Evolution of Wind Field in the Atmospheric Boundary Layer with using of Multiple Sources Observations during the Transit of Super Typhoon Doksuri (2305)

Abstract. The accurate wind field observation of tropical cyclone (TC) boundary layer is of great significance to improve the TC track and intensity forecasting. To investigate the vertical structure of TC boundary layer during the landfall process of Super Typhoon Doksuri, three kinds of instruments including the coherent Doppler lidar (CDL), radar wind profiler (RWP) and automatic weather station (AWS) are deployed at two sites in Xiamen, Fujian province. A data fusion method is developed to obtain the complete wind speed profiles covering the whole Atmospheric Boundary Layer (ABL) based on the above instruments. The wind speeds in the near field blind zones of CDL observation are interpolated by combining the AWS measurements at 10 m. The CDL provides high temporal-spatial resolution wind speed profiles from tens of meters to its highest detection height. The wind speeds above the highest detection height of the CDL would be supplemented with the RWP measurements. The hourly mean wind speed profiles are compared with traditional models. Generally, the wind speed profiles fit well with the power law in the lower part of the ABL, before wind speed changes rapidly. However, it would cause a large error (up to 73 %) to describe the exact wind speed profiles with traditional models during and after the typhoon’s passage, especially when the wind speed is almost constant with height or when wind shear exists. Then fine structures and evolutionary processes of the wind field in the ABL during the typhoon landfall are investigated. In addition, the wind field distribution and wind speed variation with distance from the typhoon center are statistical analyzed. The joint wind field measurements of CDL, RWP and AWS have the broad application prospects on the dynamics study of the TC boundary layer and the improvement of the boundary layer parameterization scheme in numerical forecast models.