Accurate and refined nowcasting of short-term intense precipitation can be utilized to provide timely warnings and mitigate the damage caused by hydrological and meteorological disasters. Radar echo extrapolation can provide highly efficient forecasts of precipitation with high spatial and temporal resolution during the first 3 h and has been widely used in operational nowcasting systems in aviation, meteorology, hydrology, and other fields. However, these extrapolation methods still have drawbacks and need to be improved to provide more accurate nowcasting. In this article, an improved nowcasting method, based on the continuity of tracking of radar echo with correlations (COTREC), named ensembled COTREC (EnCOTREC), was proposed. A comparison was then made between the COTREC method and the EnCOTREC method for several types of precipitation processes. By comparing the EnCOTREC method and the COTREC method in squall line (SL), convective and stratiform precipitation processes, the results show that the EnCOTREC method is better at describing precipitation motion than the COTREC method and thus performs better in precipitation nowcasting. Especially for processes with a large precipitation area and uniform precipitation intensity, the accuracy improvement of EnCOTREC is more pronounced. Through time series analysis, the results of the EnCOTREC method are more stable than those of the COTREC method, and the EnCOTREC method can perform better than COTREC, with an overall improvement of 5%.

China New Generation Weather Radar (CINRAD) operated in volume coverage pattern 21 (VCP21) mode may miss important information about precipitation for storms that evolve rapidly and move fast due to its volume scanning interval (about 6 min). 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 the BIO method can significantly improve the accuracy of radar QPE, namely the rainfall intensity and pattern.

Radar quantitative precipitation estimation (QPE) plays an important role in precipitation forecasting and flood early warning. Due to the vertical variability of precipitation, accurate radar QPE remains an ongoing challenge as the radar observations at high altitudes are typically used to estimate surface precipitation. The common underestimation error for convective precipitation has been recognized, but effective solutions for operational use are still lacking. In this study, a real-time vertical profile of reflectivity (VPR) correction algorithm is proposed to address this issue. The long-term observations of convective precipitation vertical structure over 24 years provided by space-borne radars (SRs), that is, the tropical rainfall measuring mission precipitation radar (TRMM-PR) and the global precipitation measurement mission dual-frequency precipitation radar (GPM-DPR), are used to generate climatological convective VPRs. The vertical structure variability of convective precipitation due to local precipitation characteristics, precipitation intensity, and environmental freezing level height is taken into account. In addition, the differences in frequency and sampling strategy between spaceborne radars and ground-based radars (GRs) are considered in the construction of the climatological convective VPRs, which can be used to correct radar QPE errors in real time. The VPR correction algorithm is evaluated using 28 typical convective precipitation events in China. The validation results show that the systematic underestimation error of the radar QPE for convective precipitation can be effectively reduced after the VPR correction. The relative mean bias (RMB), which is typically less than –0.3, is increased to between –0.2 and 0, and the root mean square error (RMSE) is significantly reduced.

Bright band (BB) is a layer of enhanced radar reflectivity because of hydrometeor melt and coalescence, which could cause significant overestimation of radar quantitative precipitation estimation (QPE). Overestimation of QPE due to BB contamination may lead to false flood alarms. Vertical profile of reflectivity (VPR) correction is an effective means to reduce the overestimation of radar QPE caused by BB contamination. This study presents an improved BB apparent VPR correction algorithm (named IAVPR) using radar multi-tilts of reflectivity observations. The reflectivity observations in the same vertical layer are averaged, and the beam broadening effect is reduced. This optimization helps to correctly identify the BB top from the IAVPR, which further improves the BB bottom identification according to the symmetry of the BB vertical structure. The IAVPR algorithm improves upon an old AVPR algorithm that constructs a range profile of azimuthally averaged reflectivity observations in the BB-affected area for each tilt and then apply correction to the specific tilt based on the AVPR. The performance of the AVPR algorithm can be greatly affected by the spatial pattern of precipitation or terrain, where the IAVPR algorithm can consistently correct for BB contamination. A comprehensive evaluation is conducted using 13 precipitation events observed by six China’s operational radars in different regions. The result shows that the IAVPR algorithm is more effective and robust for BB correction compared to the AVPR algorithm.

Particle size distribution (PSD) characteristics of mature squall lines are investigated through global precipitation measurement (GPM) dual-frequency precipitation radar (DPR) measurements. These squall lines consist of a leading convective (LC) line, a weak-echo transition (WT) region and a trailing stratiform (TS) region. Their PSD characteristics are quite different from the existing conceptual models of mature squall line, given that many small raindrops/ice particles are found in the WT region while in the TS region raindrops/ice particles are sparse. Analysis shows that it is likely due to the short distance from LC to WT, where more particles may be dispersed from LC region and fall into WT region but barely have time to grow in size. In the TS region further behind LC, the particles have more time to get larger. Analysis also reveals that the mesoscale updraft generally occurs at mid-to-high levels in the TS region so that aggregations and collisions-coalescences could be promoted to increase the particle size but decrease the particle number. Through the GPM PSD data analysis, a refined conceptual model of a mesoscale convective system (MCS) with squall line is presented in this study.

Bright band (BB) is a layer of enhanced radar reflectivity due to hydrometeor melting and coalescence. BB identification is of high importance on radar quantitative precipitation estimation (QPE) and other applications. The Dual-frequency Precipitation Radar onboard the core satellite of the Global Precipitation Measurement mission (GPM-DPR) enables new investigations of BB characteristics at a global scale. However, the GPM-DPR operational BB identification algorithm based on the vertical profile of reflectivity (VPR) from single-frequency (SF; Ku band) or dual-frequency (DF; Ku and Ka band) observations still has the room for improvement. In the current study, an improved GPM-DPR SF BB identification algorithm is presented based on the detection of inflection points within a given range in a VPR. The improved GPM-DPR SF BB identification algorithm decreases the overestimation (underestimation) error of the BB bottom (top) height identified by the GPM-DPR SF algorithm, from 322 m (-345 m) to 182 m (-211 m), compared against the identifications by the GPM-DPR DF algorithm. The GPM-DPR SF will mis-estimate the depth of the melting layer, and the reflectivity difference between BB bottom (top) and BB peak to about 1.5 (3.5) dB, which will lead to mis-understand the vertical physical variation of the precipitation particles. The validation through ρHVderived from WSR-88D observations in the CONUS demonstrates that the GPM-DPR DF algorithm is of higher accuracy, and the improved GPM-DPR SF algorithm performs better than the GPM-DPR SF algorithm. The new algorithm will contribute to hydrometeor phase classification and the studies on BB characteristics.

The ground-based radar quantitative precipitation estimation (QPE) faces various challenges including the overestimation caused by the bright band (BB) in the stratiform region and the underestimation in mountainous areas when the terrain-enhanced precipitation occurs at the levels below ground-based radar measurements. The vertical precipitation structure provided by spaceborne radars, i.e., the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement mission (GPM) Dual-frequency PR (DPR), is valuable for mitigating the above problems. Since the spaceborne radars and ground-based radars usually operate in different frequencies, e.g., the TRMM PR and the KuPR of GPM DPR work in Ku-band (13.8 and 13.6 GHz, respectively) and the ground-based radars in western China work in C-band (5.4 GHz), the reflectivity conversion from Ku-band to C-band is necessary before the vertical profile of reflectivity (VPR) measured by spaceborne radars can be utilized to improve the ground-based radar QPE in western China. This study presents a conversion method using GPM DPR measurements, i.e., the drop size distribution (DSD) for different precipitation types (the stratiform with/without BB and the convective cases) and particle phases (the solid, melting, and liquid). Using the${T}$-matrix method, the reflectivity difference between Ku-band and C-band is found and the Ku-band to C-band conversion relations are derived with the linear regression. These conversion relations have been validated by matching and comparing the converted C-band reflectivity with the C-band ground-based radar measurements. The results demonstrate the effectiveness and reliability of the conversion. This method can be extended for the reflectivity conversion in other frequencies and can facilitate the incorporation of reflectivity measurements from various instruments.