Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km² every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.

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The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dualpolarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values.@en

Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors.@en

A new model-based iterative technique to correct for attenuation and differential attenuation and retrieve rain rate, based on a neural-network scheme and a differential phase constraint, is presented. Numerical simulations are used to investigate the efficiency and accuracy of this approach named NIPPER. The simulator is based on a T-matrix solution technique, while precipitation is characterized with respect to shape, raindrop size distribution and orientation. A sensitivity analysis is performed in order to evaluate the expected errors of this method. The performance of the proposed methodology on radar measurements is evaluated by using one-dimensional Gaussian shaped rain cell models and synthetic radar data derived from disdrometer measurements. Numerical results are discussed in order to evaluate the robustness of the proposed technique.

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