Based on daily rainfall data from 26 station records, spatial and temporal variations in annual and seasonal precipitation of different rainfall intensities from 1961 to 2018 in Ningxia, China are investigated using the innovative trend analysis (ITA) method. The results show that annual precipitation increases on the northern plain but decreases in the southern mountainous area. The increase in regional annual precipitation is mainly due to an increase in weak precipitation, while the decrease in regional annual rainfall is a result of a reduction in heavy precipitation. Low-intensity precipitation shows an upward trend, while high-intensity precipitation shows a downward trend. The variation trend of extreme precipitation is more obvious. The contributions of different types of extreme precipitation vary by season. During spring, the increase in regional rainfall is mainly caused by the increase in heavy precipitation, while the decrease in regional precipitation is mainly caused by the decrease in weak precipitation. During summer and autumn, the increase in regional precipitation is caused by the increase in light precipitation, while the reduction in regional rainfall is caused by the decrease in heavy precipitation. This study provides support for water resources planning and addressing droughts and floods.

An inverse mapping table (IMT) method is proposed in this article to retrieve the raindrop size distribution (RSD) parameters from X-band polarimetric weather radar data. In the IMT method, a forward mapping database from three parameters of a gamma-type RSD to polarimetric radar variables is first built based on the scattering simulations under ideal atmospheric conditions, and then an inverse mapping database is derived. In particular, given a fixed shape parameter (μ) of RSD, the intersection of horizontal reflectivity (ZH) and differential reflectivity (ZDR) contour lines is first obtained in the domain of total number concentration (NT) and median volume diameter (MVD) D₀; and the inverse mapping relationship between ZH and ZDR to NT and D₀ at a fixed μ value is derived to form a single layer of IMT. Then, the monotonic relationship between μ and the specific differential propagation phase shift (KDP) or backscatter differential phase (δCO) can aid in determining μ and a single layer of IMT. Thus, the inverse mapping database from polarimetric observations to the three gamma-type RSD parameters μ, NT, and D₀ can be established. Demonstration studies during a convective rainfall event and a large-scale rainfall event which occurred in northeastern China are carried out to examine the performance of this IMT method compared to a constrained-gamma (C-G) method that uses empirical relations between RSD parameters. The results show that the IMT method has a better performance in the convective case and similar performance in the large-scale continuous rainfall case.

A uniformity index for the axis ratios (U_ar) derived from dual polarization weather radar data is proposed for raindrop area identification and analysis. The derivation of this new parameter is based on radar scattering simulations and assumptions. U_ar is between 0 and 1 and can be calculated from the differential reflectivity (Z_DR) and the copolar correlation coefficient (ρ_hv), which reflects the uniformity of the axis ratio (r) of the particle group. For raindrops, U_ar is close to 1 under ideal conditions, but is clearly different from that of ice particles whose value is close to 0. Studies conducted during two convective weather events observed by X-band and S-band radar are presented to show the U_ar features. In convective areas, high U_ar presents a U-shaped vertical structure. One branch corresponds to the Z_DR column, while the other branch is located at the rear of the convective cloud zone and is lower in altitude, representing the process of ice particles melting into raindrops and then being transported upward by a strong updraft. In stratiform cloud areas, a more than 95% overall identification ratio is obtained when the threshold of U_ar is set to 0.2~0.3 for discriminating rain layers.

A nonlinear grid transformation (NGT) method is proposed for weather radar convective echo extrapolation prediction. The change in continuous echo images is regarded as a nonlinear transformation process of the grid. This process can be reproduced by defining and solving a 2 × 6 transformation matrix, and this approach can be applied to image prediction. In ideal experiments with numerical and path changes of the target, NGT produces a prediction result closer to the target than does a conventional optical flow (OF) method. In the presence of convection lines in real cases, NGT is superior to OF: the critical success index (CSI) for 40 dBZ of the echo prediction at 60 min is approximately 0.2 higher. This is due to the better estimation of the movement of the whole cloud system in the NGT results since it reflects the continuous change in the historical images. For the case with a mesoscale convective complex, the NGT results are better than the OF results, and a deep learning result is cited from a previous study for the same case for 20 and 30 dBZ. However, the result is the opposite for 40 dBZ, where the deep learning method may produce an overestimation of the stronger echo.