Dunkelflaute, meaning "dark doldrums" in German, denotes prolonged periods characterized by low wind and solar energy production, attributed to overcast skies and calm weather conditions. With wind and solar power assuming increasingly crucial roles within the European energy landscape, these extreme weather conditions (Dunkelflaute events) pose a significant challenge to grid stability. Addressing this challenge in power production, the objective of this dissertation is to comprehensively analyze Dunkelflaute events and devise both physical and machine learning-based methodologies for their prediction. The research goal is approached through three key aspects: 1) conducting a statistical analysis of the frequency, duration, seasonal variations, and associated weather patterns of Dunkelflaute events to gain insights into their impact and underlying characteristics; 2) developing diverse strategies for the identification and prediction of these events from both a modelling and data availability standpoint to enhance predictability; and 3) projecting future weather patterns under climate change scenarios and investigate the impact of climate changes on this extreme weather....

Weather and climate influence life in many ways; varying climatic conditions can be associated with varying human cultures and on a day-to-day basis, the weather influences our plans and mood. As such, a proper prediction of the weather is of great importance. Ultimately, the weather is fueled by solar irradiation, which changes sharply over the course of the day. The relative position of the sun is directly influencingthe meteorological properties in the atmosphere closest to the surface. The atmospheric state in the lowest few kilometers, known as the troposphere, is therefore characterized by a daily cycle: during daytime, the sun heats the air and at night, the atmosphere typically cools down and the wind settles a bit.
In spite of its omnipresence and importance, this diurnal cycle in weather patterns
is still not fully understood by the meteorological community. Consequently, it is also hard to describe and predict the weather properly. The challenges emerge from the fact that the weather is continuously evolving, and is therefore never ‘in balance’, which would simplify analysis of the processes.

Fog is of critical importance to forecast accurately, not least because of the hazard it presents to human safety. Yet, while weather forecasts have improved significantly over recent decades—and continue to improve—fog remains a particularly challenging phenomenon to predict. The research presented within this thesis takes a step back from prediction, and aims to better understand the conditions under which fog forms and deepens. Topics investigated include the observational likelihood of fog, the near-surface conditions during the infancy of a fog layer, the spatial variability of fog (and the influences thereon), and the growth and evolution of a fog layer.

In the present research, the activation parameterization method introduced by Nenes and Seinfeld (2003) was compared and evaluated to a remote sensing-based method by Rusli, Donovan & Russchenberg (2017) for determining the cloud drop number concentration. Both methods have fundamentally different approaches for indirectly determining the cloud droplet number concentration. The parameterization method is based on the Köhler Theory, in which the activation process of particles contained in a rising parcel is modelled for predicting the number concentrations of cloud droplets. The remote sensing method, on the other hand, applies theories about particle-light interactions. Since the remote sensing method determines the cloud droplet concentrations in a more direct manner than the parameterization method, it is regarded here as the reference. An agreement was found between the two models, with a relative error of cloud droplet number concentrations between 41.1% and 78.0%, which lead to errors of the cloud’s scattering intensity in the range of 13% and 26%. Despite some discrepancies between the obtained droplet concentrations, the parameterization model shows similar trends to the remote sensing observations. It was found that the updraft velocity that is needed as input variable for the parameterization model has the largest influence on the model’s prediction of droplets concentrations, and that it is likely to be an important cause for the seen discrepancies. Furthermore, the present research shows how assumptions were made on the size distribution input variable used in the parameterization model, which were not available from observations.

Weather observation is becoming more important than ever because extreme weather, such as tropical cyclones, thunderstorms and heavy rain, is more common nowadays. To observe and forecast the atmospheric phenomena at high spatial and temporal resolution, weather radar is well recognized as an effective tool. The prerequisite of using weather radar data is sufficient measurement accuracy. The focus of this thesis is to propose advanced techniques in clutter mitigation and calibration for weather radars to improve radar measurement accuracy. On the one hand, clutter mitigation techniques for signal-polarization, dual-polarization without cross-polar measurements and full-polarimetric radar systems are proposed to mitigate different types of clutter. On the other hand, a novel radar calibration technique is developed. With clutter suppression and techniques, clean and correct weather radar measurements are expected to be obtained.

Clouds and aerosols continue to contribute to the largest uncertainty to estimates and interpretations of the Earth's changing energy budget. By comparing relative humidity (RH) and attenuated backscatter ratio (ATB) data and deriving scattering hygroscopic enhancement factors at the Cabauw Experimental Site for Atmospheric Physics Research (CESAR), an attempt is made to better understand the process of aerosol hygroscopic growth. This is done by ground based ceilometer, hygrometer and SMPS technology gathering data during the ACCEPT campaign in 2014, resulting in a high temporal resolution, continuous measurements and the possibility to compare the data with ancillary information measured on the same location. Some aerosol number concentration (ANC) data is also taken into account as indication of the influence of ANC on ATB. For the three studied time intervals with a large variation (>30%) in RH, a convincing relation between RH and ATB is found, which can be linked to the Köhler curve when a direct relation between aerosol size and ATB is assumed. For these three time intervals, scattering hygroscopic enhancement factors are derived with similar γ values as reported in previous studies. Further research is required to analyze the correlation between RH and ATB more and rule out other influences on ATB.

Climate change and human influences are widely investigated. However, the processes of aerosol-cloud interactions are still not adequately known and the associated lack of knowledge causes uncertainties in climate change prediction. Therefore this study presents different approaches to investigate those interactions, in particular the Twomey-effect, which states that an increase in aerosol loading leads to an increase in cloud drop number density and a decrease in cloud drop effective radius, considering constant liquid water path.
The data analysed was obtained during the ASCII campaign 2016 at Ascension Island. Cloud and aerosol measurements were done by an ultra-violet (UV) lidar during the month September 2016. The cloud microphysical properties - cloud drop number density and cloud drop effective radius - were retrieved using the cloud property inversion retrieval algorithm. The cloud effective radius varied between 1.88 and 4.48 $\mu m$. The cloud drop number density had values in the range of 228-1690 $cm^{-3}$. Furthermore, the total aerosol profiles for clear sky scenes and the aerosol profiles below clouds were retrieved, solving the boundary-value-problem using the ’Klett’ approach. For the aerosol profiles below clouds an extra factor was introduced, accounting for multiple
scattering inside the clouds. The aerosol loading arrived at Ascension Island came mainly from the South (Atlantic Ocean) in the lower 1200m or from the East (African continent, biomass burning events) above 1200m. The aerosol-cloud interactions were examined for both the clear sky and the below cloud aerosols with the cloud properties. Both approaches gave evidence for the Twomey-effect.
Those results suggest that the UV-lidar is a suitable instrument for investigation of aerosol-cloud interactions. Future projects can use those approaches to gain more knowledge over the interactions, enabling a major improvement of climate change predictions.

Nowcasting, synonymous for short term (0-6h) forecasting of precipitation with high detail in terms of location, timing and intensity, is a dynamic field of research in weather forecasting that is relevant in mitigating the impact of severe weather events. Along with the development of remote sensing instruments, the first methods that were developed to nowcast precipitation are based on the extrapolation of radar echoes, which are still widely employed in mobile and web applications today. However, the forecasting skill of these extrapolation based methods degrades rapidly with time. Especially in the case of convective rainfall extrapolation methods are not able to forecast the development of these precipitation events that have a more turbulent nature, primarily because they lack the description of the physical processes that govern the formation and dissipation of convective precipitation. For that reason, efforts in nowcasting are shifting towards numerical weather prediction (NWP) based methods. “High resolution” (<4km) NWP models covering a limited area are employed for their ability to describe large scale convection. Still, especially for accurately forecasting precipitation, these high resolution models require a detailed initial state which is generally not achieved by conventional initialization from global models having a much coarser resolution (>25km). To obtain a more accurate and detailed description of the initial state so called data assimilation methods have been developed, which use detailed information from recent observations to update the model. In this study it is investigated whether the precipitation forecasting skill of a regional Weather Research and Forecasting (WRF) model covering the Benelux can be improved by means of variational data assimilation. To that end, a variety of atmospheric observations is assimilated into a 4-km resolution WRF model using the accompanying WRFDA system with a 3DVAR rapid update cycling strategy. Besides conventional observations from surface weather stations, airports and soundings, observations from remote sensing platforms like radar (reflectivity and radial velocity) and zenith total delay (ZTD) from GPS are also assimilated. In a case study featuring a large squall line it is shown that assimilation of every single type of observation results in a notable improvement of forecasting skill, except for the radar assimilation of hydrometeors whose impact on the forecast is negligible after having precipitated to the surface. Especially assimilation of estimated humidity based on radar reflectivity yields a significant improvement during the full 6 hours of the forecast. The ability of the other types of observations to positively impact forecasting skill is attributed the large scale forcing by wind convergence along the cold front that is driving the formation of precipitation. Overestimation of precipitation quantity in the scenario without assimilation however is aggravated when radar derived humidity is assimilated, most likely as a consequence of the rapid update cycling strategy.