The Shanghai PAWR network has shown advantages in detecting the structure, evolution, and dynamics of local severe storms, MCSs, and tropical cyclone rain bands in better detail than the operational S-band radars (WSR-88D). A mini-supercell with a tornado was detected by using phased array radar, while the mini-supercell could not be identified by the Shanghai S-band radar. The positive and negative radial velocity pair structure and evolution process revealed that the vortex originated near the ground and then reached up to 2.0 km. Only 2-3 minutes before the tornado touched down, the contracted and stretched tornado vortex dropped rapidly from as high as 1.5-2.0 km to near surface.
Some new algorithms have been developed using its high spatial and temporal data and 3-D retrieved wind data:
1) Wind gust index: combines the descent of storm’s center of gravity and the retrieved 3-D Doppler wind field to obtain the area and intensity of the ground gust, which is especially useful for real-time monitoring and early warning of damaging wind with thunderstorms.
2) Hail warning index: uses three-dimensional reflectivity and dual polarization information (Zdr column and HCA product), combined with wind field dynamic conditions (e.g. strong convergence and updraft below freezing level), to estimate the probability of hail occurrence, which can effectively improve the lead time of hail warnings.
3) Convective development index: uses the low-level convergence/divergence from the retrieved 3-D Doppler wind field to establish a new predictor of intensity change for convective systems. This index can provide information about the potential for convective systems to maintain or increase strength in the near future.
4) Micro-scale vortex detection: uses the high temporal and spatial resolution data of the phased array radar network to identify micro-scale vortex and its diameter and to calculate the azimuth shear and vorticity. This product can characterize rotational properties and strength of micro-scale systems (<2km), especially in the low levels, which is very helpful to predict the possibility of a tornado in advance.
The PWAR network and its algorithms have provided an opportunity to increase nowcasting lead time for extreme winds, heavy rain, and large hail in Shanghai.

