This thesis investigates the implementation of three-dimensional nudging into large-eddy simulation (LES) to assimilate observed atmospheric data into an LES model. 3D-nudging 'pushes' the thermodynamic fields in a simulation towards the desired observed fields. The aim is to test if such a method is useful in improving solar forecasts of stratocumulus-topped boundary layers. For this purpose 3D-nudging LES solar forecasts are compared to persistence forecasts and conventional LES-based forecasts. As a proxy for observations, exact thermodynamic fields from LES were used in this research. Using LES fields is advantageous as it provides full 3D thermodynamic fields but also dynamic fields for checking the turbulence in the different methods. Results show that 3D-nudging is quite capable of replicating the desired thermodynamic fields. Unfortunately, nudging comes with a penalty as it causes the turbulence built up in a simulation to be flawed. This effect is mitigated by the design of variations on the nudging technique, the most promising of which is multiple time fields nudging, which nudges the thermodynamic fields in a simulation to subsequent desired fields every 10 minutes during the nudging period. Solar forecasts found by this method are found to be more accurate than the persistence and regular LES methods on forecast horizons of 30 minutes and larger. Approaches proposed in this study to approximate thermodynamic fields from observational data estimate thermodynamic fields to a reasonable accuracy but are far from perfect, and thus it should be noted that solar forecast accuracy of the discussed methods will be less accurate when applied to real observations. Further research is recommended to focus on the use of the 3D-nudging methods in more LES case studies, and on devising better methods for the estimation of thermodynamic fields from observations.

Wind energy is becoming an important source of energy and reliable forecasts for the production of wind energy are needed to improve its integration in the power grid. The increasing height of wind turbines results in higher layers of the atmosphere being reached, where other phenomena than the surface-based ones can be of importance. One of these phenomena that can affect the wind energy production is a ramp up or a ramp down; a sudden increase or decrease in wind speed. One of the causes of a ramp down is a Frontal Low Level Jet (FLLJ), a jet stream which forms just ahead of a cold front. When the front has passed, the wind speed suddenly drops. It is yet unknown how well frontal Low Level Jets (LLJs) are simulated by a numerical weather prediction model. For this research, case studies of occurrences of FLLJs were determined based on Light Detection And Ranging (LiDAR) observations, synopticmaps and coarse-resolutionWeather Research and Forecasting (WRF) simulations. The chosen case studies were simulated with WRF using a finer resolution. These simulations were compared with surface observations from weather stations, wind profiler observations, LiDAR observations and observations from a wind farm. From these comparisons it was concluded that the WRF model performed relatively well in general. The general development and dissolving of the FLLJ were simulated correctly, but the timing and magnitude of these simulations can be improved.