Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.@en

The use of spectral polarimetric filters in the range-Doppler domain shows great promise for clutter mitigation in weather radar applications. One limitation of these filters is that they cannot deal with situations in which ground clutter and precipitation overlap. In this letter, we propose a new signal recovery technique based on kriging in the spectral domain to recover the precipitation in clutter-contaminated areas. Using synthetic radar data, we test our new method and compare its performance to that of Gaussian model adaptive processing and bilinear interpolation. Our results indicate that kriging is the most accurate and robust technique out of the three.@en

Weather radar is well recognized as an effective sensor for obtaining the microphysical and dynamical properties of precipitation at high spatial and temporal resolution. Radar calibration is one of the most important prerequisites for achieving accurate observations. In this article, a portable, cost-effective and repeatable radar calibration technique, namely, unmanned aerial vehicle (UAV)-aided radar calibration, is proposed. A UAV serves as the stable aerial platform carrying a metal sphere, flying over the radar illumination areas to complete the calibration process. The flying routine of the UAV can be pre-programmed, and thus, the antenna pattern regarding different elevation and azimuth angles can be retrieved. To obtain the position of the sphere, the real-time single-frequency precise point positioning-type global navigation satellite system solution is developed. In addition, the radar constant is calculated in the range-Doppler domain, and only the data where the metal sphere separates from clutter and other objects are selected. The S-band polarimetric Doppler transportable atmospheric radar (TARA) is used in the calibration campaign. The experiments demonstrate the following results: 1) antenna pointing calibration can be completed and 2) antenna pattern can be retrieved and weather radar constant can be accurately calculated.@en