Microphysical processes in shallow Arctic precipitation clouds are illustrated using measurements of differential reflectivity ZDR from the U.S. Department of Energy Atmospheric Radiation Measurement Program polarimetric X-band radar deployed in Barrow, Alaska. X-band hemispheric range height indicator scans used in conjunction with Ka-band radar and lidar measurements revealed prolonged periods dominated by vapor depositional, riming, and/or aggregation growth. In each case, ice precipitation fell through at least one liquid-cloud layer in a seeder–feeder situation before reaching the surface. A long period of sustained low radar reflectivity ZH (<0–5 dBZ) and high ZDR (6–7.5 dB) throughout the depth of the cloud and subcloud layer, coinciding with observations of large pristine dendrites at the surface, suggests vapor depositional growth of large dendrites at low number concentrations. In contrast, ZDR values decreased to 2–3 dB in the mean profile when surface precipitation was dominated by aggregates or rimed dendrites. Small but consistent differences in zenith Ka-band radar Doppler velocity and lidar depolarization measurements were found between aggregation- and riming-dominated periods. The clean Arctic environment can enhance ZDR signals relative to more complex midlatitude cases, producing higher values.

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We present a theoretical study of the bias in the copolar correlation coefficient (ρ hv ) caused by cross-polar radiation patterns and by unmatched horizontal and vertical copolar radiation patterns. The analysis of the bias induced by crosspolarization radiation is carried out for both modes of operation of polarimetric radars, designated as the simultaneous transmission and reception of horizontally and vertically polarized waves and the alternate transmission of horizontally and vertically polarized waves, respectively. The bias caused by unmatched horizontal and vertical copolar radiation patterns as a function of slight differences in pointing angles and beamwidths is also analyzed. In well-designed weather radars, for the purpose of hydrometeor classification, the overall acceptable bias in the copolar correlation coefficient should be less than about 0.01. The levels of crossto-copolar gain ratios for acceptable performance are indicated. Ultimately, pointing angle and beamwidth tolerances are indicated for horizontal and vertical copolar antenna patterns.

To date, few polarimetric weather radars have exhibited the capability to measure full scattering matrices. In contrast, in the synthetic aperture radar (SAR) community, considerable experience has been gained in dealing with complete scattering matrices and their statistical behavior. This paper aims to place weather radar parameters in a wider context in order to exploit more general concepts like target decomposition theorems and polarization basis transformations. Entropy, which is a fully polarimetric variable derived from the Cloude-Pottier decomposition, and the degree of polarization, which is derived from Wolf's coherence matrix, are the subject of this paper. The theoretical analysis carried out in the first part is checked against fully polarimetric data from POLDIRAD, which is the German Aerospace Center research weather radar. The entropy and the degree of polarization are compared with the copolar correlation coefficient in order to understand whether they can add value to radar meteorological investigations. Because the degree of polarization is available to conventional dual-polarization coherent systems, it is important to assess its potential for operational use.

Abstract. The correct classification of prevailing bulk hydrometeor type within a radar resolution volume is a challenge task even if a full set of polarimetric radar observables is available. Indeed scattering and propagation effects from the variety of hydrometeors present interact each others and sometimes, if not often, tend to obscure the characteristic signature of each weather radar target type. This consideration is enforced when the atmospheric volume is sampled with a wavelength where both Mie scattering effects and attenuation start to become relevant. In this paper, we utilize the hydrometeor classification scheme developed at the National Severe Storms Laboratory (USA). Briefly, the scheme uses a fuzzy logic approach to combine different polarimetric variables and environmental temperature in order to determine the most likely type of prevalent hydrometeor in the radar volume. This means that the resulting classification is based on two characteristics: the volume polarimetric responses and the thermal value. The relative balance between these two is managed through the coefficients in the fuzzy scheme. We have observed that these parameters are crucial in order to get "physical reasonable result", independently from the meteorological character of the event investigated. Our work is based on a reduced set of polarimetric variables (Z and ZDR) as input. Data used in this study were collected by a C-band radar over weather events ranging from convective to stratiform.