Variability of the raindrop size distribution: model and estimation uncertainties across different scales

Precipitation is a profoundly important meteorological process and a crucial component of the water cycle. Thus, the continuous and reliable monitoring of precipitation at global scale is fundamental for scientific sectors such as numerical weather prediction and hydrology. However, accurately estimating precipitation, its type and intensity at planetary scale remains a notoriously challenging task. While point measurements from rain gauges provide generally accurate direct rain observations, their lack of spatial coverage is a significant limitation. Therefore, global-scale precipitation monitoring heavily relies on remote sensing sensors, such as weather radars (ground-based or spaceborne). Radars are capable of indirectly measuring rainfall over extended domains but with a higher level of uncertainty. For accurate rainfall estimates from radar, the complex microphysical properties of rain must be known or inferred. The drop size distribution (DSD) plays a crucial role by offering valuable insights into the microphysical properties of precipitation and linking radar observations to physical quantities such as rainfall intensity. However, similar to rainfall, DSD exhibits significant variability in space and time. The objective of this PhD thesis is to better understand the small-scale variability of rainfall, contributing to the improvement of quantitative precipitation estimation. In this study various critical aspects around DSD which are often overlooked such as DSD measurements, modeling and retrievals across different scales are investigated.