The Precipitation Radar (PR), the first space-borne precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, could observe three-dimensional precipitation in global tropical regions and acquire continuous rainfall information with moderate temporal and high spatial resolutions. TRMM PR had carried out 17 years of observations and ended collecting data in April, 2015. So far, comprehensive and abundant research results related to the application of PR data have been analyzed in the current literature, but overall precipitation features are not yet identified, a gap that this review intends to fill. Studies on comparisons with ground-based radars and rain gauges are first introduced to summarize the reliability of PR observations or estimates. Then, this paper focuses on general precipitation features abstracted from about 130 studies, from 2000 to 2018, regarding lightning analysis, latent heat retrieval, and rainfall observation by PR data. Finally, we describe the existing problems and limitations as well as the future prospects of the space-borne precipitation radar data.

Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12.

The Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission satellite has already provided reliable observations for more than 15 years. There is a potential possibility to assess long-term data of ground-based radar (GR) in the China new generation weather radar network through comparison between PR and GR. In this letter, stratiform precipitation above and below bright bands is used to implement reflectivity comparison for PR and the ground-based weather radar in Nanjing from 2003 to 2014. The results show that the accuracy of GR reflectivity is well correlated with the distance from GR, and there are several unusual years in which GR did not work properly. The comparison also indicates that bias of reflectivity between PR and GR is negative above bright bands but positive below bright bands, and the bias has high correlation with GR reflectivity. The proposed scheme provides a practical method for a sanity check of GR data.

Velocity volume processing (VVP) is a common wind retrieval method for single‐Doppler radar. However, hindered by the problem of the ill‐conditioned coefficient matrix, VVP method has not been applied effectively. In this paper, a support vector machine‐based VVP (SVM VVP) method is proposed to improve the VVP method. SVM VVP retrieves wind field through constructing a support vector regression machine for the radial velocity. The new method is applied to a real case, and the retrieval shows that SVM VVP can give accurate and high‐resolution results. It is helpful for studying wind fields of small‐scale precipitation systems.

A new storm identification and warning technique is proposed which exclusively uses radar data as input. The new identification method assembles contiguous storm points to constitute 2D storm components and improve the vertical association of storm components to construct 3D storms, which can overcome the deficiencies existing in traditional identification methods. Based on the evolution properties and characteristic distributions, strong storms and general storms are specified to train support vector machines (SVMs) which then can be used to discriminate storms. The performance of the SVM shows that it can indicate the intensity and development of a storm, providing an important aid in severe weather warning. Copyright © 2011 Royal Meteorological Society