Accurate classification of ice particles in clouds is essential for improving the understanding of cloud microphysics and improving weather and climate models.
This thesis investigates the use of spectral polarimetry in millimetre-wavelengths, combined with a Discrete Dipole Approximation (DDA) and Gaussian Mixture Model (GMM) scattering database, to classify ice particles through fuzzy logic. Utilizing a dual-wavelength (94 and 35 GHz), dual-polarized cloud radar installed in Cabauw, this study analyses two non-precipitating ice cloud events. Spectral polarimetric variables, including differential reflectivity (Z_DR), Slanted Linear Depolarization Ratio (SLDR), backscattering phase (φ_bs), and Dual Spectral Ratio (DSR), were derived from radar measurements and compared with modelled values from the scattering database. Results indicated that different ice particle types exhibited distinct polarimetric characteristics, but a lot of overlap between particles remained.

A fuzzy logic classifier was developed, incorporating both 1D and 2D membership functions to improve differentiability between particle types. Adding temperature and liquid water path as variables was necessary to distinguish between branched planar, aggregates and graupel particles. The classification results were mostly consistent and as expected, though there was a high dependence on temperature, suggesting areas for further refinement. Through fuzzy logic outputs Q and Q-gap, the most probable type of ice particles is identified and a first assessment on the quality of this identification is given.

This study demonstrates that combining spectral polarimetric variables with an advanced scattering database has potential to improve the classification of ice particles. In particular, the proposed technique could allow the classification of possible different ice particle types for each radar observation volume. The method lays the basis for future developments in cloud microphysics and radar-based ice particle classification.



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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. ... Read more

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. ... Read more

This study investigates radar measurements of small cloud particles using a 94-GHz cloud radar, focusing on backscattering cross-section, Doppler velocity, and associated aliasing effects. The Rayleigh scattering regime allows sensitive detection of cloud droplets, but high-frequency radar introduces challenges, including limited maximum unambiguous Doppler velocity and aliasing. Existing dealiasing methods often rely on spatial and temporal continuity or specialized radar modes, which are unavailable for the radar used in this study. A dealiasing method based on spatial and temporal continuity is developed, applied, and evaluated to improve the accuracy of mean Doppler velocity and radar-derived cloud measurements. ... Read more

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). ... Read more

Accurate rainfall intensity measurements are important to many applications like weather monitoring and forecasting, economics, urban design, agriculture and so on. A weather radar is one of the instruments to measure rainfall by reflected microwave signal from raindrops. However, the rainfall rate estimated by weather radar has various errors as it is an indirect measurement of rain. One type of errors relates to the measurement of other objects than raindrops and noise. These objects are termed clutter and represent buildings, trees, airplanes, birds, insects, … One type of techniques mitigates clutter and noise using polarimetric measurements in the spectral domain. This methodology, spectral polarimetric processing, can be reinforced by digital image processing techniques. Image segmentation is the image processing technique chosen due to its similar objectives with spectral polarimetric processing. There are two major challenges for this project. The first one is to search and design effective methods with the help of image segmentation techniques to remove clutter and noise while retaining precipitation. Most of the image segmentation studies were done to tackle images from other areas which have different features from compared to Range-Doppler spectrograms. The methods from image segmentation should be thoroughly tested before being applied. Another problem to be resolved is to assess the techniques applied without any ground truth data. And those methods used in previous studies may not be suitable for testing large data sets. A framework is proposed to understand and quantify the features of polarimetric spectrograms which can be useful for tackling the two challenges. Then, a new filtering technique, criteria for good results and evaluation methods are proposed based on the framework. Three different filtering techniques are tested by the evaluation methods in different scenarios. Finally, some points for future research are also discussed. ... Read more

Oceans cover a significant part of the Earth's surface. The coupling between the upper ocean and the atmosphere is very complicated with defied theoretical understanding, while it is essential for climate studies, weather prediction, and marine ecosystems. With the advent of spaceborne Synthetic Aperture Radar (SAR) systems, surface signatures of ocean and atmospheric processes have been revealed. As winds blowing over the ocean excite the wind waves, all undulations of the ocean surface are assumed as waves in this study. The primary sources for ocean surface signatures in SAR images are waves that are created by the exertion of the local wind stress. Wind waves cause changes in the backscattered power due to three mechanisms: specular reflections, Bragg scattering, and a contribution from wave breaking. A statistical multi-static normalized radar cross-section (NRCS) background model in terms of the directional wave spectrum is developed, considering both Bragg and non-Bragg mechanisms for various polarization states. As the qualitative comparison between optical and SAR data reveals a significant correlation in sea surface signatures, a synthetic attempt is made to estimate the SAR signals from optical signatures. This is realized with the transformation of the wave spectrum in a nonuniform medium, as a consequence of surface currents, and varying near-surface wind fields. A comparison between modeled NRCS and observations is presented. This modulated NRCS model advances the quantitative interpretation of the upper ocean dynamics from satellite measurements. ... Read more

Stratiform precipitation is one of the most important precipitation systems in the mid-latitudes. To gain understanding of the melting layer, which is part of a stratiform precipitating system, an algorithm which is able to select melting layer data from large datasets is required. The goal of this thesis work is to create and deliver a program which is able to performthis work. For development the available data was separated into three groups: no melting layer, only melting layer and the rest. Based on the second group, typical melting layer signatures are analysed. In particular the different signatures (reflectivity, polarimetric and Doppler) occur at different heights. Based on the knowledge gained from the analysis and focussing on the reflectivity and polarimetric signatures, three different approaches were taken to detect and characterise the melting layer fromthe data. The first approach is based on an existing method in literature, this approach acts as the reference method. The second approach is based on image processing techniques, while the third approach is based on machine learning techniques. The second approach is later abandoned because of limitations of the techniques investigated. The third approach, machine learning, is the main contribution of this thesis work. For analysis of the performance of the different algorithms an annotated test dataset is created which represents the entire dataset. The performance is determined by the melting layer detection probability using a confusion matrix and by the determination of errors of the melting layer boundaries. The reference method proved to show an extremely low false positive rate (0%) on the test dataset. This means that if the method detected a melting layer, it is almost certain that there is one. The overall detection probability was 80%. The method fails in detecting the melting layer when the peak reflectivity is below the threshold (30dBZ) used in the method. The detected upper melting layer boundary of the reference method is on average 365 m lower compared to the ground truth. The lower boundary is on average 149 m higher than the ground truth. This means that the reference method only selects a part of the entire thickness of the ML. The correlation between the lower boundary and the ground truth is higher than the upper boundary (0.987 vs. 0.965). The proposed machine learning method has a detection performance of almost 94%, which is higher than the reference method, but some false positives occur (3%). The upper boundary is on average 20 m above the ground truth, while the lower boundary is 66 m lower than the ground truth. The machine learned method thus overestimates the thickness of the ML. The correlation between the lower boundary and the true boundary is 0.976 and the correlation between the upper boundary and the true boundary is 0.966. The upper boundaries of both methods have a very similar correlation, while the lower boundary of the machine learning method has a lower correlation. However, despite the lower correlation of the lower boundary and the introduction of false positives, the machine learning method is an improvement over the reference method since it has a significantly higher detection probability and it captures the entire thickness of theML much better. It is therefore suited forML analysis. ... Read more

In many countries the number of wind turbines is growing rapidly as a response to the increasing demandfor renewable energy.Modern wind turbines are large structures, many reach more than 150 meters above theground. Clusters of densely spaced wind turbines, so called wind farms, are being built both on- and offshore. Wind farm installations relatively near to radar systems generate clutter returns that usually affect the normal operation of these radars. Interference caused by wind turbines is more severe for many radar systems than interference caused by stationary objects such as masts or towers. This is due to the rotating blades of the wind turbines. Many Doppler radars use a filter that removes echoes originating from objects with no or little radial velocity. However, these filters do not work for rotating objects such as the rotating blades of wind turbines. Wind turbines located around the line of sight of Doppler radars can cause clutter, blockage, and erroneous velocity measurements, affecting the performance of both military and civilian radar systems. As a result, the unwanted radar return from wind farms, known as Wind Turbine Clutter (WTC), is considered to be dynamic clutter due to the nonzero Doppler return created by rotating wind turbine blades. Nowadays numerous radar systems are developed in order to exploit the diverse information obtained through transmission of waves with different polarizations. This technique is widely known as polarimetry. Many targets of interest exhibit Radar Cross Sections which vary with different transmitted and received polarizations. Wind Turbines also experiences this variability. In this thesis we propose a method to optimal detect the presence of WTC with the use of radar polarimetry. Since the crucial part of this interference comes from the blades rotation, we initially propose a method to estimate the angular velocity of these blades. The estimation of this parameter is derived with the use of proper combination of maximum likelihood estimation theory and radar polarimetry. As there is absence of Micro-Doppler when the radar beam axis and rotation coincide, a separate estimator for this case is pro-posed. In the final part of this thesis, we present a detection approach based on the same signal model used for angular velocity estimation. Again we define a detection rule for the case when radar beam axis and rotation axis coincide and one when they do not. Although at some extent the used model for the second case is valid for low frequencies (f<1 GHz), both estimator and detector derivations can be further applied for higher frequencies signal models. All these mathematical derivations are accompanied with proper simulations. ... Read more

Wind is an important indicator of circulation related processes in the atmosphere. Accurate wind in- formation is used as an input for weather and circulation models. Wind data with a high temporal and spatial resolution are useful for research on the microphysics of the atmosphere, which is the area of application for the three-beam Transportable Atmospheric Radar (TARA) located at Cabauw Ex- perimental Site for Atmospheric Research (CESAR) in the Netherlands. Although some comparisons were performed using radiosondes, no thorough quality assessment of the wind estimation results has been done so far. During this research, the quality assessment is performed for the estimation of the wind speed and direction during the Analysis of the Composition of Clouds with Extended Polarization Techniques (ACCEPT) campaign, which took place in October and November 2014. This research shows that the wind estimation is working well during precipitation and a TARA-based criteria for the data selection to guarantee the quality of the wind retrievals is identified in the form of the coefficient of variation for the mean Doppler velocities.
The quality assessment of the results of the TARA wind estimation is done by comparing the wind retrievals to the measurements of the meteorological tower at a height of 200 m above the surface at minute resolution. For the assessment to be performed, an effective way of removing clear air measurements is needed, which is done for the whole ACCEPT-campaign using a rough selection of time steps including rain. This approach showed that the wind estimation is working well during precipitation and shows that an improved data selection is needed.
Several approaches towards improving the data selection for the wind estimation of TARA are per- formed. The most successful is the use of the coefficient of variation, which is defined as the standard deviation divided by the mean. This coefficient is calculated for the mean Doppler velocities, which are the input for the wind estimation algorithm. Comparing the results of the coefficient of variation method to the one based on rain selection shows that both return good results. This leads to the conclusion that the coefficient of variation is useful to improve the data selection. ... Read more

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. ... Read more