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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]

Radar-rainfall estimation is a complex process that involves several error sources, some of which are related to the environmental context. The presence of orographic obstacles heavily affects the quality of the retrieved radar products. In relatively flat terrain conditions, dual-polarization capability has been proven either to increase the data quality or to improve the rainfall estimate. The potential benefit of using polarimetric techniques for precipitation retrieval is evaluated here using data coming from two radar systems operating in Italy under complex-orography conditions. The analysis outlines encouraging results that might open new scenarios for operational applications. Indeed, the applied rainfall algorithm employing specific differential phase mostly outperformed the examined reflectivity-based retrieval techniques except for the analyzed winter storm. In the latter case, the likely contamination by frozen or melting snow tended to degrade the performance of the examined Kdp-based rainfall algorithms. [ABSTRACT FROM AUTHOR]

The differential phase (ΦDP) measured by polarimetric radars is recognized to be a very good indicator of the path integrated by rain. Moreover, if a linear relationship is assumed between the specific differential phase (KDP) and the specific attenuation (AH) and specific differential attenuation (ADP), then attenuation can easily be corrected. The coefficients of proportionality, γH and γDP, are, however, known to be dependent in rain upon drop temperature, drop shapes, drop size distribution, and the presence of large drops causing Mie scattering. In this paper, the authors extensively apply a physically based method, often referred to as the “Smyth and Illingworth constraint,” which uses the constraint that the value of the differential reflectivity ZDR on the far side of the storm should be low to retrieve the γDP coefficient. More than 30 convective episodes observed by the French operational C-band polarimetric Trappes radar during two summers (2005 and 2006) are used to document the variability of γDP with respect to the intrinsic three-dimensional characteristics of the attenuating cells. The Smyth and Illingworth constraint could be applied to only 20% of all attenuated rays of the 2-yr dataset so it cannot be considered the unique solution for attenuation correction in an operational setting but is useful for characterizing the properties of the strongly attenuating cells. The range of variation of γDP is shown to be extremely large, with minimal, maximal, and mean values being, respectively, equal to 0.01, 0.11, and 0.025 dB °-1. Coefficient γDP appears to be almost linearly correlated with the horizontal reflectivity (ZH), differential reflectivity (ZDR), and specific differential phase (KDP) and correlation coefficient (ρHV) of the attenuating cells. The temperature effect is negligible with respect to that of the microphysical properties of the attenuating cells. Unusually large values of γDP, above 0.06 dB °-1, often referred to as “hot spots,” are reported for 15%—a nonnegligible figure—of the rays presenting a significant total differential phase shift (ΔφDP > 30°). The corresponding strongly attenuating cells are shown to have extremely high ZDR (above 4 dB) and ZH (above 55 dBZ), very low ρHV (below 0.94), and high KDP (above 4° km-1). Analysis of 4 yr of observed raindrop spectra does not reproduce such low values of ρHV, suggesting that (wet) ice is likely to be present in the precipitation medium and responsible for the attenuation and high phase shifts. Furthermore, if melting ice is responsible for the high phase shifts, this suggests that KDP may not be uniquely related to rainfall rate but can result from the presence of wet ice. This hypothesis is supported by the analysis of the vertical profiles of horizontal reflectivity and the values of conventional probability of hail indexes. [ABSTRACT FROM AUTHOR]

A procedure for the estimation of rainfall rate, capitalizing on a radar-based raindrop size distribution (RSD) parameter retrieval and neural network (NN) inversion techniques, is validated using an extensive and quality-controlled archive. The RSD retrieval algorithm utilizes polarimetric variables measured by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), through an ad hoc regularized neural network method. Evaluation of rainfall estimation from the NN-based method is accomplished using a large radar data and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign. Point estimates of hourly rainfall accumulations and instantaneous rainfall rates from NN-based and parametric polarimetric rainfall relations are compared with dense surface gauge observations. Rainfall accumulations from RSD retrieval-based methods are shown to be sensitive to the choice of a raindrop fall speed model. To minimize the impact of this choice, a new “direct” neural network approach is tested. Proposed NN-based approaches exhibit bias and root-mean-square error characteristics comparable with those obtained from parametric relations, specifically optimized for the JPOLE dataset, indicating an appealing generalization capability with respect to the climatological context. All tested polarimetric relations are shown to be sensitive to hail contamination as inferred from the results of automatic polarimetric echo classification and available storm reports. [ABSTRACT FROM AUTHOR]

Rain path attenuation correction is a challenging task for quantitative use of weather radar measurements at frequencies higher than S band. The proportionality relationship between specific attenuation αhh (specific differential attenuation αdp) and specific differential phase Kdp is the basis for simple path-integrated attenuation correction using differential phase Φdp. However, the coefficients of proportionality are known to be dependent upon temperature, on the one hand, and shape and raindrop size distribution, on the other hand. To solve this problem, a Bayesian classification scheme is proposed to empirically find the prevailing rain regime and adapt the Φdp-based method. The proposed approach herein is compared with other polarimetric techniques currently available in the literature. Several episodes observed in the Paris, France, area by the C-band dual-polarized weather radar operating in Trappes (France) are analyzed and results are discussed. [ABSTRACT FROM AUTHOR]

The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dual-polarized 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. [ABSTRACT FROM AUTHOR]

Ground-based microwave radar systems can have a valuable role in volcanic ash cloud monitoring as evidenced by available radar imagery. Their use for ash cloud detection and quantitative retrieval has been so far not fully investigated. In order to do this, a forward electromagnetic model is set up and examined taking into account various operating frequencies such as S-, C-, X-, and Ka-bands. A dielectric and microphysical characterization of volcanic vescicular ash is carried out. Particle size-distribution (PSD) functions are derived both from the sequential fragmentation-transport (SFT) theory of pyroelastic deposits, leading to a scaled-Weibull PSD, and from more conventional scaled-Gamma PSD functions. Best fitting of these theoretical PSDs to available measured ash data at ground is performed in order to determine the value of the free PSD parameters. The radar backscattering from spherical-equivalent ash particles is simulated up to Ka-band and the accuracy of the Rayleigh scattering approximation is assessed by using an accurate ensemble particle scattering model. A classification scheme of ash average concentration and particle size is proposed and a sensitivity study of ash radar backscattering to model parameters is accomplished. A comparison with C-band radar signatures is finally illustrated and discussed. [ABSTRACT FROM AUTHOR]

A new stable backward iterative technique to correct for path attenuation and differential attenuation is presented here. The technique named, neural network iterative polarimetric precipitation estimator by radar (NIPPER), is based on a polarimetric model used to train an embedded neural network, constrained by the measurement of the differential phase along the rain path. Simulations are used to investigate the efficiency, accuracy, and the robustness of the proposed technique. The precipitation is characterized with respect to raindrop size, shape, and orientation distribution. The performance of NIPPER is evaluated by using simulated radar volumes scan generated from S-band radar measurements. A sensitivity analysis is performed in order to evaluate the expected errors of NIPPER. These evaluations show relatively better performance and robustness of the attenuation correction process when compared with currently available techniques. [ABSTRACT FROM AUTHOR]

Operating a meteorological radar is generally a challenging task when in presence of a significant beam blockage as in complex orography. Apart from enhanced ground clutter, mountainous obstructions of the radar beam can significantly reduce the radar visibility and, thus, its monitoring capabilities. Self-consistent adaptive techniques to reconstruct vertical profiles of reflectivity (VPR) and near-surface rain-rate fields' from high-elevation reflectivity bins are here proposed, compared, and tested for ranges up to 60 km. The methodology is based on statistical estimators trained by a large reflectivity volumetric datasets, classified into stratiform and convective rain regimes and resampled onto a uniform Cartesian grid by means of a modified Cressman technique. For what concerns reflectivity vertical profiles, two methods, respectively named statistical nonlinear reconstruction (NSR) and neural network reconstruction (NNR), are considered. The NSR method is based on the principal component analysis, applied to the radar dataset, in order to extract significant reflectivity-profile variance. A retrieval technique, based on a nonlinear multiple regression scheme, is then used to infer near-surface reflectivity from available high-altitude echoes at a given range. The NNR is based on a three-layer artificial neural network trained by means a feed forward backpropagation algorithm. For what concerns the near-surface rain retrieval, besides a power-law reflectivity-rain-rate (ZR) approach, a three-layer neural network technique is also set up in order to estimate surface rain rate from reconstructed VPR. The proposed reconstruction techniques are here illustrated by using volumetric data acquired by the C-band Doppler single-polarization radar, operated in L'.Aquila, Italy. A case study, related to a rainfall event that occurred during fall 2000, is discussed. Using a test area within 60 km from the radar site and simulating the presence of beam obstructions, a comparison of NSR and NNR with conventional area average reconstruction techniques shows that the percentage improvement of both NSR and NNR approaches is significant, both for the error bias (by 30% to more than 50%, depending on attitude) and variance (by 10% to more than 20%). A sensitivity test indicates that the VPR reconstruction procedure is fairly robust to missing data, especially in terms of error bias. The comparison of estimated radar rainfall with rain gauge data measurement is also illustrated. The mean field bias closer to its optimal value and an error variance much smaller is obtained when neural network techniques are applied than with conventional ZR methods for both techniques of reconstruction. With respect to the latter, the obtained improvement is more than 40% in terms of root mean square error and is comparable when estimating near-surface rain rate using either NSR or NNR methods to reconstruct the reflectivity vertical profiles. Limitations, potential, and future developments of the proposed adaptive reconstruction techniques are finally discussed. [ABSTRACT FROM AUTHOR]

On 10 July 2019, a giant hail-bearing supercell hit the Adriatic coast of central Italy. Hailstones with a maximum diameter of 14 cm were reported in the city of Pescara between 10:00 and 11:00 UTC. In this work, the main synoptic and mesoscale features, responsible for the triggering and the development of the supercell, are analyzed using the WRF model. The intrusion of Bora wind over the northern and central Adriatic was relevant for two reasons: on the one side, the arrival of low-level cold air produced an uplift of the pre-existing warm air and favored the triggering of convection; on the other side, the strong vertical wind shear, also due to the presence of intense upper-level southwesterlies, created conditions favorable to the formation of the supercell. The predictability of the event is also discussed, comparing simulations starting at different initial times and forced with GFS and IFS forecasts. The model results show that the runs initialized at earlier times reproduced more accurately the track and the time evolution of the supercell. The HAILCAST module of WRF was also used to simulate hailstorm characteristics, such as the average hailstone diameter. WRF-HAILCAST simulations proved to be in fair agreement with the radar reflectivity retrievals and with local reports. [ABSTRACT FROM AUTHOR]

Recent research has demonstrated the value of polarimetric measurements for the correction of rain-path attenuation at X-band radar frequency and the estimation of rain parameters including drop-size distributions (DSD). The issue this paper is concerned with is to what degree uncertainties in attenuation correction can affect the estimation of DSD. Since attenuation-correction uncertainty enhances with rain path, our hypothesis is that DSD retrieval uncertainty at X-band may deteriorate with range. In this paper, we evaluate the relative accuracy of X-band DSD retrieval against DSD estimates from S-band radar observations and in situ disdrometer spectra. We present comparisons of various techniques for estimating DSD model parameters from attenuation-corrected X-band dual-polarization radar data. Coincident X-band polarimetric-radar (XPOL) and S-band polarimetric-radar dual-polarized radar measurements from the International H2O Project experiment as well as coincident XPOL (MP-X) measurements over disdrometer during a typhoon storm case in Japan are used to assess the accuracy of the different DSD retrieval algorithms applied to X-band radar measurements. [ABSTRACT FROM AUTHOR]

A large data set of raindrop size distribution (RSD) measurements collected with the Joss-Waldvogel disdrometer (JWD) and the 2-D video disdrometer (2DVD) in the U.K., Greece, Japan, and theU.S. are analyzed and modeled. This work extends a previous effort devoted to the exploitation of U.K. data and the design of a stochastic procedure to randomly generate synthetic RSD intermittent time series. This study seeks to: 1) explore the differences of RSD-derived moments for distinct hydroclimate regions, ranging from tropics to subtropics and mid and northern latitudes; 2) compare the governing parameters of the normalized gamma RSD for both stratiform and convective events and perform a sensitivity analysis by using different best fitting techniques; 3) exploit the time-correlation structure of the estimated RSD parameters as the input of a vector autoregressive stationary model used to simulate time series (or horizontal profiles) of RSDs and, consequently, its moments as the rain rate and concentration; and 4) characterize the distribution of the inter-rain duration and rain duration to design a semi-Markov chain to represent the intermittency feature of the rainfall process in a climatological framework. This climatological analysis and the related stochastic RSD generation model may find useful applications within both hydrometeorology and radio propagation. [ABSTRACT FROM AUTHOR]

Hydrometeorological and radio propagation applications can benefit from the capability to model the time evolution of raindrop size distribution (RSD). A new stochastic vector autoregressive semi-Markov model is proposed to randomly synthesize (generate) the temporal series of the three driving parameters of a normalized Gamma RSD. Rainfall intermittence is reproduced through a discrete semi-Markov process, modeled from disdrometer measurements using two-state analytical statistics of rain and dry period duration. The overall model is set up by means of a large set of disdrometer measurements, collected from 2003 to 2005 at Chilbolton, U.K. The driving parameters of the retrieved RSD are estimated using three approaches: the Gamma moment method and the 1-D and 3-D maximum-likelihood methods. Interestingly, these methodologies lead to quite different results, particularly when one is interested in evaluating RSD higher order moments such as the rain rate. The accuracy of the proposed RSD time-series generation technique is evaluated against available disdrometer measurements, providing excellent statistical scores. [ABSTRACT FROM AUTHOR]

A model-based fuzzy-logic method for hydrometeor classification using C-band polarimetric radar data is presented and discussed. Membership functions of the fuzzy-logic algorithm are designed for best fitting simulated radar signatures at C-band. Such signatures are derived for ten supervised hydrometeor classes by means of a fully polarimetric radar scattering model. The Fuzzy-logic Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC) is designed to use a relatively small set of polarimetric observables, i.e., copolar reflectivity and differential reflectivity, but a version of the algorithm based on the use of specific differential phase is also numerically tested and documented. The classification methodology is applied to volume data coming from a C-band two-radar network that is located in north Italy within the Po valley. Numerical and experimental results clearly show the improvements of hydrometeor classification, which were obtained by using FRAHCC with respect to the direct use of fuzzy-logic-based algorithms that are specifically tuned for S-band radar data. Moreover, the availability of two C-band rainfall observations of the same event allowed us to implement a path-integrated attenuation correction procedure, based on either a composite radar field approach or a network-constrained variational algorithm. The impact of these correction procedures on hydrometeor classification is qualitatively discussed within the considered case study. [ABSTRACT FROM AUTHOR]

The potential of ground-based microwave weather radar systems for volcanic ash cloud detection and quantitative retrieval is evaluated. The relationship between radar reflectivity factor, ash concentration, and fall rate is statistically derived for various eruption regimes and ash sizes by applying a radar-reflectivity microphysical model. To quantitatively evaluate the ash detectability by weather radars, a sensitivity analysis is carried out by simulating synthetic ash clouds and varying ash concentration and size as a function of the range. Radar specifications are taken from typical radar systems at S-, C-, and X-band. A prototype algorithm for volcanic ash radar retrieval (VARR) is discussed. Starting from measured single-polarization reflectivity, the statistical inversion technique to retrieve ash concentration and fall rate is based on two cascade steps, namely: 1) classification of eruption regime and volcanic ash category and 2) estimation of ash concentration and fall rate. Expected accuracy of the VARR algorithm estimates is evaluated using a synthetic data set. An application of the VARR technique is finally shown, taking into consideration the eruption of the Grímsvötn volcano in Iceland on November 2004. Volume scan data from a Doppler C-band radar, which is located at 260 km from the volcano vent, are processed by means of the VARR algorithm. Examples of the achievable VARR products are presented and discussed. [ABSTRACT FROM AUTHOR]

A severe weather event is analyzed in terms of cell development, dynamics, and thermodynamics. The Mediterranean region is sporadically affected by severe weather events causing damage and injuries. An unusual and severe weather event hit Italy on July 9–10, 2019 causing heavy damage. The meteorological structure was characterized by a trough entering the eastern Mediterranean area from northeastern Europe affecting Italy and the Balkans with cold-air advection over the Adriatic Sea. On the morning of 10 July 2019, a supercell developed along the coast north of Pescara (central Italy), producing intense rainfall (130 mm/3 h) and a heavy hailstorm with hailstones larger than 10 cm in diameter. In this work, the dynamics and thermodynamics for triggering and maintenance of the supercell are investigated using the numerical model WRF (Weather Research and Forecasting system), satellite, radar-data, soundings, and ground observations. In the companion paper, the role of SST and orography was investigated assessing that the SST spatial representation, its anomaly distribution and the topography play a key role in triggering the supercell (Ricchi et al., 2023). In this paper, high spatial and temporal-resolution model simulations and observations are used to investigate the characteristics of the supercell by analyzing both its horizontal and vertical structure. The hailstone characteristics are assessed by using the HAILCAST parameterization in which its performance and reliability are discussed. • Vertical velocity up to 50 m/s and overshooting top presence in development phase. • 10 min output, horizontal grid interval O(1 km), around 100 vertical levels needed. • Strong agreement with observed data and WRF simulated supercell signature. • WER and BWER are well simulated, proxies of strong vertical updraft structure. • The HAILCAST module adequately reproduces the size of the hail. [ABSTRACT FROM AUTHOR]

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring. [ABSTRACT FROM AUTHOR]

Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI), the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and effective radius), the generation of new satellite products (ash concentration and particle number density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude, total ash mass erupted and particle number density from thermal infrared to microwave). This approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining event of 23 November 2013 was considered as a test case. The results of the integration show the presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after the integration, of about the 30%. Moreover, the percentage of the airborne "fine" ash retrieved from the satellite is estimated to be about 1%-2% of the total ash emitted during the eruption. Finally, all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits. [ABSTRACT FROM AUTHOR]

Microphysical and dynamical features of volcanic tephra due to Plinian and sub-Plinian eruptions can be quantitatively monitored by using ground-based microwave weather radars. The methodological rationale and unique potential of this remote-sensing technique are illustrated and discussed. Volume data, acquired by ground-based weather radars, are processed to automatically classify and estimate ash particle concentration and fallout. The physical- statistical retrieval algorithm is based on a backscattering microphysical model of fine, coarse, and lapilli ash particles, used within a Bayesian classification and optimal estimation methodology. The experimental evidence of the usefulness and limitations of radar acquisitions for volcanic ash monitoring is supported by describing several case studies of volcanic eruptions all over the world. The radar sensitivity due to the distance and the system noise, as well as the various radar bands and configurations (i.e., Doppler and dual polarized), are taken into account. The discussed examples of radar-derived ash concentrations refer to the case studies of the Augustine volcano eruption in 2002, observed in Alaska by an S-band radar; the Grímsvötn volcano eruptions in 2004 and 2011, observed in Iceland by C- and X-band weather radars and compared with in situ samples; and the Mount Etna volcano eruption in 2011, observed by an X-band polarimetric radar. These applications demonstrate the variety of radar-based products that can be derived and exploited for the study of explosive volcanism. [ABSTRACT FROM AUTHOR]

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events. [ABSTRACT FROM AUTHOR]

In this paper, a Bayesian statistical approach for supervised classification and estimation of hydrometeors, using a C-band polarimetric radar, is presented and discussed. The Bayesian Radar Algorithm for Hydrometeor Classification at C-band (BRAHCC) is supervised by a backscattering microphysical model, aimed at representing ten different hydrometeor classes in water, ice, and mixed phase. The expected error budget is evaluated by means of contingency tables on the basis of C-band radar noisy and attenuated synthetic data. Its accuracy is better than that obtained from a previously developed fuzzy logic C-band classification algorithm. As a second step of the overall retrieval algorithm, a multivariate regression is adopted to derive water content statistical estimators, exploiting simulated polarimetric radar data for each hydrometeor class. The BRAHCC methodology is then applied to a convective hail event, observed by two C-band dual-polarized radars in a network configuration. The hydrometeor classification along the line of sight, connecting the two C-band radars, is performed using the BRAHCC applied to path-attenuation-corrected data. Qualitative results are consistent with those derived from the fuzzy logic algorithm. Hydrometeor water content temporal evolution is tracked along the radar line of sight. Hail vertical occurrence is derived and compared with an empirical hail detection index applied along the radar connection line during the whole event. [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]

On 10 October 2018 an intense storm, characterized by heavy rainfall, hit the Sardinia island, reaching a peak of 452 mm of rain measured in 24 h. Among others, two particularly intense phases were registered between 3 and 6 UTC (Universal Coordinated Time), and between 18 and 24 UTC. The forecast of this case study is challenging because the precipitation was heavy and localized. In particular, the meteorological model used in this paper, provides a good prediction only for the second period over the eastern part of the Sardinia island. In this work, we study the impact of lightning data assimilation and horizontal grid resolution on the Very Short-term Forecast (VSF, 3 and 1 h) for this challenging case, using the RAMS@ISAC meteorological model. The comparison between the 3 h VSF control run and the simulations with lightning data assimilation shows the considerable improvement given by lightning data assimilation, especially for the precipitation that occurred in the eastern part of the island. Reducing the VSF range to 1 h, resulted in higher model performance with a good precipitation prediction over eastern and south-central Sardinia. In addition, the comparison between simulated and observed reflectivity shows an important improvement of simulations with lightning data assimilation compared to the control forecast. However, simulations assimilating lightning overestimated the precipitation in the last part of the day. The increasing of the horizontal resolution to 2 km grid spacing reduces the false alarms and improves the model performance. [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]

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]