Abstract

In radar polarimetry, the differential phase ${\mathrm{\Psi }}_{\text{DP}}$ consists of the propagation differential phase ${\mathrm{\Phi }}_{\text{DP}}$ and the backscatter differential phase ${\delta }_{\text{hv}}$. While ${\mathrm{\Phi }}_{\text{DP}}$ is commonly used for attenuation correction (i.e., estimation of the specific attenuation A and specific differential phase $K_{\text{DP}}$), recent studies have demonstrated that ${\delta }_{\text{hv}}$ can provide information concerning the dominant size of raindrops. However, the estimation of ${\mathrm{\Phi }}_{\text{DP}}$ and ${\delta }_{\text{hv}}$ is not straightforward given their coupled nature and the noisy behavior of ${\mathrm{\Psi }}_{\text{DP}}$, especially over short paths. In this work, the impacts of estimating ${\mathrm{\Phi }}_{\text{DP}}$ on the estimation of A over short paths, using the extended version of the ZPHI method, are examined. Special attention is given to the optimization of the parameter α that connects $K_{\text{DP}}$ and A. In addition, an improved technique is proposed to compute ${\delta }_{\text{hv}}$ from ${\mathrm{\Psi }}_{\text{DP}}$ and ${\mathrm{\Phi }}_{\text{DP}}$ in rain. For these purposes, diverse storm events observed by a polarimetric X-band radar in the Netherlands are used. Statistical analysis based on the minimum errors associated with the optimization of α and the consistency between $K_{\text{DP}}$ and A showed that more accurate and stable α and A are obtained if ${\mathrm{\Phi }}_{\text{DP}}$ is estimated at range resolution, which is not possible by conventional range filtering techniques. Accurate ${\delta }_{\text{hv}}$ estimates were able to depict the spatial variability of dominant raindrop size in the observed storms. By following the presented study, the ZPHI method and its variations can be employed without the need for considering long paths, leading to localized and accurate estimation of A and ${\delta }_{\text{hv}}$.

ABSTRACT

One of the most beneficial polarimetric variables may be the specific differential phase K _DP because of its independence from power attenuation and radar miscalibration. However, conventional K _DP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase Ψ_DP. In addition, the backscatter differential phase δ _hv component of Ψ_DP, significant at C- and X-band frequency, may lead to inaccurate K _DP estimates. In this work, an adaptive approach is proposed to obtain accurate K _DP estimates in rain from noisy Ψ_DP, whose δ _hv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δ _hv from Ψ_DP while maintaining its spatial variability. In addition, the standard deviation of the proposed K _DP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between K _DP and reflectivity, and K _DP standard deviation reduction.

Abstract

Phased-array radars (PARs) have the capability of instantaneously and dynamically controlling beam position on a pulse-by-pulse basis, which allows a single radar to perform multiple functions, such as tracking multiple storms or weather and aviation surveillance. Moreover, these tasks can be carried out with different update times to achieve the goal of better characterizing and forecasting the storms of interest. However, these tasks usually compete for finite radar resources, and scheduling algorithms are often needed to address resource contention. To capitalize on the PAR capabilities, an algorithm based on the concept of time balance (TB) is developed for adaptive weather sensing. Two quality measures are introduced to quantify the gain of adaptive sensing relative to standard scanning patterns used by the Weather Surveillance Radar-1988 Doppler (WSR-88D). A simulation experiment is performed to demonstrate the advantages of adaptive sensing and to test and verify the performance of the TB scheduling algorithm. It is shown that the gain of adaptive sensing can be realized by the TB scheduler; that is, storms of interest can be revisited more frequently within a relatively short period time compared to conventional scanning.

Abstract

Radar Doppler spectra that deviate from a Gaussian shape were observed from a tornadic supercell on 10 May 2003, exhibiting features such as a dual peak, flat top, and wide skirt in the nontornadic region. Motivated by these observations, a spectral model of a mixture of two Gaussian components, each defined by its three spectral moments, is introduced to characterize different degrees of deviation from Gaussian shape. In the standard autocovariance method, a Gaussian spectrum is assumed and biases in velocity and spectrum width estimates may result if this assumption is violated. The impact of non-Gaussian weather spectra on these biases is formulated and quantified in theory and, consequently, verified using four experiments of numerical simulations.

Those non-Gaussian spectra from the south region of the supercell are further examined and a nonlinear fitting algorithm is proposed to estimate the six spectral moments and compare to those obtained from the autocovariance method. It is shown that the dual-Gaussian model can better represent observed spectra for those cases. The authors’ analysis suggests that vertical shear may be responsible for the flat-top or the dual-peak spectra in the lower elevation of 0.5° and their transition to the single-peak and wide-skirt spectra in the next elevation scan of 1.5°.