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M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca


The Ka–Ku Dual-Frequency Precipitation Radar (DPR) and the Microwave Imager on board the Global Precipitation Measurement (GPM) mission core satellite have been collecting data for more than 3 years, providing precipitation products over the globe, including oceans and remote areas where ground-based precipitation measurements are not available. The main objective of this work is to validate the GPM-DPR products over a key climatic region with complex orography such as the Italian territory. The performances of the DPR precipitation rate products are evaluated over an 18-month period (July 2015–December 2016) using both radar and rain gauge data. The ground reference network is composed of 22 weather radars and more than 3000 rain gauges. DPR dual-frequency products generally show better performance with respect to the single-frequency (i.e., Ka- or Ku-band only) products, especially when ground radar data are taken as reference. A sensitivity analysis with respect to season and rainfall intensity is also carried out. It was found that the normal scan (NS) product outperforms the high-sensitivity scan (HS) and matched scan (MS) during the summer season. A deeper analysis is carried out to investigate the larger discrepancies between the DPR-NS product and ground reference data. The most relevant improvement of the DPR products’ performance was found by limiting the comparison to the upscaled radar data with a higher quality index. The resulting scores in comparison with ground radars are mean error (ME) = −0.44 mm h−1, RMSE = 3.57 mm h−1, and fractional standard error (FSE) = 142%, with the POD = 65% and FAR = 1% for rainfall above 0.5 mm h−1.

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F. Chen, W. T. Crow, L. Ciabatta, P. Filippucci, G. Panegrossi, A. C. Marra, S. Puca, and C. Massari


Satellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques [i.e., triple collocation (TC) and quadruple collocation (QC) analyses]. Our collocation approach leverages the Soil Moisture to Rain (SM2RAIN) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, because other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23—even in areas that lack adequate ground-based observations to apply traditional validation approaches.

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