The wind effects on disdrometer and rain gauges measurements: results from a 4-year long rain series data-set in Pescara and a 10-year long rain series data-set in Calabria (Italy)

Disdrometers are in-situ, non-catching devices capable of measuring the size and fall velocity (for most models) of each individual hydrometeor (solid or liquid) that enters their measurement volume. These devices are primarily used for research purposes, and their data have applications in fields such as meteorology, climatology, and hydrology. However, their measurements can be influenced by the presence of wind. In this context, one of the objectives of the PRIN project titled “Fostering innovation in precipitation measurements: from drop size to hydrological and climatic scales” is to quantify the accuracy of disdrometers. In this regard, data collected from a Thies Clima disdrometer and wind sensors installed in the city of Pescara serve as a valuable resource for: i) characterizing precipitation, ii) conducting a joint analysis of atmospheric conditions, including wind directionand speed, and iii) evaluating the effect of wind on disdrometer measurements. The dataset covers the period from July 2021 to August 2024, although it includes significant interruptions. This study presents the main characteristics of the site in terms of wind and rain distributions, as well as their joint distributions. Additionally, the effects of wind on disdrometer measurements are quantified in terms of the associated bias on on DSD (Drop Size Distribution) estimation. Results indicate that wind-corrected DSDs differ, on average, by 136.41m−3 ·mm−1 in terms of root mean square error compared to uncorrected DSDs. Subsequently, since we do not have a DSD from the rain gauge, we hypothesize that it has the form of an exponential αeβ, and we interpolate these parameters from the disdrometer data. Then this parametrs are used to apply corrections to nearby rain gauge measurements, and the corrected and uncorrected values are compared. These differences are found to be statistically significant. Furthermore, twenty-six stations in Calabria, equipped with rain gauges and anemometers, are analyzed using the same DSD parameters derived from the Pescara dataset. Precipitation amounts obtained from corrected and uncorrected DSDs are compared with corresponding corrected and uncorrected rain gauge data, revealing statistically significant differences. These findings provide insight into the effects of the applied correction on rain rate measurements.