Accurate and reliable precipitation measurements play a key role in a wide disdrometriche e sviluppando un metodo di correzione. Le DSD corrette consentiranno inoltre di migliorare l’efficienza dei pluviometri tradizionali. Il risultato atteso è quantificare l’accuratezza delle misure e valutare gli effetti dell’uso di precipitazioni corrette su bias ambientali, modellistica idrica, stime radar e analisi climatiche, attraverso attività sperimentali e modellistiche.

English text:

Accurate and reliable precipitation measurements are essential for applications such as flash-flood forecasting, water resources management, and climate-change projections. However, in-situ measurements of liquid precipitation have undergone little innovation over the past decades. Rain gauges, the first instruments used to record accumulated rainfall over fixed time intervals, remain the most trusted inputs for hydrological models despite their known instrumental and environmental biases. A previous PRIN 2015 project showed, through numerical simulations and wind-tunnel experiments, that correcting gauge biases requires detailed information on rainfall microphysics, typically described by the drop size distribution (DSD) and the corresponding fall velocities.

Such information is provided by disdrometers, non-catching sensors capable of measuring the DSD and fall speed of hydrometeors. They are crucial for remote-sensing calibration, precipitation-type classification, and the assessment of rainfall-induced erosion. Yet, despite their potential, disdrometers are still mainly research instruments, partly due to limited knowledge of their accuracy and the absence of shared calibration standards.

This project aims to fill these gaps by evaluating wind effects on disdrometer measurements and developing an appropriate correction method. The corrected DSD will also allow improved adjustments for traditional catching gauges. The final goal is to quantify measurement accuracy and assess the benefits of corrected precipitation and DSD data for environmental bias estimation, water-resources modelling, radar-based rainfall retrievals, and climate-scale rainfall analyses, combining experimental and numerical approaches.