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Abstract: Artificial rainfall is widely used to study the surface runoff process but several problems are related to the reproducibility of natural rainstorms. A new rainfall simulator and a collection system were designed and tested in the laboratory and in the field. The rainfall simulator consists of four independent lines of low-cost pressure washing nozzles operated at a pressure of 80 mbar which number and position causes the rainfall intensity delivered on the plot. The spatial rainfall distribution and his intensity were measured with 63 rainfall gauges covering the whole plot The Joss Waldvogel Disdrometer was used to characterize the rainfall produced by the rainfall simulator. The drop size distribution was obtained. The drop size spectrum ranges from 0.25mm to 3.3mm and its shape is the same to that one produced by a natural rainfall. The rainfall intensity varies from approximately 31 to 62mm/h and it is sufficiently spatially uniform (Christiansen's coefficient of uniformity is 0.62 to 0.75) over the plot. Field tests were carried out in on a grassy field with silt-loam soil in Orroli, Sardinia in July and August 2010. The values of the mean rainfall intensities obtained from field data are in accord with the laboratory values. The field site measurement includes the surface runoff, evaluated using a dedicated tipping bucket flow meter, and the soil water content measured throughout the field experiments. The results showed the good performance of this new rainfall simulator that offers the possibility to reproduce natural rainfall to gather parameters needed for hydrologic modeling. The entire designed system offers the possibility to carry out reliable measurements of the surface runoff under different rain intensities and also allows one to measure this on different temporal scales by taking into account the differen environmental conditions. [Copyright &y& Elsevier]

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications. [ABSTRACT FROM AUTHOR]