Snow crystals consist of many different types of ice particles. Typical radar measurements observe only bulk properties of all types of ice particles present in a radar volume. Due to their difference in radar cross-section, larger particles will reflect more power of the transmitted radar signal, than small particles. If small and large particles are present in the same radar volume with comparable volume concentration, the radar measurements will be dominated by larger particles. Because of this, it is difficult to obtain microphysical properties of both large and small particle types, based on reflectivity alone. In this work, application of dual polarization spectral analysis for retrieval of microphysical properties of ice particles in stratiform precipitation is presented. Based on literature research, a selection on the particle types that are predominantly present in radar measurements is done. The selection is based on both radar cross sections of the different particles and meteorological conditions. The radar cross-sections are calculated with a model derived from literature, on the microstructure of ice crystals. With the obtained knowledge, it is shown that only aggregates and plates dominate spectral radar retrievals above the melting layer of stratiform precipitation. A model for the spectral horizontal reflectivity and spectral differential reflectivity of plates and aggregates, is created. This model is dependent on the parameters of the drop size distribution of plates and aggregates, the ambient wind velocity and spectral broadening. Using a sensitivity study of the spectral radar observables on the drop size distribution parameters, an algorithm is developed that is able to retrieve drop size distribution parameters of plates and aggregates from spectral radar measurements collected above the melting layer in stratiform precipitation. The drop size distribution parameters are retrieved by fitting the modelled spectral radar observables to the radar measurements using a non-linear least squares optimization technique. This kind of optimization algorithms, change the dependent variables of the model to obtain the best fit of the model to the measurement. The retrieval of drop size distribution parameters of plates and aggregates, is illustrated on data of stratiform precipitation collected by TARA. TARA is an S-band FM-CW dual polarization Doppler radar, situated at the measurement site Cabauw, The Netherlands. Using the outputs of the algorithm, it is shown that the obtained drop size distribution parameters are consistent. Verification of the outputs of the algorithm is performed by comparison of the estimated ice water content with the liquid water content, obtained below the melting layer. The estimated ice water content is also compared to the measured reflectivity. It is shown that the obtained results for plates are in good agreement with relations between ice water content and reflectivity obtained from literature. A new relation between the ice water content and the reflectivity is derived for aggregates.@en

The Transportable Atmospheric RAdar (TARA) is a S-band polarimetric FM-CW profiler that is located at Cabauw, the Netherlands. It is a part of European Union sponsored CESAR Observatory. At this wavelength (¿=9.1 cm), the wind tracers are raindrops, water cloud droplets, ice crystals and snowflakes. These particles are ranging in sizes from 0.01 mm to 10 mm. Clear air scattering, which consists of reflection echos from spatial differences in refractivity index of the order of ¿/2, gives another possibility of wind tracing in absence of hydrometeors. The aim of TARA is to provide simultaneous measurements of the dynamics and microphysics of clouds and precipitation by using the Doppler polarimetric measurement methodology. Therefore the TARA measurements can be carried out in single polarization mode, alternating polarization mode and/or wind measurement mode, that employs two offset beams. For the wind mode, which is the purpose of this paper, the radar TARA uses three independent beams to acquire Doppler spectra. The three measured mean Doppler velocities are input for the 3D wind estimates. They are measured with a high temporal resolution (1 s -10 s) and spatial resolution (3 m ¿ 30 m), allowing a detailed study of the troposphere. Because of the importance of a correct estimate of the vertical wind for clouds and boundary layer studies, one offset beam is looking at the zenith while the two other beams are respectively at 75 deg. and 69 deg. of elevation. A retrieval method of the three dimensional wind velocity is based on processing of Doppler spectra. Because it makes use of three consecutive beam observations, aliasing problem appears as the main drawback of this technique. This limitation is further enhanced by adding dual-polarization measurements for clutter suppression. As the maximum unambiguous measurable velocity decreases dramatically, a very important effort has been dedicated to palliate this important limitation. Therefore, a successful dual-polarization technique has been employed for the main beam dealiasing, while a more classical one was used for the other beams with good results, as well. It is shown that the polarimetric dealiasing technique carries out a significant clutter suppression. The results show the good behaviour of this wind velocity retrieval method. Different measurements have demonstrated how this technique behaves in different meteorological conditions, with or without precipitation.@en

Knowledge of water clouds is essential for climate studies. To understand present global climate and predicat climate changes, global observations of cloud microphysical properties are needed and space-based systems must be considered. Retrieval of cloud parameters from space challenges current tedchnological possibilities; not just due to sensor limitations, but also due to complex relationships between cloud parameters and remote sensing observables. Straight-forward retrieval of cloud microphysics with radar only., is hindered bu the presence of drizzle. To overcome this problem, synergetic use of multiple sensors is employed. This paper focusses on the retrieval of the cloud liquid water content by means of spaceborne radar and lidar measurements. The combination of radar reflectivity and lidar optical extinction is used to classify clouds according to their drizzle fraction. Appropriate retrival algorithms can then be applied to each category to obtain the liquid water content. As the method was initially developed for ground-based instruments, differences between sensing clouds from above and below are studied. Airborne data was then used to simulate space-based applications was established. It is shown that accurte liquid water content retrieval from space is possible.@en

No abstract available@en

Atmospheric signal in spaceborne radar interferograms can be used for both meteorological interpretation in atmospheric studies, as well as for subtracting it from interferograms intended for surface deformation or topography studies. We show that atmospheric signal can be conveniently described stochastically by a power-law behavior, where the absolute amount of energy in the signal, related to the weather situation, can be described using a χ2 probability density function, based on EUREF GPS data. We present single master stacking as well as cascaded interferogram stacking as methods to derive the atmospheric phase screen from the data. @en