A multi-sensor approach for volcanic ash cloud retrieval and eruption characterization: the 23 November 2013 Etna lava fountain

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

Rainfall Estimation from Polarimetric S-Band Radar Measurements: Validation of a Neural Network Approach

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

Microphysical characterization of microwave radar reflectivity due to volcanic ash clouds

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 pyroclastic 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. © 2006 IEEE

Spatially-Adaptive Advection Radar Technique for Precipitation Mosaic Nowcasting

A new numerical nowcasting technique to predict the radar reflectivity field at very short term, up to few hours, is presented. The method is based on the spatial segmentation of the reflectivity field and estimated advection field to produce radar reflectivity forecasts and, for this reason, is named Spatially-adaptive Precipitation Advective Radar Estimator (SPARE). A large data set coming from the Italian radar network mosaic (spatial domain size of about 1200 x 1200 km(2)) is used to test the overall performance of SPARE against the simplest method of radar map temporal persistence. An original approach to estimate the radar field motion, based on the phase cross-correlation principle, is formulated in this paper. Results are given either in terms of skill scores of predicted radar maps or in terms of predicted uncertainty. The latter provides a new methodology to evaluate the expected performance of SPARE predictions

Synthetic Signatures of Volcanic Ash Cloud Particles From X-Band Dual-Polarization Radar

Weather radar retrieval, in terms of detection, estimation, and sensitivity, of volcanic ash plumes is dependent not only on the radar system specifications but also on the range and ash cloud distribution. The minimum detectable signal can be increased, for a given radar and ash plume scenario, by decreasing the observation range and increasing the operational frequency and also by exploiting possible polarimetric capabilities. For short- range observations in proximity of the volcano vent, a compact portable system with relatively low power transmitter may be evaluated as a suitable compromise between observational and technological requirements. This paper, starting from the results of a previous study and from the aforementioned issues, is aimed at quantitatively assessing the optimal choices for a portable X-band system with a dual-polarization capability for real-time ash cloud remote sensing. The physical-electromagnetic model of ash particle distributions is systematically reviewed and extended to include nonspherical particle shapes, vesicular composition, silicate content, and orientation phenomena. The radar backscattering response at X-band is simulated and analyzed in terms of self-consistent polarimetric signatures for ash classification purposes and correlation with ash concentration for quantitative retrieval aims. An X-band radar system sensitivity analysis to ash concentration, as a function of radar specifications, range, and ash category, is carried out in trying to assess the expected system performances and limitations

C-band polarimetric weather radar calibration using a fuzzy logic fusion of three techniques

The goal of this work is to show the possibility to combine three different calibration techniques to obtain a reliable monitoring of the radar system through the definition of a quality index concept. The fuzzy logic approach uses the idea to convert the calibration error in a so called linguistic variable defined as the impact of it on the parameter estimation. After an inference step, we obtain a quality matrix that represents the quality index of calibration on the observed variables in different part of the system (transmitting and receiving). This information can be extremely important for the remote monitoring and the realtime diagnosis of the radar system state. The output of the procedure is a diagnostic quality index, useful to establish where and when a technical intervention on the radar system is necessary. Results, using copolar and differential reflectivity, are shown for a C-band weather radar operating in Italy

Comparison of GPM Core Observatory and Ground-Based Radar Retrieval of Mass-Weighted Mean Raindrop Diameter at Midlatitude

Abstract 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

Comparison of advanced radar polarimetric techniques for operational attenuation correction at C band

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 alpha(hh) (specific differential attenuation alpha(dp)) and specific differential phase K(dp) is the basis for simple path-integrated attenuation correction using differential phase Phi(dp). 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 Phi(dp)-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