Search

X-band dual-polarization phased array radar (XPAR-D) possesses high resolution and plays a significant role in detecting meso- and micro-scale convective systems. However, the precipitation attenuation it endures necessitates an effective correction method. This study selected radar data from XPAR-D at the peak of Maofeng Mountain in Guangzhou during 16–17 May 2020 from three precipitation stages after quality control. Attenuation coefficients were calculated for different precipitation types through scattering simulations of raindrop size distribution (RSD) data. Next, an attenuation correction algorithm (MZH-KDP method) was proposed for the radar reflectivity factor (ZH) according to different raindrop types and compared to the ZH-KDP method currently in use. The results indicate that the attenuation amount of XPAR-D echoes depends on the attenuation path and echo intensity. When the attenuation path is shorter and the echo intensity is weaker, the amount of attenuation and correction is smaller. Difficulties arise when there are noticeable deviations, which are challenging to resolve using attenuation correction methods. Longer attenuation paths and stronger echoes highlight the advantages of the MZH-KDP method, while the ZH-KDP method tends to overcorrect the bias. The MZH-KDP method outperforms the ZH-KDP method for different precipitation types. The superior correction capability of the MZH-KDP method provides a significant advantage in improving the performance of XPAR-D for the detection of extreme weather. [ABSTRACT FROM AUTHOR]

Wind profiler radar (WPR) is used for all-weather atmospheric wind-field monitoring. However, the reliability of these observations reduces significantly when there is electromagnetic interference echo, generally caused by ground objects, birds, or rain. Therefore, to optimize the data reliability of WPR, we proposed a synthetic data quality control process. The process included the application of a minimum connection method, judgment rule, and median test optimization algorithm for optimizing clutter suppression, spectral peak symmetry detection, and radial speed, respectively. We collected the base data from a radiosonde and multiple radars and conducted an experiment using these data and algorithms. The results indicated that the quality control method: (1) had good adaptability to multiple WPRs both in clear sky and precipitation; (2) was useful for suppressing ground clutter and (3) was superior to those of the manufacturer as a whole. Thus, the data quality control method proposed in this study can improve the accuracy and reliability of WPR products and multiple types of WPR, even when they function under vastly different weather conditions. [ABSTRACT FROM AUTHOR]

The rainfall regime is changing in the Catalan territory, likely in most areas in the Mediterranean Basin. This variability, spatial and temporal, means that there may be periods of severe drought combined with periods of heavy rainfall and floods. In this way, the management of water resources is complicated and can produce a high impact on different social aspects. The high convective activity leads to investigating the relationship between the electric discharges and radar parameters (reflectivity, echo top, vertically integrated liquid, and accumulated rainfall). The correlation allows identifying some elements that may be significant in terms of changes in rainfall regimes. Besides, using several radar parameters apart from precipitation accumulation reveals interesting explicit patterns of the previously known. These patterns can help better understand the precipitation behavior and the changes associated with it. [ABSTRACT FROM AUTHOR]

In this paper, a dual-polarization weather radar echo signal simulation method is proposed for the evaluation of the performance enhancement of dual-polarization weather radar systems, the optimization of signal processing algorithms and the improvement of scanning strategies. The actual weather radar base data are used in the simulation as the reference weather scene, which avoids using a complex algorithm for weather modeling. Moreover, based on radar weather equations, the radar system parameters are added into the radar echo signal modeling to establish the relationship between the simulated echo signal and radar system. As a result, the final simulated echo signal not only shows both the time and frequency domain characteristics of the weather target, but also includes the effects of the important performance of the dual-polarization weather radar system. In addition, to evaluate the performance of range ambiguity mitigation using phase coding and batch working modes, two different simulation methods for the radar signal are established on the method above; one is for batch working mode with long-PRT (pulse repetition time) and short-PRT transmission and receiving, and the other is for phase-coded mode with phase-coded transmission and phase-uncoded receiving. Under the same weather scene, the observation of these two different methods of range ambiguity mitigation are simulated and compared. Results show that the performance of the phase coding mode for mitigating range ambiguity is better than that of the batch mode. Obviously, the simulation method can be used to directly show the observation of different algorithms for mitigation range ambiguity under the same weather process, and quickly compare and evaluate the algorithm's performance, which is not possible for real radars. [ABSTRACT FROM AUTHOR]

The echo of weather radar is seriously disturbed by clear-air turbulence echo (CAT) which needs identifying and eliminating to improve the data quality of weather radar. Using the data observed with the five X-band dual polarimetric radars in Changping, Fangshan, Miyun, Shunyi, and Tongzhou, Beijing in 2018, the probability density distribution (PDD) of the horizontal texture of four radar moments reflectively factor (ZH), differential reflectivity (ZDR), correlation coefficient (ρHV), differential propagation phase shift (ΦDP), and then the CAT is identified and removed using Bayesian method. The results show that the radar data can be effectively improved after the CAT has been eliminated, which include: (1) the removal rate of CAT is more than 98.2% in the analyzed cases. (2) In the area with high-frequency distribution of CAT, the CAT can be effectively suppressed; in the area with low-frequency distribution, some weather echo in the edge with SNR < 15 dB may be mistakenly identified as CAT, but the proportion of meteorological echoes to the total echoes is more than 85%, which indicate that the error rate is very low and does not affect the radar operation. [ABSTRACT FROM AUTHOR]

The merging of a fast-moving bow echo with a convective cell of a hook-echo signature was studied by using polarimetric radar detections. Gusts with wind speeds near 35 m s−1 were recorded by the surface station, which caused significant damage. A convective cell with a mesovortex signature, which is hereafter referred to as a mini-supercell, was observed over the northeast of the bow echo before the convective merging. It was found that the mesovortex possessed cyclonic circulation and resembled a supercell-like feature. The merging of the bow echo and the mini-supercell strengthened the updraft near the apex of the bow echo. The enhanced updraft was also demonstrated by the appearance of a differential reflectivity (ZDR) column with a topmost height of 4 km above the melting layer (~4 km). The bow was separated into northern and southern sectors after merging with the mini-supercell, leading to the gusty wind over the surface of the south sector. [ABSTRACT FROM AUTHOR]

In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directional convolutional long short-term memory. The model first constructs dimensions of input data and gets 3D tensor data with spatiotemporal features, extracts local short-term spatiotemporal features of radar echoes through 3D convolution networks, then utilizes constructed bi-directional convolutional LSTM to learn global long-term spatiotemporal feature dependencies, and finally realizes the forecast of echo image changes by forecasting network. This structure can capture the spatiotemporal correlation of radar echoes in continuous motion fully and realize more accurate forecast of moving trend of short-term radar echoes within a region. The samples of radar echo images recorded by Shenzhen and Hong Kong meteorological stations are used for experiments, the results show that the critical success index (CSI) of this proposed model for eight predicted echoes reaches 0.578 when the echo threshold is 10 dBZ, the false alarm ratio (FAR) is 20% lower than convolutional LSTM network (ConvLSTM), and the mean square error (MSE) is 16% lower than the real-time optical flow by variational method (ROVER), which outperforms the current state-of-the-art radar echo nowcasting methods. [ABSTRACT FROM AUTHOR]

Weather radar echo plays an important role in early warning and timely forecasting of severe weather. However, the radar echo may not be accurate enough to predict or analyze small-scale weather phenomenon due to the degradation of the observed radar. In order to solve this problem, some radar echo super-resolution reconstruction algorithms have been proposed, but the algorithm may result in an excessively smooth edge and detail in a local region. To reconstruct radar echo with better edges and finer details, a novel nonlocal self-similarity sparse representation (NSSR) model is proposed. The NSSR model is based on the sparse representation of weather radar echoes to better reconstruct the echo edge and detail information. We exploit the radar echo nonlocal self-similarity to recover more realistic details based on the NSSR model. Experiment results demonstrate that the proposed NSSR outperforms current general-purpose radar echo super-resolution approaches on both visual effects and objective radar echo quality. [ABSTRACT FROM AUTHOR]