In this study, we take a closer look at the important issue of μ–Λ relationships in raindrop size distributions (DSDs) by conducting a systematic analysis of 20 months of data collected by disdrometers in the Netherlands. A new power-law model for representing μ–Λ relationships based on the double normalization framework is proposed and used to derive separate μ–Λ relationships for stratiform and convective rain events. The sensitivity of the obtained relationships to measurement uncertainty is studied by applying two different quality control filters based on the mass-weighted mean drop diameter (Dm) and liquid water content (LWC). Our results show that there are significant differences in μ–Λ relationships between convective and stratiform rainfall types. However, the retrieved relationships appear to be quite robust to measurement noise and there is good agreement with other reference relations for similar climatological conditions.@en

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.@en

Raindrop size distributions (DSDs) play a crucial role in quantitative rainfall estimation using weather radar. Thanks to dual polarization capabilities, crucial information about the DSD in a given volume of air can be retrieved. One popular retrieval method assumes that the DSD can be modeled by a constrained gamma distribution in which the shape (μ) and rate (Λ) parameters are linked together by a deterministic relationship. In the literature, μ-Λ relationships are often taken for granted and applied without much critical discussion. In this study, we take another look at this important issue by conducting a detailed analysis of μ-Λ relations in stratiform rain and quantifying the accuracy of the associated DSD retrievals. Crucial aspects of our research include the sensitivity of μ-Λ relations to the temporal aggregation scale, drop concentration, inter-event variability, and adequacy of the gamma distribution model. Our results show that μ-Λ relationships in stratiform rain are surprisingly robust to the choice of the sampling resolution, sample size, and adequacy of the gamma model. Overall, the retrieved DSDs are in a rather decent agreement with ground observations (correlation coefficient of 0.57 and 0.74 for μ and Dm). The main sources of errors and uncertainty during the retrievals are calibration offsets in reflectivity (Zhh) and differential reflectivity (Zdr). Measurement noise and differences in scale between radars and disdrometers also play a minor role. The raindrop concentration (NT) remains the most difficult parameter to retrieve, which can be off by several orders of magnitude. After careful data filtering and removal of problematic Zhh/Zdr pairs, the correlation coefficient for the retrieved NT values remained low, only slightly increasing from 0.12 into 0.24.

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The adequacy of the gamma model to describe the variability of raindrop size distributions (DSD) is studied using observations from an optical disdrometer. Model adequacy is checked using a combination of Kolmogorov–Smirnov goodness-of-fit test and Kullback–Leibler divergence and the sensitivity of the results to the sampling resolution is inves-tigated. A new adaptive DSD sampling technique capable of determining the highest possible temporal sampling resolution at which the gamma model provides an adequate representation of sampled DSDs is proposed. The results show that most DSDs at 30 s are not strictly distributed according to a gamma model, while at the same time they are not far away from it either. According to the adaptive DSD sampling algorithm, the gamma model proves to be an adequate choice for the majority (85.81%) of the DSD spectra at resolutions up to 300 s. At the same time, it also reveals a considerable number of DSD spectra (5.55%) that do not follow a gamma distribution at any resolution (up to 1800 s). These are attributed to transitional periods during which the DSD is not stationary and exhibits a bimodal shape that cannot be modeled by a gamma distribution. The proposed resampling procedure is capable of automatically identifying and flagging these periods, providing new valuable quality control mechanisms for DSD retrievals in disdrometers and weather radars.

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