Zhe Zhang

Quantitative precipitation estimation (QPE) is one of the primary applications of weather radar. Over the last several decades, dual-polarization radars have significantly improved QPE accuracy by providing additional observational variables that offer more microphysical information about precipitation particles. In this work, we review QPE methods for dual-polarization radars and summarize their advantages and disadvantages from both theoretical and practical perspectives. The development paths and current status of operational QPE systems in the United States, China, and France are examined. We demonstrate how dual-polarization radars have improved QPE accuracy in these systems not only directly through the application of polarimetric QPE methods, but also indirectly through the more accurate identification of non-meteorological echoes, the mitigation of the partial blockage effect, and the detection of melting layers. The challenges are discussed for dual-polarization radar QPE, including the quality of polarimetric variables, QPE quality in complex terrain, estimation of surface precipitation with observations within or above the melting layer, and polarimetric QPE methods for snow.

In 2019, the World Meteorological Organization (WMO) pointed out the following based on the statistics from 2007 to 2019: in natural disasters, 90% of losses are related to meteorology, of which heavy storms and floods account for more than 70%

China New Generation Weather Radar (CINRAD) operated in volume coverage pattern 21 (VCP21) mode may miss important information about precipitation for storms that evolving rapidly and moving fast due to its volume scanning interval (about 6 minutes). However, despite the rapid changes and fast movement of storms, according to the continuity equation, the pattern and location of storms are continuous in temporal and spatial. An attempt is made in this research to use interpolation to supplement the missing information about storm evolution and improve the radar quantitative precipitation estimation (QPE) quality. Three interpolation methods are used to supplement storm evolution information between scan intervals for two fast-moving storm events. In these events, weather radar using VCP-21 mode cannot capture storm evolution well. The results show that all of the three interpolation methods do enhance the accuracy of the radar QPE. However, the linear interpolation method only describes the linear variation of storms, and the optical flow method can consider the movement of the storm and has a significant improvement in capturing the rainfall area, yet it cannot delineate the variation in rainfall shape and intensity. The Bi-directional optical flow (BIO) can describe not only the storm movement, but also the change of intensity and pattern of storms. BIO can greatly enhance the accuracy of radar QPE among the three methods. This study suggests that the interpolation of fast-moving storms between two neighboring radar volume scans needs to take into account both intensity variation and precipitation movement. The application of BIO method can significantly improve the accuracy of radar QPE, namely the rainfall intensity and pattern.

Understanding the characteristics of the raindrop size distribution (DSD) is crucial to improve our knowledge of the microphysical processes of precipitation and to improve the accuracy of radar quantitative precipitation estimation (QPE). In this study, the spatial variability of DSD in different regions of Beijing and its influence on radar QPE are analyzed using 11 disdrometers. The DSD data are categorized into three regions: Urban, suburban, and mountainous according to their locations. The DSD exhibits evidently different characteristics in the urban, suburban, and mountain regions of Beijing. The average raindrop diameter is smaller in the urban region compared to the suburban region. The average rain rate and raindrop number concentration are lower in the mountainous region compared to both urban and suburban regions. The difference in DSD between urban and suburban regions is due to the difference in DSD for the same precipitation types, while the difference in DSD between mountain and plains (i.e., urban and suburban regions) is the combined effect of the convection/stratiform ratio and the difference of DSD for the same precipitation types. Three DSD-based polarimetric radar QPE estimators were retrieved and estimated. Among these three QPE estimators, R(Z_H), R(K_dp), and R(K_dp, Z_DR), R(K_dp, Z_DR) performs best, followed by R(K_dp), and R(Z_H) performs worst. R(K_dp) is more sensitive to the representative parameters, while R(Z_H) and R(K_dp, Z_DR) are more sensitive to observational error and systematic bias (i.e., calibration).