Search

In this paper, we study the impact of lightning and radar reflectivity factor data assimilation on the precipitation VSF (very short-term forecast, 3 h in this study) for two severe weather events that occurred in Italy. The first case refers to a moderate and localized rainfall over central Italy that occurred on 16 September 2017. The second case occurred on 9 and 10 September 2017 and was very intense and caused damages in several geographical areas, especially in Livorno (Tuscany) where nine people died. The first case study was missed by several operational forecasts, including that performed by the model used in this paper, while the Livorno case was partially predicted by operational models. We use the RAMS@ISAC model (Regional Atmospheric Modelling System at Institute for Atmospheric Sciences and Climate of the Italian National Research Council), whose 3D-Var extension to the assimilation of radar reflectivity factor is shown in this paper for the first time. Results for the two cases show that the assimilation of lightning and radar reflectivity factor, especially when used together, have a significant and positive impact on the precipitation forecast. For specific time intervals, the data assimilation is of practical importance for civil protection purposes because it changes a missed forecast of intense precipitation (≥40 mm in 3 h) to a correct one. While there is an improvement of the rainfall VSF thanks to the lightning and radar reflectivity factor data assimilation, its usefulness is partially reduced by the increase in false alarms, especially when both datasets are assimilated. [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]

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]

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]

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]