How clouds change due to global warming is a major source of uncertainty in global climate models. A large part of this uncertainty originates from the feedback of low clouds over the subtropical ocean. The processes that shape these clouds range from microphysical processes of condensation and evaporation to the large-scale circulations spanning thousands of kilometers. While different models can resolve the large scale circulations and the small scale of individual clouds increasingly well, there is a gap in understanding the scale in between: the mesoscale with its fascinating patterns. They exhibit a large continuous variability that affects not only their visual appearance, but also the amount of sunlight they reflect.
This thesis aims to analyze the temporal evolution of the mesoscale cloudiness by following the clouds on their path over the Atlantic along the trade winds and characterize their patterns by cloud metrics, cloud cover and mean cloud object length, computed on geostationary satellite images. Both decrease as the cloud fields move over warmer waters. The most prominent feature is the diurnal cycle in the cloud metrics that can at least partially be explained by oscillations in the atmospheric stability and the surface wind speed of the large-scale environment. Following the line of evidence found from these cloud controlling factors, we conclude that the decrease in low cloud amount and size due to increase in sea-surface temperature will most likely overcompensate the rise in cloud object size due to increase of stability. Especially stratocumulus clouds might become much less and also smaller. However, there remains a large, seemingly random variability in the cloud metrics that has little mean contribution to the climatology. As we cannot expect this to be the case in a warmer climate, we still need to understand what causes this variability. Therefore, the trade wind cloudiness needs to be further investigated with higher temporal resolution of the cloud controlling factors, more trajectories to bin for a fixed large-scale and better understanding of the interplay of governing processes both from models and observations.

Mixed-phase clouds, which have a significant impact on the global climate, are complex systems where liquid water and various types of ice particles coexist at temperatures below the freezing point. A key process in mixed-phase clouds is riming which alters microphysical and scattering properties of ice particles. Cloud radar is a powerful instrument for observing and understanding the processes that occur within mixed-phase clouds. Observations from multi-frequency radars and simulation results were combined in recent research to retrieve microphysical properties of ice particles in snowfall and ice clouds. This report presents an ambitious attempt to retrieve all common microphysical properties of ice particles, such as maximum dimension, density, aspect ratio and number concentration in slight rime condition using Doppler spectra. Two mixed-phase cloud events with low liquid water path are studied for such purpose. Spectral dual-wavelength ratio is introduced to retrieve maximum dimension of particles. An iteration process is developed in order to retrieve aspect ratio and density of ice particles from observation of spectral differential reflectivity. The number concentration of particles is retrieved with additional spectral reflectivity. With all the retrieved microphysical properties, ice water content and particle size distribution can be further derived. Ice water content is compared with results from an empirical model. The retrieved properties obtained from using three distinct mass-size relations are compared. Also the bulk and spectral retrieved profiles are compared. The retrieval process can provide consistent microphysical properties of ice particles. It is found that the retrieved ice water content is generally smaller than that from empirical model. Besides, the mass-size relation has significant impact on all retrieved microphysical properties except maximum dimension. The resulting profiles from bulk retrieval are smoother, while spectral retrieval can provide values in regions where the former cannot. The possible error from different sources are discussed or estimated, including the effect on dual-wavelength ratio from the elevation angle of radar, the neglect of differential attenuation caused by liquid and the usage of soft spheroid model. Recommendations are discussed, which include the usage of the latest microphysical models for ice aggregates and Discrete Dipole Approximation for electromagnetic wave scattering simulation.

Lightning is a natural phenomena that can be dangerous to humans. It is however challenging to study thunderstorm clouds using direct observations since it can be dangerous to fly into thunderstorm clouds. In this study, cloud radar with millimeter wavelength is used to study the properties and dynamics of thunderstorm clouds. It is based on a case of thunderstorm on 2021-06-18 from 16:10 to 17:45 UTC near Cabauw. Polarimetric radar variables are used to investigate possible hydrometeors in the clouds and look for vertical alignment of ice crystals that is expected due to electric torque. The technique of Doppler spectra analysis, which has not been used in previous studies about thunderstorms so far, is used to help understand the behaviours of different types of particles within a radar resolution volume. Due to challenges posed by Mie scattering, scattering simulations are carried out to aid the interpretation of spectral polarimetric variables. From the results, there is a high chance that supercooled liquid water and conical graupel are present in thunderstorm clouds. There is also a possibility of ice crystals arranged in chains at the cloud top. Ice crystals become vertically aligned a few seconds before lightning and return to their usual horizontal alignment afterwards. However, this phenomenon has been witnessed in only a few cases, specifically when the lightning strike is in close proximity to the radar's line of sight or when the lightning is exceptionally strong. Doppler analyses show that updrafts are found near the core of the thunderstorm cloud, while downdrafts are observed at the edges. Strong turbulence is also observed as reflected by the large Doppler spectrum width.

BaCla is a new stochastic parametric precipitation model to estimate rainfall associated with Tropical cyclones (TCs) in a computationally efficient way. It is validated for a number of calibration cases along with the current benchmark IPET deterministic method. The results of these models are compared with the observed StageIV-based rainfall. Two predictive parameters, the pressure deficit [hPa] (Δ𝑃) and maximum sustained wind speed [m/s] (vmax) are tested for the BaCla model. Frank copula is used by the BaCla model to predict the peak amount of rainfall. It employs the adapted Holland wind profile to create a 1D rain profile. Asymmetry may be added to make a 2D rain profile. The calibration study challenges the assumption that the radius of maximum wind speed (Rvmax) is equal to the radius of maximum precipitation (Rpmax) in the BaCla model. Additionally, it is observed that the radial fit overestimates rainfall in scenarios where the peak amount of rainfall is less than 2.8mm/hr. These observations lead to modifications in the relationship between Rvmax and Rpmax, the radial fit threshold, and the fitting coefficients of the adapted Holland wind profile for rainfall distribution in the improved BaClHa model. The new BaClHa model is verified with new and calibrated sets of TCs. The BaClHa model shows potential in achieving good accuracy along with global applicability at a limited computational expense.

Machine learning is becoming an increasingly important tool for climate scientists, but hampered by lacking uncertainty quantification. Here, a machine learning approach for detecting patterns indicating a changing climate is combined with probabilistic modelling to retrieve uncertainty values. We train neural networks on climate model simulations of temperature and precipitation under historical and future scenarios. We find that the resulting so-called Bayesian neural network (BNN) has similar predictive strength to an Artificial neural network (ANN), with a post-year 2000 mean absolute error of 9.00 years for temperature, but over-fits less. The BNN is able to recognise temperature change starting in 1994, which is 14 years later than the ANN. Our analysis shows that uncertainties in found climate patterns are much higher than the patterns themselves, reducing their value for further use. This work demonstrates that BNNs are a suitable tool for quantifying uncertainties of patterns indicating a changing climate.

The knowledge of the raindrop size distribution is key for characterizing precipitation. It is however still a challenge to retrieve it with radars. Several polarimetric and spectral techniques are proposed for cm-wavelength radars (weather radars). What about the mm-wavelength radars (cloud radars), which have a better spatial and time resolution and can still measure light and moderate rain? Knowing that 90% of the rain volume in Europe comes from rainfall rates between 0.1 mm/h and 10 mm/h, this is worthwhile to investigate. The goal of this thesis is to retrieve 1 of the 3 parameters of the modelled gamma raindrop size distribution, the median volume diameter (D₀), during stratiform rainfall events using a slantwise profiling dual-frequency polarimetric cloud radar. Focus is given to phase measurements, which are not affected by attenuation. Simulations show that the differential backscatter phase (δ_co) strongly depends on D₀. At mm-wavelength, backscattering and propagation effects need to be disentangled first. To achieve this, an algorithm to detect and characterize Rayleigh plateaus is proposed and implemented. After the application of this algorithm, a methodology to estimate the differential backscatter phase and its error is given. The 95% confidence interval of δ_co is estimated with the re-sampling method bootstrapping. 
Using simulation results, an attempt is made to find combinations of D₀ and the raindrop size distribution shape parameter μ that match with the confidence interval of δ_co. The confidence interval of δ_co restricts D₀, but not μ in most cases. This proposed technique is applied for both the 35 and 94 GHz frequency band of the new cloud radar at Cabauw (Ruisdael Observatory site near Utrecht). The resulting 95% confidence intervals of D₀ with 35 and 94 GHz and their overlap are compared with in-situ disdrometer measurements of the mass-weighted mean diameter (D_m) which is closely related to D₀. The median volume diameter retrieved with the 35 and 94 GHz frequency bands both shows a normalized cross correlation coefficient of 0.845 with the measured D_m of the disdrometer. Therefore, the cloud radar seems to have the capability to provide the detailed variations of the raindrops mean/median diameter like a local disdrometer, but at different heights. Nonetheless, the values differ. The disdrometer provides higher values than the cloud radar. One possible explanation is the inability of the disdrometer to measure raindrops smaller than 0.25 mm and the expected underestimation of the number of raindrops with sizes between 0.25 and 0.375 mm. However, because D₀ values retrieved from 35 GHz data are also higher than the ones at 94 GHz, further research, which can use all the methodologies proposed in this master thesis work, is needed to examine the quantitative values of the median volume diameter retrieval. 
These techniques can be implemented for all the single-frequency cloud radars (94 GHz) of the national Ruisdael Observatory (cloud and precipitation profiling mobile station and Lutjewad site above Groningen).

Torrential rain from tropical cyclones can have a devastating impact, causing loss of life and billions in damages. To better understand the risk faced by coastal communities, it is important to estimate how often a tropical cyclone could occur and how much rainfall it will produce. One way to do this is by analyzing past storms and building parametric models of rainfall rates during tropical cyclone events. While many parametric precipitation models –such as the Bader model– exist, their accuracy remains limited and many challenges still need to be overcome. The most important challenges are output overestimation and a poor representation of rainfall over land. Therefore, this thesis aims to reduce
these biases by answering the following research question:

"How can the bias in the radial rainfall distributions of a tropical cyclone in Bader’s
parametrized model be reduced and be used for reliable rainfall estimates both above land and the ocean?"

To answer this question, several new data sources were introduced from the TRMM/GPM satellites and Stage IV to improve the Bader model. While this original model only predicted precipitation based on maximum wind speed (vmax), the updated model also considers pressure deficit ΔP. The results suggest that ΔP can be a useful parameter to reduce bias and improve accuracy. However, it also
leads to larger uncertainty ranges. Next, four precipitation profiles were proposed. A profile where precipitation is constant for low maximum precipitation values based on the predicted total rainfall (area under the graph) was selected for further exploration.

The new models are explored during a case study of Hurricane Florence. Both the ΔP and vmax based models produced satisfactory results, compared to the benchmark IPET model. Moreover, an alternative fit above land has been proposed, where the highest precipitation is simulated at the eye. The proposed land fit improved the median of the predictions based on both vmax and ΔP. The ΔP
based model performed the best in the case study, however, no definitive conclusion could be reached upon which model is most suitable overall as more case studies would be required.

Finally the updated model has been compared to the original Bader model. The new data ensured better representation over land, the overestimation of precipitation was reduced, and the model was applied with more confidence outside of the training data set. Consequently, results showed an improvement
on its prediction capabilities. As a concluding remark, this research project highlights the importance of having insightful data to enhance the decision-making and risk management of natural hazards: a model that accurately quantifies uncertainty and the risks associated with a TC, representing a valuable tool for better understanding flood risk. Nonetheless, there are still several ways to further improve the modeled profiles (e.g., by including more data, introducing asymmetry or adding temporal autocorrelation).

Extra-Tropical Cyclones (ETCs) are major storm system ruling and influencing the atmospheric structure at mid-latitudes. These events are usually characterized by strong winds and heavy precipitation and cause considerable storm surges with threatening wave systems for coastal regions. The possibility to simulate these storms or to increase the amount of significant data available is crucial to optimize risk assessment and risk management for construction projects and territorial plans which might get damaged by events of this kind. The project addresses the possibility to learn the distribution of cyclones atmospheric fields of pressure, wind and precipitation in the North Atlantic by training a Generative Adversarial Network (GAN). The ETCs tracks are extracted from the ERA5 reanalysis dataset in the domain with boundaries 0°-90°N, 70°W-20°E and period going from 1st January 1979 to 1st January 2020. A GAN tries to learn the distribution of a training set based on a game theoretic scenario where two network competes against each other, the generator and the discriminator. The former is trained to generate new examples which are plausible and similar to the real ones by having as input a vector of random Gaussian values. The random vectors domain is called latent space. The latter learns to distinguish whether an example is coming from the dataset distribution or not. The competition set by the game scenario makes the network improve until the counterfeits are indistinguishable form the original. The generative models trained on the ETCs dataset are validated to understand if they are able to generate new samples of fields presenting similar atmospheric characteristics to those of the original dataset. To train the GAN two different loss function are considered, the Wasserstein distance and the Cramèr distance. The Cramèr Gan (CGAN) shows better performance in representing the distribution of the atmospheric fields, generating images that on average look similar to the original ones. The Wasserstein GAN (WGAN) behaviour shows poor performance in representing the precipitation in general, but it is able to similarly reproduce the values distribution for what concerns pressure and wind. The images generated by the WGAN have many differences compared to the original ones and are very blurry with particular data structures that looks like artefacts built by the network. The atmospheric structure of the images generated by the CGAN is investigated by considering 4 cyclones as case study and comparing the frames of their tracks to those of synthetic tracks generated by linear interpolation. The linear interpolation is performed between the random vectors generating the most similar images to the initial and final snapshot of the original track. The interpolated images show interesting features in the similarity with the original track, which suggest that the network has learned a representation of the ETCs fields that is promising for further investigation.