Data collected by C-band and X-band radars in northwestern Italy are analyzed to study the behavior of the polarimetric variables in the ice region of precipitating clouds, with special emphasis on the specific differential phase K dp . It is found that stratiform precipitation, irrespective of the precipitation type at the ground and as opposed to convective systems, is characterized by well-pronounced positive differential reflectivity Z dr and K dp values near the model-predicted 2158C isotherm. The regions of enhanced Z dr and K dp are likely related to the growth of dendrite crystals in the region where the difference between the saturation vapor pressure over water and the saturation vapor pressure over ice is greatest. Coincident C-band and X-band measurements, in conjunction with electromagnetic scattering simulations, demonstrate that K dp scales with frequency, indicating that the ice particles in the vapor deposition preferential growth zone are Rayleigh scatterers. Peak values around 2.08 and 3.58 km 21 are observed at C band and X band, respectively. Most noteworthy is that an extended analysis of hourly and daily vertical profiles of C-band data over 1 year has shown that K dp observations around the 2158C temperature level in stratiform precipitation are well correlated (0.8) with the reflectivity in the underlying rain layer.

Most of the recent hydrometeor classification schemes are based on fuzzy logic. When the input radar observations are noisy, the output classification could also be noisy, since the process is bin based and the information from neighboring radar cells is not considered. This paper employs cluster analysis, in combination with fuzzy logic, to improve the hydrometeor classification from dual-polarization radars using a multistep approach. The first step involves a radar-based optimization of an input temperature profile from auxiliary data. Then a first-guess fuzzy logic processing produces the classification to initiate a cluster analysis with contiguity and penalty constraints. The result of the cluster analysis is eventually processed to identify the regions populated with adjacent bins assigned to the same hydrometeor class. Finally, the set of connected regions is passed to the fuzzy logic algorithm for the final classification, exploiting the statistical sample composed by the distribution of the dual-polarization and temperature observations within the regions. Example applications to radar in different environments and meteorological situations, and using different operating frequency bands-namely, S, C, and X bands-are shown. The results are discussed with specific attention to the robustness of the method and the segregation of the data space. Furthermore, the sensitivity to noise and bias in the input variables is also analyzed.

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

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n this paper we describe the main characteristics of the JEM-EUSO instrument. The Extreme Universe Space Observatory on the Japanese Experiment Module (JEM-EUSO) of the International Space Station (ISS) will observe Ultra High-Energy Cosmic Rays (UHECR) from space. It will detect UV-light of Extensive Air Showers (EAS) produced by UHECRs traversing the Earth's atmosphere. For each event, the detector will determine the energy, arrival direction and the type of the primary particle. The advantage of a space-borne detector resides in the large field of view, using a target volume of about 10(12) tons of atmosphere, far greater than what is achievable from ground. Another advantage is a nearly uniform sampling of the whole celestial sphere. The corresponding increase in statistics will help to clarify the origin and sources of UHECRs and characterize the environment traversed during their production and propagation. JEM-EUSO is a 1.1 ton refractor telescope using an optics of 2.5 m diameter Fresnel lenses to focus the UV-light from EAS on a focal surface composed of about 5,000 multi-anode photomultipliers, for a total of a parts per thousand integral 3a <...10(5) channels. A multi-layer parallel architecture handles front-end acquisition, selecting and storing valid triggers. Each processing level filters the events with increasingly complex algorithms using FPGAs and DSPs to reject spurious events and reduce the data rate to a value compatible with downlink constraints