Abstract. This study focuses on the reliability assessment of precipitation data calculated from drop size distribution (DSD) based on disdrometer data observations according to wet-bulb temperature (Tw). Three distinct quality control (QC) methods based on fall velocity were implemented and validated against measurements from tipping-buckets and weighing rain gauges collected from January 2020 to February 2024. The analysis indicated that all QC methods exhibited high reliability (correlation coefficient (CC)>0.98) for rainfall conditions when Tw was above 5 °C, with a mean absolute percentage error (MAPE) of approximately 8.5 %. However, the precision of precipitation measurements exhibited a notable decline when Tw was below 2 °C, as indicated by a CC of less than 0.6 and MAPE exceeding 30 %. This reduction in accuracy can primarily be attributed to the outcomes of the QC methods, which rely on the falling velocity, given that raindrops and solid particles were observed within the specified Tw range. When considering the melting of snow particles at Tw ranging from 0 to 2 °C, the CC approached 0.9, suggesting enhanced measurement reliability. The findings of this study indicate that Tw is a more effective variable than air temperature (Tair) for differentiating the precipitation types. This conclusion arises from the observation that the fall velocity of hydrometeors does not reach the terminal velocity of raindrops, even within the Tair range of 1–5 °C, coupled with the broad distribution of fall velocities. The DSD shape demonstrated stability across multiple QC methods when Tw was equal to or greater than 2 °C. In contrast, considerable variations were observed at lower temperatures, where particles with diameters ranging from 1 to 2 mm exhibited irregular distribution patterns at temperatures below 1 °C. These results suggest that DSD parameters should be derived from disdrometer data obtained under conditions where Tw is above 2 °C to ensure the reliability of the findings. This study provides critical insights for improving precipitation measurement techniques and DSD analyses in regions with variable temperature conditions.
Kim et al. (Mon,) studied this question.