The raindrop size distribution (DSD) is a statistical description of the number and
size distribution of raindrops within a specified volume of air. DSDs play a central
role in radar remote sensing and are essential for understanding the scattering and
absorption of electromagnetic radiation as it travels through the air and interacts with
falling rain. They form the mathematical backbone for linking radar observations
to physical quantities such as rainfall intensities, liquid water content, and kinetic
energy. Yet, in most remote sensing applications, the DSD in the target volume is
likely to be unknown. Therefore, parameterized DSD models have been created to act
as substitutes for direct measurementswhen observations are unavailable, incomplete,
or impractical. Over time, a large number of models have been proposed, each with
their own intricacies and constraints. Understanding these mathematical constructs
and the assumptions behind them is essential for interpreting radar measurements and
improving quantitative rainfall estimation.@en

Many applications in urban areas require high-resolution rainfall measurements. Typical operational weather radars can provide rainfall intensities at 1-km ² grid cells every 5 min. Opportunistic sensing with commercial microwave links yields path-averaged rainfall intensities (typically 0.1–10 km) within urban areas. Additionally, large amounts of urban in situ rainfall measurements from amateur weather observers are obtainable in real-time. The accuracy of these three techniques is evaluated for an urban study area of 20 × 20 km, taking into account their respective network layouts and sampling characteristics. We use two simulated rainfall events described in terms of drop size distributions on a 100-m grid and with a temporal resolution of 30 s. Accurate radar rainfall estimation with the Z-R relationship relies heavily on an appropriate choice of parameters, and a dual-polarization strategy is more suitable for higher intensities. Under ideal measurement conditions, the weather station network is the most promising, with a Pearson correlation coefficient above 0.86 and a relative bias below 4% for 100-m rainfall estimates at 5-min resolution. Microwave link rainfall observations contain the largest error, shown by a consistently larger coefficient of variation. The accuracy of all techniques improves when considering rainfall at larger scales, especially by increasing time intervals, with the strongest improvements found for microwave links for which errors are largely caused by their temporal sampling. Sparser networks are examined, showing that the decline in measurement accuracy only becomes significant when the link and station network density are reduced to less than half their levels in Amsterdam.

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In the last decade there has been a growing interest from the hydrometeorological community regarding rainfall estimation from commercial microwave link (CML) networks. Path-averaged rainfall intensities can be retrieved from the signal attenuation between cell phone towers. Although this technique is still in development, it offers great opportunities for the retrieval of rainfall rates at high spatiotemporal resolutions very close to the Earth's surface. Rainfall measurements at high spatiotemporal resolutions are highly valued in urban hydrology, for instance, given the large impact that flash floods exert on society. Flash floods are triggered by intense rainfall events that develop over short time scales. Here, we present one of the first evaluations of this measurement technique for a humid subtropical climate. Rainfall estimation for subtropical climates is highly relevant, as many countries with few surface rainfall observations are located in such areas. The test bed of the current study is the Brazilian city of São Paulo. The open-source algorithm RAINLINK was applied to retrieve rainfall intensities from (power) attenuation measurements. The performance of RAINLINK estimates was evaluated for 145 of the 213 CMLs in the São Paulo metropolitan area for which we received data, for 81 days between October 2014 and January 2015. We evaluated the retrieved rainfall intensities and accumulations from CMLs against those from a dense automatic gauge network. Results were found to be promising and encouraging when it comes to capturing the city-average rainfall dynamics. Mixed results were obtained for individual CML estimates, which may be related to erroneous metadata.

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Flash flooding is a potentially destructive natural hazard known to occur in the Ce´vennes-Vivarais region in southern France. HyMeX (Hydrological Cycle in the Mediterranean Experiment) is an international program focused on understanding the hydrological cycle in the Mediterranean basin. Soil moisture is known to be a useful indicator of catchment response, however, establishing a meaningful estimation of soil moisture at the catchment level can be difficult due to its high variability in space and time.In a small gauged catchment in the Ce´vennes-Vivarais region in southern France, a series of manual soil moisture measurements was taken from September to December 2012 at both the field and catchment scale during the Special Observation Period 1 (SOP1) as part of the HyMeX program. Six plots were selected along a trajectory of a microwave link installed in the catchment and were chosen to represent different elevations in the catchment. Within each field plot, surface soil moisture was measured along a 50. m transect at 2. m intervals. This allowed the study of changes in within-field variability as well as between-field variability in response to precipitation events and during the drying out phase.Several precipitation events occurred over this autumn 2012 period which caused a significant wetting-up of the catchment, allowing the study of soil moisture processes over a wide range of wetness conditions. The influence of antecedent catchment conditions (soil moisture) on rainfall-runoff dynamics is demonstrated through the comparison of storm hydrographs for the various events. Dry catchment conditions result in minimal response in event flow, whereas large precipitation events occurring during wetter conditions produce much stronger responses in event flow. This further confirms the importance of quantifying catchment initial conditions to enhance the prediction of flash flood occurrences.

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