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One of the main goals of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission is to retrieve parameters of the raindrop size distribution (DSD) globally. As a standard product of the Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory satellite, the mass-weighted mean diameter Dm and the normalized intercept parameter Nw are estimated in three dimensions at the resolution of the radar. These are two parameters of the three-parameter gamma model DSD adopted by the GPM algorithms. This study investigates the accuracy of the Dm retrieval through a comparative study of C-band ground radars (GRs) and GPM products over Italy. The reliability of the ground reference is tested by using two different approaches to estimate Dm. The results show good agreement between the ground-based and spaceborne-derived Dm, with an absolute bias being generally lower than 0.5 mm over land in stratiform precipitation for the DPR algorithm and the combined DPR–GMI algorithm. For the DPR–GMI algorithm, the good agreement extends to convective precipitation as well. Estimates of Dm from the DPR high-sensitivity (HS) Ka-band data show slightly worse results. A sensitivity study indicates that the accuracy of the Dm estimation is independent of the height above surface (not shown) and the distance from the ground radar. On the other hand, a nonuniform precipitation pattern (interpreted both as high variability and as a patchy spatial distribution) within the DPR footprint is usually associated with a significant error in the DPR-derived estimate of Dm. [ABSTRACT FROM AUTHOR]

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values. [ABSTRACT FROM AUTHOR]

Recently, the interest on snowfall remote sensing and quantitative precipitation estimation is becoming a popular topic by both the scientific and operational communities. As a matter of fact, snow plays a key role in the hydrological cycle and Earth energy budget and clearly represents a meteorological hazard that can seriously compromise human activities and properties. In this study, we used a dual-polarization X-band weather radar to quantify the near-surface liquid equivalent snowfall rate, proposing a new parameterization based on the use of radar reflectivity factor and specific differential phase shift. This effort adds to several recent works, mainly focused on S-band weather radar systems, demonstrating that the use of the radar specific differential phase shift (K dp) is able to enhance the estimation precision with respect to the more customary approaches making use of radar reflectivity factor alone. To demonstrate this concept also at X-band, some case studies were collected from December 2018 to May 2019 in the Southern Apennine Mountains in the area of Naples (Italy). They were used to compare the proposed radar based liquid equivalent snowfall rate estimations, based on Z and K dp , with reference laser-optical disdrometer time series collected in the close reference site of Montevergine observatory. Findings show that also at X band the use of K dp produces a better score between the radar-derived liquid equivalent snowfall rate and the reference one from the disdrometer. • The use of specific differential phase shift improves the radar-based estimation of snowfall rate. • Snow estimators based on equivalent radar reflectivity factor can be corrupted by melting layer effects. • In snow conditions, strong horizontal winds can corrupt laser-optical disdrometer measurements. [ABSTRACT FROM AUTHOR]

H-SAF is the Satellite Application Facility on support to operational Hydrology and water management supervised and coordinated by EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) since 2005. Currently, the program entered in its Third Continuous Development and Operation Phase (CDOP-3), which will end on February 2022. During this phase, H-SAF contributes to the EUMETSAT MTG (Meteosat Third Generation) program by providing operational precipitation products based on combined microwave/infrared (MW/IR) techniques for the high spatial/temporal/spectral resolution Flexible Combined Imager (FCI) and for the MTG Lightning Imager (LI). Moreover, in view of the EUMETSAT Polar System Second Generation (EPS-SG) satellite mission, day-1 precipitation products for the Microwave Sounder (MWS), and Microwave Imager (MWI) will be delivered to provide precipitation estimates on a global scale. The collaboration with the NASA Precipitation Measurement Mission (PMM) Research Program promotes fruitful interactions on several critical aspects in precipitation retrieval algorithm development between H SAF and GPM (Global Precipitation Measurement). The nearly-global observational datasets built from coincident active and passive spaceborne MW observations are used as reference for the development of the EPS-SG precipitation products, and for the optimization of the existing PMW (Passive Microwave) products. The current work fits in this context. The H-SAF validation activities are performed by the Product Validation Group (PVG) composed by experts from the National Meteorological and Hydrological European Institutes under the coordination of the Italian Civil Protection Department (DPC). The product validation methodology is based on comparisons with radar and rain gauge data to produce yearly statistics (multi-categorical and continuous scores) through the use of a Unique Common Code (UCC) developed by the PVG. Recently, the validation over the MSG full disk area was performed with respect to the 2A DPR products. Comparison results obtained by the two H-SAF neural network algorithms retrieved by MW cross-track scanners (AMSU/MHS for H02B and ATMS for H18) over the European and full disk area are shown. [ABSTRACT FROM AUTHOR]