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Difficulties in the representation of rainfall variability using ground-based sensors over mountainous areas necessitate the use of high-resolution satellite precipitation data sets from combined passive microwave (PMW) and geostationary infrared observations. The accuracy of these data sets depends greatly on the uncertainty characteristics of the PMW sensor retrievals and sampling frequency. In this paper, we evaluate the retrieval error characteristics from different PMW sensors over mountainous terrain using high temporal and spatial resolution ground-based radar (GR) reference rainfall data sets from two field experiments: one in the mid-Atlantic East Coast of the United States and the second in the Northeastern Italian Alps. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW overpasses, and scales GR parameters to the satellite products’ nominal spatial resolution. Different PMW precipitation retrieval algorithms are evaluated for four PMW sensors. The error analysis shows varying error characteristics across different PMW retrievals. Moreover, the magnitude-dependent systematic error, going from overestimation or weak underestimation of light precipitation to mainly underestimation of heavier precipitation, shows weak covariation with the ground reference. Detection capabilities for all sensors increase markedly with an increasing reference rate and convective occurrence because of the more pronounced ice-scattering signal associated with deeper convection and high rain rates. [ABSTRACT FROM AUTHOR]

The accurate estimation of rainfall from remote sensing is of paramount importance for many applications as, for instance, the mitigation of natural hazards like floods, droughts, and landslides. Traditionally, microwave observations in the frequency between 10 and 183 GHz are used for estimating rainfall based on the direct interaction of radiation with the hydrometeors within precipitating clouds in a so-called top-down approach. Recently, a bottom-up approach was proposed that uses satellite soil moisture products derived from microwave observations (<10 GHz) for the estimation of accumulated rainfall amounts. The integration of the bottom-up and top-down approaches has large potential for providing high accurate rainfall estimates exploiting their different and complementary nature. In this study, we perform a long-term (3 years) assessment of different satellite rainfall products exploiting the full range of microwave frequencies over Italy. Specifically, the integration of two top-down algorithms (CDRD, Cloud Dynamics and Radiation Database, and PNPR, Passive microwave Neural network Precipitation Retrieval) for estimating rainfall from conically and cross-track scanning radiometers, and one bottom-up algorithm (SM2RAIN) applied to the Advanced SCATterometer soil moisture product is carried out. The performances of the products, individually and merged together, are assessed at daily time scale. The integration of top-down and bottom-up approaches provides the highest performance both in terms of continuous and categorical scores (i.e., median correlation coefficient and root mean square error values equal to 0.71 and 6.62 mm, respectively). In such a combination, the limitations of the two approaches are compensated allowing a better estimation of ground accumulated rainfall through SM2RAIN while, overcoming the limitations of rainfall estimation for intense events during wet conditions through CDRD-PNPR product. The accuracy and the reliability of the merged product open new possibilities for their testing in hydrological applications, such as the monitoring and prediction of floods and droughts over large areas, including regions where ground-based measurements are sparse or not available. [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]

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]

State-of-the-art satellite rainfall products are often the only way for measuring precipitation in remote areas of the world. These products are mainly based on the exploitation of microwave (MW) radiometers on board LEO satellites (top-down approach). However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications (natural hazards forecast and mitigation, water management). Currently, one of the major issues impacting the quality of the information retrieved from space is related to the estimation of light precipitation that causes a general underestimation of the total amount of rainfall. This issue is particularly important over land due to the uncertainty and spatial variability of surface emissivity. With the purpose of improving the accuracy of satellite rainfall products, some approaches using satellite soil moisture (SM) data were recently developed (bottom-up approach). In 2013 a new method for estimating rainfall using satellite SM observations, called SM2RAIN, has been proposed. The method is based on the inversion of the soil water balance equation, i.e. it estimates rainfall by using the variation in time of the amount of water stored into to the soil, thus considering it "as a natural rain gauge". SM2RAIN has been applied both at local and global scale with ground- and satellite-based SM data as input with satisfactory results in terms of rainfall estimation. Moreover, previous studies found that the correction of rainfall estimates through SM2RAIN provides improvement in flood modeling. However SM-only based rainfall estimates suffer a number of issues such as the inability to estimate precipitation when the soil is fully saturated, when it is vegetated or snow-covered, besides not providing any precipitation data over the sea. Thus a fully global satellite rainfall product, able to integrate rainfall estimates derived from top-down and bottom-up approaches, has been demonstrated to be highly beneficial for increasing the accuracy of satellite precipitation estimates.Through the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF), the SM2RAIN approach together with the MW-based rainfall product H23 are the scientific background for the development of the "H64 precipitation-soil moisture integrated product". H23 provides daily precipitation estimates by combining MW precipitation rate estimates derived from conical (H01) and cross-track (H02) scanning radiometers. SM estimates obtained through H16 and H101 H SAF products are used to estimate rainfall through SM2RAIN, and then merged with H23 precipitation estimates through a simple nudging scheme. H64 has been designed to combine optimally the two different approaches in order to provide a more reliable rainfall product, that can be used in an operational framework. Preliminary results show that H64 can efficiently reproduce the observed rainfall during a 4-year period, providing a useful and reliable source of information over scarcely instrumented areas. This first assessment provides very useful insights about the development of a near-real time rainfall product based on SM2RAIN and MW-based top-down precipitation products. [ABSTRACT FROM AUTHOR]