Errico Picciotti

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.

Optimum Estimation of Rain Microphysical Parameters From X-Band Dual-Polarization Radar Observables

Modern polarimetric weather radars typically provide reflectivity, differential reflectivity, and specific differential phase shift, which are used in algorithms to estimate the parameters of the rain drop size distribution (DSD), the mean drop shape, and rainfall rate. A new method is presented to minimize the parameterization error using the Rayleigh scattering limit relations multiplied with a rational polynomial function of reflectivity-weighted raindrop diameter to approximate the Mie character of scattering. A statistical relation between the shape parameter of the DSD with the median volume diameter of raindrops is derived by exploiting long-term disdrometer observations. On the basis of this relation, new optimal estimators of rain microphysical parameters and rainfall rate are developed for a wide range of rain DSDs and air temperatures using X-band scattering simulations of polarimetric radar observables. Parameterizations of radar specific path attenuation and backscattering phase shift are also developed, which do not depend on this relation. The methodology can, in principle, be applied to other weather radar frequencies. A numerical sensitivity analysis shows that calibration bias and measurement noise in radar measurements are critical factors for the total error in parameters estimation, despite the low parameterization error (less than 5%). However, for the usual errors of radar calibration and measurement noise (of the order of 1 dB, 0.2 dB, and 0.3

Inside Volcanic Clouds: Remote Sensing of Ash Plumes Using Microwave Weather Radars

\hbox{deg}\ \hbox{km}^{-1}

Evaluation of a New Polarimetric Algorithm for Rain-Path Attenuation Correction of X-Band Radar Observations Against Disdrometer

for reflectivity, differential reflectivity, and specific differential propagation phase shift, respectively), the new parameterizations provide a reliable estimation of rain parameters (typically less than 20% error).

Correction of Polarimetric Radar Reflectivity Measurements and Rainfall Estimates for Apparent Vertical Profile in Stratiform Rain

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 ...

Spatially-Adaptive Advection Radar Technique for Precipitation Mosaic Nowcasting

A new algorithm called self-consistent with optimal parameterization (SCOP) for attenuation correction of radar reflectivities at low elevation angles is developed and evaluated. The SCOP algorithm, which uses optimal parameterization and best-fitted functions of specific attenuation coefficients and backscattering differential phase shift, is applied to X-band dual-polarization radar data and evaluated on the basis of radar observables calculated from disdrometer data at a distance of 35 km from the radar. The performance of the SCOP algorithm is compared with other algorithms [reflectivity-differential phase shift (ZPHI) and full self-consistent (FSC)] presented in the literature. Overall, the new algorithm performs similarly to ZPHI for the attenuation correction of horizontal-polarization reflectivity, whereas the FSC algorithm exhibits significant underestimation. The ZPHI algorithm tends to overestimate small rain-path attenuation values. All algorithms exhibit significant underestimation at high differential rain-path attenuation values, probably due to the presence of hail along the path of the radar beam during the examined cases. The new SCOP algorithm has the potential to retrieve profiles of horizontal and differential reflectivities with better accuracy than the other algorithms due to the low error of the parameterization functions used in it. Typical radar calibration biases and measurement noise are sufficient requirements to ensure low errors of the proposed algorithm. A real-time method to calibrate the differential reflectivity without additional measurements is also described.

Sensitivity analysis of self-consistent polarimetric rain retrieval to C-Band radar observables

AbstractA method for correcting the vertical profile of reflectivity measurements and rainfall estimates (VPR) in plan position indicator (PPI) scans of polarimetric weather radars in the melting layer and the snow layer during stratiform rain is presented. The method for the detection of the boundaries of the melting layer is based on the well-established characteristic of local minimum of copolar correlation coefficient in the melting layer. This method is applied to PPI scans instead of a beam-by-beam basis with the addition of new acceptance criteria adapted to the radar used in this study. An apparent vertical profile of reflectivity measurements, or rainfall estimate, is calculated by averaging the range profiles from all of the available azimuth directions in each PPI scan. The height of each profile is properly scaled with melting-layer boundaries, and the reflectivity, or rainfall estimate, is normalized with respect to its value at the lower boundary of the melting layer. This approach allows va...

Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays

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 1200 km2) 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.

Mobile Radar Network Measurements for Flood Applications During the Field Campaign of HydroRad Project

Numerical simulations are used to investigate the sensitivity of C-Band rain retrieval to polarimetric radar observables. The simulator is based on a T-matrix solution technique, while the hydrometeor distribution have been characterized with respect to dielectric composition (water, ice, and mixed phase), raindrop size distribution (normalized gamma distribution), shape (ellipsoid with parameterized aspect ratio), and angle orientation. The self-consistent ZPHI approach is here adopted and the sensitivity analysis is performed in order to evaluate the expected errors of this method to radar observables. Since differential phase shift K/sub DP/ is affected by the spatial variation of the backscattering differential phase shift /spl delta/, a new neural-network estimation technique is applied to remove /spl delta/ effects on K/sub DP/ estimate. The performance of these correction procedures and the effects of an error bias on radar measurements is evaluated by using mono-dimensional Gaussian raincell models.

Performance evaluation of rain products from a polarimetric X-band radar by using a new raw data processing chain

During an eruptive event, the near-real-time monitoring of volcanic explosion onset and its mass flow rate (MFR) is a key factor to predict ash plume dispersion and to mitigate risk to air traffic. Microwave (MW) weather radars have proved to be a fundamental instrument to derive eruptive source parameters. We extend this capability to include an early-warning detection scheme within the overall volcanic ash radar retrieval methodology. This scheme, called the volcanic ash detection (VAD) algorithm, is based on a hybrid technique using both fuzzy logic and conditional probability. Examples of VAD applications are shown for some case studies, including the Icelandic Grímsvötn eruption in 2011, the Eyjafjallajökull eruption in 2010, and the Italian Mt. Etna volcano eruption in 2013. Estimates of the eruption onset from the radar-based VAD module are compared with infrasonic array data. One-dimensional numerical simulations and analytical model estimates of MFR are also discussed and intercompared with sensor-based retrievals. Results confirm in all cases the potential of MW weather radar for ash plume monitoring in near real time and its complementarity with infrasonic array for early-warning system design.