In this study, a dual-polarization spectral analysis for retrieval of microphysical properties of ice hydrometeors is developed. It is shown that, by using simultaneous Doppler polarimetric observations taken at a 45° elevation angle, it is possible to discriminate between different types of ice particles. Particle size distribution parameters for maximally two dominating types of ice particles (aggregates and plates) observed above the melting layer are retrieved. Prior to the retrieval algorithm, a selection of possible types of ice particles based on environmental conditions is carried out. The retrieval procedure is based on a least squares optimization that simultaneously minimizes fit residuals in a Doppler power spectrum and spectral differential reflectivity. The proposed method is illustrated on transportable atmospheric radar (TARA) observations of stratiform rain collected on 19 September 2001 at Cabauw, Netherlands.@en

Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors.@en

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 raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dualpolarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values.@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

A new model-based iterative technique to correct for attenuation and differential attenuation and retrieve rain rate, based on a neural-network scheme and a differential phase constraint, is presented. Numerical simulations are used to investigate the efficiency and accuracy of this approach named NIPPER. The simulator is based on a T-matrix solution technique, while precipitation is characterized with respect to shape, raindrop size distribution and orientation. A sensitivity analysis is performed in order to evaluate the expected errors of this method. The performance of the proposed methodology on radar measurements is evaluated by using one-dimensional Gaussian shaped rain cell models and synthetic radar data derived from disdrometer measurements. Numerical results are discussed in order to evaluate the robustness of the proposed technique.@en