Rotating phased array radars utilize advanced technology for meteorological observations; however, these radars may experience misalignment in azimuth pointing. This study proposes a calibration approach for azimuth pointing in rotating phased array radars based on ground clutter (GC) correlation. First, the proposed method adaptively determines the number of clutter bins for each direction in the plan position indicator. Second, the azimuth calibration numbers are derived by optimizing the correlation between the GC echoes of two successive scans. Third, in light of the radar’s physical rotation constraints, the proposed method includes a process for handling situations when GC is not available. The effectiveness of the proposed method was experimentally assessed using data from thousands of rays collected under both rainy and clear-air conditions. The results obtained using the proposed method demonstrate an improvement compared with radar readings using standard angle encoders. This azimuth pointing calibration method is easy to carry out and achieves more accurate azimuth angles than current methods; it has the potential for use in operational radars.

One of the significant challenges in using polarimetric phased array radars (PPARs) for weather observations is the implementation of dual polarization with an acceptable level of cross-polar isolation induced by the antenna. This paper quantitatively analyses and compares the bias caused by cross-coupling in different modes. For the alternately transmitting and simultaneously receiving (ATSR) mode, simultaneously transmitting and simultaneously receiving (STSR) mode, and the pulse-to-pulse phase coding STSR (PC-STSR) mode, the bias is quantified as a function of the maximum cross-polar power, while for the alternate orthogonal waveform STSR (A-OW-STSR) mode, the bias is a function of the maximum cross-polar power and waveform isolation. In the meantime, the cross-polar isolation required in different modes to attain the accuracy of polarization and spectral moment parameters measurement is quantitatively analyzed. The results from theoretical developments and simulation analysis exhibit that the A-OW-STSR mode can obtain an unbiased estimate of velocity and have an equal requirement for cross-polar isolation as the PC-STSR mode. Additionally, the A-OW-STSR mode can not only obtain the polarization scattering matrix (PSM) of the target within one pulse repetition time but also ensure accurate precipitation observation. Therefore, it can be used as an alternative transmitting mode for multi-function polarimetric phased array radar.

In the context of escalating frequency spectrum congestion, the prevalence of radio frequency interference (RFI) poses a growing challenge for weather radars, compromising data quality and adversely affecting the accuracy of variable estimations. This paper proposes a novel algorithm for the separation of precipitation and RFI, based on Low-Rank Sparse Decomposition (LRSD). When precipitation and RFI overlap, this method effectively filters out RFI while minimizing its impact on precipitation by analyzing the property difference between precipitation and RFI in the time domain. The proposed algorithm operates on the assumption that precipitation exhibits low-rank properties, whereas RFI manifests as sparse signals. This assumption is grounded in the observation that RFI in weather radars is typically unintentional, occupying a limited number of pixels in the range-time power image, while precipitation demonstrates approximate stationarity with a slow speed within a coherent processing interval (CPI). This algorithm is intended to alleviate the adverse effects of RFI, and the efficacy of the approach is validated using data collected by polarimetric Doppler weather radar systems at the Royal Netherlands Meteorological Institute. This paper systematically evaluates the impact of the proposed LRSD method in comparison to two traditional RFI mitigation strategies(e.g., the Vaisala-3 method and 2D filter) on the quality of meteorological data. The results demonstrate that the proposed LRSD method outperforms the alternatives in terms of RFI suppression and precipitation retention performance. At present, the LRSD method loses phase information, which may impact the accuracy of measuring polarization parameters of precipitation.

Spectral polarization filtering in the range-Doppler domain plays an important role in weather radar clutter mitigation. However, when ground clutter and precipitation overlap, these methods tend to filter out clutter-contaminated precipitation, leading to estimation errors. To address this problem, this paper proposes a ground clutter and noise mitigation method based on range-Doppler spectral processing for polarimetric Doppler weather radars. The proposed method can filter out clutter and noise and retain precipitation overlapped with clutter by analyzing the property differences between precipitation and clutter and noise in the range-Doppler spectrogram. Specifically, due to the spatial continuity of precipitation in the range-Doppler domain, the spectral moments (e.g., velocity and spectral width) are also continuous. In addition, polynomial fitting is used to compensate for the missing spectral moments, and the missing precipitation is recovered by Gaussian fitting using the computed spectral moments. The results demonstrate performance improvements after applying the proposed method to radar data collected by Chinese operational weather radars. The proposed method is compared with several other algorithms, and the comparison results show that the proposed method performs best in clutter and noise suppression and precipitation retention performance.

Conventional polarimetric measurement systems typically require multi-channel polarization information, leading to high hardware cost and complexity. To address this limitation, this letter proposes a low-cost method for polarization scattering matrix (PSM) measurement using single-channel polarization information. By employing a polarization modulator in a single-channel front-end to cycle the polarization along a Poincaré sphere orbit, a unique triple-line spectrum is generated. The elements in PSM are then estimated by decoupling these spectral components. The underlying principles, polarization-agile orbit constraints, and performance limits of the method are thoroughly analyzed. Simulations demonstrate high-precision PSM estimation for various types of targets, exhibiting strong robustness against signal-to-noise ratio (SNR) and polarization modulation errors. In particular, it maintains a polarization similarity coefficient (PSC) above 0.985 even at an SNR as low as $-$ 20dB.

Linear frequency modulation (LFM) waveforms have been widely adopted due to their excellent performance characteristics, such as good Doppler tolerance and ease of physical implementation. However, LFM waveforms suffer from high autocorrelation sidelobes (ACSLs) and limited design flexibility. Phase-coded frequency modulation (PCFM) waveforms can be used to design waveforms similar to LFM, offering greater design flexibility to optimize ACSLs. However, it has been found that the initial PCFM waveform experiences spectral expansion during the ACSL optimization process, which reduces its similarity to LFM. Therefore, this article jointly optimizes the ACSLs and spectrum of the initial PCFM waveform, establishes an optimized mathematical model, and then solves it using the heavy-ball gradient descent algorithm. Numerical experiments indicate that the proposed method effectively addresses the problem of waveform similarity degradation caused by spectral expansion while reducing waveform ACSLs. At the same time, a balance between reducing waveform ACSLs and preserving waveform similarity can be achieved by adjusting the parameters.