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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]

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]

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]

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]

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]

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 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]

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]

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]