近地面雨滴粒徑分布(Drop Size Distribution, DSD)的量測，對於了解臺灣近地面降水與雲物理特性、改進雷達定量降水估計(Quantitative Precipitation Estimation, QPE)扮演著重要的角色，然而在進行DSD分析及降雨參數估算前，必須確保雨滴譜儀之觀測資料品質。本研究利用中央氣象局27個測站的一維雷射式雨滴譜儀(Parsivel²)觀測資料，建立適用臺灣區域的雨滴譜儀資料品管(Quality control, QC)流程。並針對不同環境條件(風向、風速、濕度及地形等)及作業上的限制，分析探討造成雨滴譜儀資料品質不佳可能原因，期望有效提升雨滴譜儀網資料品質。分析結果顯示，Parsivel²資料經過QC後能有效濾除不合理資料。統計比對2016至2018年Parsivel²雨滴譜儀與傾斗式雨量計觀測資料，發現Parsivel²雨滴譜儀的雨量普遍低估，尤其是山區及離島測站。分析不同環境條件之影響，顯示強風速對Parsivel²觀測品質影響較為顯著。未來將進一步結合雙偏極化雷達觀測資料，期可提升臺灣雷達QPE的準確度，以提供後續研究及其相關應用。

Drop size distribution (DSD) is widely used to improve radar quantitative precipitation estimation (QPE) and to understand the precipitation microphysics. However, ensuring a useful data quality is important before disdrometer data is applied. In this study, 27 operational disdrometer (Parsivel²) data from CWB (Central Weather Bureau) are chosen to develop an applicable quality control (QC) process in Taiwan. In addition, data quality is investigated under different conditions, including wind speed, wind direction, humidity, terrain and limitation of real-time data transmission. The result indicates that unreasonable data can be removed by the QC procedure. Based on the comparison between Parsivel² and tipping-bucket rain gauge (TBRG) rainfall from 2016 to 2018, Parsivel² has an underestimation in the most of 27 stations, especially in the mountain area and over the islands. Also, strong wind speed has a significant impact on the quality of Parsivel² data. In the future, the dual-polarimetric radar parameter retrievals from DSDs will be applied to improve radar QPE in Taiwan.