The Impact of Assimilating High and Low-Level X-Band Phased Array Radar Data on Tropical Cyclone Forecasting

This study conducts numerical experiments on tropical cyclone Saola (2023) using the Typhoon Rapid Refresh Analysis and Nowcasting System developed by the Chinese Academy of Meteorological Sciences, assimilating radial wind velocity data from S-band Doppler Radar and X-band Phased Array Radar with the Ensemble Kalman Filter method. The assimilation effects on Saola's track and intensity are evaluated, with particular focus on the assimilation sensitivity of X-band Phased Array Radar data at different altitudes. Results indicate that compared with solely assimilating S-band Doppler Data assimilating X-band Phased-Array Radar data in addition to S-band Doppler Radar data further reduces track error and intensity error by 13.7% and 58.0%, respectively. X-band Phased Array Radar data above 4 km primarily influences the geopotential height, horizontal wind, and warm-core intensity above 600 hPa at the initial time though dynamic and thermodynamic processes, although this effect diminishes progressively during forecast integration. X-band phased array radar data below 4 km mainly affect geopotential height and horizontal wind below 700 hPa through dynamic processes, directly strengthening the mid-to-lower-level pressure and wind fields. Moreover, after 3 hours of the deterministic forecast initiation, the entire wind and pressure fields are influenced. This study finds that assimilating X-band Phased Array Radar data below 4 km provides equivalent forecast skill of tropical cyclone's track, intensity, and structure compared to assimilating X-band Phased Array Radar data at all altitudes. Furthermore, it verifies the effectiveness of X-band Phased-Array Radar in complementing the low-level coverage of S-band Doppler Radar in tropical cyclone data assimilation and forecasting, offering actionable insights for refining operational tropical cyclone forecasting.