Tornadoes are among the most destructive weather phenomena on Earth. Their small scale, short life cycle, and sudden onset make them extremely difficult to predict. This challenge is amplified for weak tornadoes embedded within mid-latitude westerly flows, which often hide in plain sight with faint signals. Improving the simulation capability for these events is vital for reducing disaster risk in highly vulnerable regions like the Pearl River Delta.
A new study led by Engineer Kaifeng Zhang of the Foshan Meteorological Bureau, under the guidance of Prof. Lingkun Ran from the Institute of Atmospheric Physics, Chinese Academy of Sciences, China, has achieved a significant breakthrough. Using a 37-meter ultra-high-resolution WRF model, the team assimilated data from the Foshan Nanhai X-band dual-polarization phased-array radar via a 3D-Var method to reconstruct a weak tornado event that struck Guangdong in June 2022. The research was recently published in Atmospheric and Oceanic Science Letters .
The study shows that X-band phased-array radar data greatly improves tornado simulations. Adding radar radial wind data (XPAR and VEL tests) strengthened the storm's spinning motion, helping the model capture the violent whirlwind. The XPAR test—using both radial wind and radar reflectivity data—predicted the tornado's path most accurately, matching the real track closely. Overall, XPAR performed best, followed by VEL (radial wind only), REF (reflectivity only), and CTRL (no radar data).
"Our experiment quantifies the value of phased-array radar in mesoscale meteorology," says Prof. Ran. "We demonstrated that assimilating radar wind data is essential for constructing a realistic low-level dynamical vortex in the model, while reflectivity data refines the microphysical environment. This synergy offers an exploratory pathway for improving the accuracy of operational tornado forecasting and early warning systems."
The team plans to expand this research to a broader range of tornado cases to validate the universality of this data assimilation strategy for regional numerical weather prediction.
