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).

Over the period 1901-2012, the average global sea surface temperature has increased by 0.89 °C due to climate change and it is expected to increase consistent with global warming. As a consequence, marine ecosystems have become more susceptible to change in species and ocean chemistry. Due to the increase in sea surface temperature, the ocean pH has decreased in all regions. The main driver for this development is the uptake of approximately 30% of anthropogenic carbon dioxide (CO2) by oceans (IPCC, 2014). In a warming ocean, populations of warm-water species grow and populations of cold-water species decline. Furthermore, declines in coral growth are observed (Poloczanska et al., 2016). Marine cloud brightening is a proposed way to counteract climate change. This geo-engineering technique brightens clouds by spraying aerosols in the air which act as cloud condensation nuclei in order to form cloud droplets. As a result, the albedo of the brightened cloud increases and more sunlight is reflected, reducing the global mean temperature (Latham et al., 2012). Although it is a potential way to counteract climate change, marine cloud brightening has never been applied on a large scale. Also, little is known about its feasibility. We determined how physical properties of sea salt aerosols, specifically the aerosol mean geometric radius, the aerosol number concentration and the modal standard deviation of the initial aerosol spectrum, relate to the albedo of the brightened cloud using a numerical cloud parcel model with input parameters (Glassmeier et al., 2020). For six different values of the initial aerosol mean geometric radius, the initial aerosol number concentration and the modal standard deviation of the initial aerosol spectrum, i.e. the variability in aerosol radius, three output variables were generated in time and height. These output variables were the mean radius of cloud droplets, the cloud droplet number and the relative supersaturation. The differences caused by varying the input parameters were interpreted for the output variables. Also, we generated the albedo for the six different values of each input parameter mentioned. The cloud droplet number depends on the aerosol number concentration. The higher the number, the more cloud droplets are formed. The cloud albedo does not depend on the initial aerosol mean geometric radius. The moment of activation depends on the initial aerosol mean geometric radius and the initial aerosol number concentration. The growth of the cloud droplets is mainly driven by condensation when variability in aerosol radius is small. The higher the initial aerosol number concentration, the higher the albedo of the brightened cloud. When the variability in radius is bigger, collision-coalescence occurs as the spectrum of cloud droplets consists of bigger radii which tend to collide and coalesce. This leads to bigger and heavier droplets which precipitate due to an increased mass, resulting in a decrease in cloud droplet number and a decrease in cloud albedo. To conclude, three relations between physical properties of sea salt aerosol and the cloud albedo were found. First, the aerosol mean geometric radius at start does not affect the cloud albedo. Secondly, we found that the higher the aerosol number concentration, the higher the cloud albedo. Furthermore, a higher modal standard deviation of the initial aerosol spectrum leads to a lower cloud albedo. We recommend further research on other types of aerosols, like organic matter and algae, and the effect of external forcing, like wind, on cloud brightening.

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.

Uncertainty in the radiative forcing from anthropogenic activities since the Industrial Revolution is dominated by how clouds respond to aerosol. Climate projections are limited by this uncertainty. The cloud response to aerosol is influenced by the meteorological conditions of the atmosphere wherein the cloud is suspended. Understanding the covariation between meteorological state and cloud response to aerosol is a path forward to improve our understanding of aerosol effects on the climate. In this thesis we study aerosol-cloud interactions while controlling for meteorology using clustering techniques. This allows us to study the interactions per meteorological regime and gain deeper understanding of the effect of meteorology on aerosol-cloud interactions. Cloud-controlling factors are clustered using k-means clustering. Six meteorological clusters are found and satellite observations over ocean between -60 and 60 degrees latitude are used to study cloud response to changes in aerosol concentrations per cluster. The choice of clustering does not create significant variability in the sensitivity and the radiative forcing of the cloud albedo effect, but the sensitivity of cloud liquid water path and cloud fraction adjustments do show variability between clusters. This indicates that controlling for meteorology is specifically important for the adjustments to the cloud albedo effect. Our results show that the effective radiative forcing from aerosol-cloud interactions over our study domain since 1850 is -1.0 watts per square meter with a 90% confidence interval of [-1.6, -0.48] watts per square meter. There are variations in the forcing estimates depending on the number of clusters, but this signal is small compared to other sources of uncertainty. Our findings corroborate recent findings and present a novel method to control for meteorological covariation using cluster analysis.

Airport operations are highly dependent on safe weather conditions for their daily operations. Especially the visibility conditions are crucial. Low Visibility Procedures (LVP phase M, A, B and C) are defined at Schiphol Airport, resulting in capacity and runway use restrictions. These Low Visibility Pro­cedures are based on a post­processing model of a Numerical Weather Prediction (NWP): the Terminal Aerodrome Forecast Guidance (TAFG). This model provides a probability for the occurrence of a given LVP phase, based on thresholds of the Runway Visual Range (visibility including back luminescence at airports) and the ceiling (cloud base height). Currently, the TAFG is operational for three NWP models (HARMONIE, HIRLAM and ECMWF). This research found that the HIRLAM TAFG has a better performance than the HARMONIE TAFG. This result is caused by the larger training data­set that is used for the HIRLAM TAFG (20 years) compared the training data­set of for the HARMONIE TAFG (3 years). The ECMWF TAFG has a better performance for the severe LVP phases B and C. This is remarkable, since ECMWF is not often used for airport operations. However, the standard error of the performance indicator of the ECMWF TAFG is higher compared to the other models and, therefore, a larger data­set should be studied to confirm these findings.

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.

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.

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.

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.

Thanks to increasing computational resources, the grid sizes of global climate and weather prediction models are decreasing to scales at which subgrid processes, especially the evolution of clouds, can become spatially too variable for traditional deterministic parameterization approaches. We propose a stochastic parameterization in which clouds are represented by a Markov-Chain (MC) lattice model. The state of each lattice site is characterized by cloud top height (CTH) and cloud optical depth (COD). Based on these two parameters, clouds can be categorized into different cloud types (cumulus, stratus, cirrus, deep convection, etc.). In contrast to previous work, where the transition probabilities between different cloud types were calculated, we determine the transition probability densities in the continuous CTH-COD domain. We moreover explore the influence of neighbouring lattice sites on these transition densities to research whether it is possible to simulate cloud organization structures, without the use of extra external large scale variables or forcings. We estimate the parameters of the distributions using GOES-16 satellite images. Using these estimates, we perform simulations of the continuous state space Markov Chain over the lattice. This results in 2D spatial cloud evolutions, which spatial organization we compare to the original images. The simulations did not show any spatial structures as were observed in the original images. All the simulations resulted in a granulated image of different cloud types after only a couple of hours. A simulation from the MC model describing transitions from a finite number of cloud types converges to the observed occurrence frequencies, which is property of a MC. When describing the MC in a continuous state space the simulations still converged to the observed cloud state frequencies. However, introducing spatial dependence this property was not a given anymore. Since we were unable to reproduce the organizational structure from previous work using the GOES-16 satellite images, we cannot conclude whether it is possible to simulate cloud organization using the MC lattice model. A better understanding of these type of models was achieved. Neighbouring sides could diverge too fast in the performed models. We believe adding a stronger coupling between the cloud state evolution of neighbouring cells can improve the spatial models. Therefore one method to simulate correlated variables was tried to implement on the CTH evolution. This showed promising early results for further research. Also a better correction for horizontal advection on the original images could be performed to observe a specific cloud over time, which could improve the observed data quality.

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.

In this thesis we are interested in distinguishing patterns of mesoscale cloud patterns in the trades. Specifically, whether Sugar, Gravel, Fish and Flowers patterns can objectively be identified using physical quantities. For this purpose, we use cloud fraction data attained by the CORAL Ka-Band cloud radar at the Barbados Cloud Observatory during the boreal winter seasons of 2018, 2019 and 2020. These cloud fraction data represents the curves up until a height of 4 km for a given 6-hour interval of time. We do this to see if these clusters match up with the labels assigned to each cloud fraction curve obtained from a classification model used in Schulz (2021). Firstly, we map the cloud fraction curves onto points on a finite dimensional space using functional principal component analysis. We subsequently apply K-means, Gaussian Mixture Models and Mean Shift clustering onto the pre-processed dataset to identify any robust clusters. We have been able to attain robust Sugar-like clusters for K-means for 3 and 4 partitions and Mean Shift with bandwidth λ ≈ 585. This provides evidence that we are able to use cloud fraction data to distinguish Sugar. However, the same can not be said for Gravel, Fish and Flowers as we have not been able to identify them in our analysis. It is suggested for future research to do sensitivity analysis in the height interval of the cloud fraction data, that outliers are omitted and that the labeled data from Schulz (2021) are used instead of the mean and spread of the data pertaining to those labels.

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 (δco) 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 δco 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 δco. The confidence interval of δco 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 (Dm) 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 Dm 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).

Clouds, specifically shallow clouds, are known as a major source of uncertainty in global climate models. Shallow clouds over the global oceans show different spatial patterns and organizations that may be influenced by climate change. Besides, the frequency of these patterns can change the climate feedback of marine clouds in the subtropics. Experts in atmospheric sciences have proposed different kinds of organization metrics. In a recent paper (Janssens et al., 2021), 21 cloud organization metrics were collected and computed over 5000 satellite scenes of shallow clouds located near the east of Barbados. Almost all of the existing metrics of cloud organization are bulk parameters of the cloud field. Accordingly, over the same data as Janssens et al. (2021), this project aims to define a metric that originated from the mutual arrangements of the individual clouds in the field. To this end, network theory as a mathematical tool is employed to define new cloud organization metrics to explore how cloud objects interact with and coordinate concerning each other. It should be noted that this research is the first in which cumulus cloud fields are considered as complex spatial networks. In this regard, an important question is whether a new network metric can distinctly explain a variability which has not been captured by previously defined metrics in cloud organization. To address the research question, we utilize a multivariate regression model to understand whether a linear combination of principal components of the existing metrics can encapsulate a variation in newly proposed network metrics. We found that degree standard deviation and mean of clustering coefficient are two network metrics that can distinctly capture a variability that has not been encapsulated by the existing metrics in cumulus cloud organizations. Degree standard deviation simultaneously measures the homogeneity of the cloud size distribution and the distance between nearest neighboring clouds distribution. A large value of the average clustering coefficient indicates that the network field consists of either large clouds located on corners of triangles or relatively small clouds closely coagulated. Additionally, two sensitivity analyses were performed to understand how the main results are influenced by either center-spacing or edge-spacing distance between clouds and the nodes (clouds) located close to the boundaries of the field. Finally, one should note that all of the results of network theory-based approaches are comprehensively affected by how the network is defined. Defining a network that combines both geometric and physical interaction between cloud objects will be the future work of this research.

Although monitoring and maintenance of railways is important to ensure safety and avoid delays and financial losses, it is still mainly based on human inspection. The complexity of a railway along with the large area it extends makes manual monitoring difficult and time-consuming. The increasing availability of 3D acquisition technologies has made point clouds a widely used 3D data form. Thus, identifying the key components of a railway and its environment using 3D point cloud data can be the first step for automating this procedure. The past years, machine learning has become the most popular subfield of artificial intelligence used for various different applications, with its subfield of deep learning evolving dramatically. Although deep learning has been vastly researched on 2D data, applying deep learning on 3D point clouds can be challenging due to the irregularity, unstructuredness and unorderedness of such data. To overcome those challenges, recent approaches use projection or voxelization, while the latest methods focus on working directly on raw point cloud data. In the present research, 2 machine learning methods are implemented to classify 3D point cloud data of railway environments into 7 categories of rails, sleepers, track bed, masts, overhead wires, trees and other. To this end, the ensemble method of Random Forest, and the deep learning method of DGCNN (Dynamic Graph Convolutional Neural Network) are implemented. While Random Forest is a simple and handy ensemble algorithm used for a wide range of applications, DGCNN is a deep learning method based on PointNet, the pioneer method on raw point clouds, and graph CNNs. The methods are validated under 3 case studies, produced by structure from motion photogrammetry, airborne laser scanning and terrestrial laser scanning, and locating in 2 different areas within the Netherlands. For each method, 2 scenarios are developed for classifying colored and uncolored point clouds, respectively. Finally, the 2 methods are combined for the first scenario, as a first attempt to further improve the final results. The obtained results show that, in this work, DGCNN performs better than Random Forest, and both methods approximate state-of-the-art performance. The contribution of colors is important to improve both the overall accuracy of the models, as well as the classification results of the individual classes. More specifically, Random Forest scenario 1, Random Forest scenario 2, DGCNN scenario 1, DGCNN scenario 2, and the combination of Random Forest and DGCNN scenario 1 result in an overall accuracy of 88.65%, 83.39%, 89.19%, 88.20% and 90.57%, respectively. The corresponding per class F1-scores for all methods and scenarios range between 43% and 94%. Both methods meet difficulties in generalizing on data from different sensor systems and different areas with very different point density and missing data. Nonetheless, the individual methods are already very promising, while they are able to achieve the required accuracy of more than 90% when combined.

Cloud droplet number concentrations change due to perturbations in aerosol concentrations. The strength of this correlation covaries with meteorology. Using polarimetric aerosol estimates and MODIS-2 cloud retrievals we compute the interaction strength per meteorological regime, which we determined using clustering techniques on MERRA-2 reanalysis data. The clusters that were found are similar to other clustering studies. The clusters are however not well-separated. The resulting interaction strengths are slightly higher compared to previous satellite studies. The clusters which show large scale vertical movement, generally have higher interaction strengths.

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.