain height, are an example of atmospheric convection. When the sun heats Earth’s surface layer, warm and moist air rises in thermals to higher parts of the atmosphere. In this way, convection transports heat and moisture vertically in the atmosphere. This often leads to the formation of clouds and heavy rainfall. A major part of the rainfall on Earth, especially in the tropics, is produced by cumulus clouds. Furthermore, convection and cloud formation affect the large-scale planetary circulation. In the atmosphere, these processes are of major importance for Earth’s weather and climate. Convection and clouds also play a major role in numerical simulations of weather and climate. With general circulation models, the large-scale wind circulation and variables such as temperature and humidity are calculated on a three-dimensional global grid. The model grid resolution is low, and therefore, smaller-scale processes such as convection and cloud formation can not be calculated explicitly. The impact of these small-scale processes has to be determined in another way. They are represented by parameterizations that give an estimate of the effect of the smallscale processes on the large-scale model variables. For models with relatively large columns, the presence of a large number of realizations of the same small-scale process justifies the expression of their effect on the large-scale variables in terms of statistical properties. For example, the effect of a large number of clouds can be represented statistically. A problem arises from the fact that the resolution of operational weather and climate models tends to increase. Generally speaking, with higher model resolutions the atmosphere can be simulated more accurately. However, if resolutions keep increasing, the expression of the small-scale effects in terms of statistical properties can no longer be justified. In a small model column, there is for example only space for a small number of clouds. The chaotic behavior of convective clouds becomes an important factor and deterministic parameterizations no longer give accurate estimates. The increase of fluctuations and randomness is a motivation for using stochastic convection parameterizations. The central research theme in this dissertation is stochastic convection parameterization. Stochastic processes are used in the representation of convective clouds. Traditional deterministic parameterizations only give an estimate of the expected value of the effect of small-scale variables. Stochastic parameterizations can deviate from this expected value and can produce a range of convective responses. Especially in models with a relatively high resolution, it is important that parameterizations can represent fluctuations around the expected value. There are several ways of introducing stochastics. In this dissertation, Markov chains are examined, stochastic processes that are named after the famous Russian mathematician Andrei Markov (1856-1922). Markov chains have a finite number of states of which the transition probabilities can be estimated from data. By inferring transition probabilities from high-resolution data of convection, Markov chains mimic convective behavior.
A Large-Eddy Simulation model is used to construct a data set. Large-Eddy Simulation models are able to resolve clouds and convection in detail. After inference of the Markov chains, they are able to mimic clouds and convection as observed in a field-experiment near Barbados. The same method has also been applied for convective clouds in Brazil. These Markov chains only work for a very specific range of atmospheric circumstances. Therefore, another Markov chain model is constructed from a large observational data set from a rain radar in Darwin, Australia. A larger range of atmospheric circumstances is covered, and the Markov chains can be applied more generally. The Darwin Markov chains are implemented in a climate model to stochastically parameterize convection. This improves the variability related to convection as well as the distribution of the simulated tropical precipitation. The Markov-chain model is not perfect yet; however, a large step has been made in the development of this stochastic method for usage in state-of-the-art weather and climate models.