资源环境科技发展态势分析平台

Particle Size and Velocity (PARSIVEL) disdrometers have been widely applied in studies of the precipitation microphysics of tropical cyclones (TCs). However, few studies have focused on the accuracy of PARSIVEL data in the strong-wind environments induced by TCs. This work examines the performance of PARSIVEL under strong horizontal-wind conditions using data collected from four typhoon field experiments in China during 2014–2018. Observational data from a PARSIVEL disdrometer is compared to a collocated automatic weather station (AWS) onboard a mobile observation vehicle. It is shown that the integral rain rate derived from PARSIVEL is smaller than the AWS data, and the underestimation increases gradually with wind speed. This bias results partly from measurement errors of raindrop diameter and fall velocity due to the drifting and tilting of raindrops as they fall. The bias is particularly prominent in the case of large raindrops (>4 mm) in strong horizontal wind. To account for the canting and incidence angles of raindrops entering the sampling area, a wind-adjusted quality control (QC) method is developed by adding a correction module that complements existing QC methods. The validation of the corrected disdrometer data against AWS data demonstrates that this modified QC method presents a remarkable improvement for the wind-dependent underestimation of rain rates from PARSIVEL. For instance, when the wind speed exceeds 14 m s⁻¹, the mean error in rain rate derived from PARSIVEL for 5 min of rainfall with (without) the complementary correction module in QC procedures is −11.8% (−22.4%), in comparison with the AWS data.