Dr. Antonio Capponi Profile

Dr. Antonio Capponi

at Lancaster Univ

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Proceedings Article | 3 June 2022 Paper

Progress on the R4AsH triple frequency radar laboratory experiments for characterization of volcanic ash

David Macfarlane, Duncan Robertson, Antonio Capponi

Proceedings Volume 12111, 1211103 (2022) https://doi.org/10.1117/12.2618751

KEYWORDS: Radar, Particles, Reflectivity, Amplifiers, Algorithm development, Upconversion, Linear filtering, Bandpass filters, Atmospheric particles, Receivers

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Volcanic plumes pose a global risk to aviation, due to fine ash that can be dispersed over a global scale. Hazard mitigation relies on forecasts of plume evolution over time. However, the main sources of uncertainty in plume dispersion modelling remain the accurate quantification of the eruption source parameters, known as ‘source term’, describing the plume characteristics and informing the dispersion models. These parameters include particle size distribution (PSD) and concentrations of ash particles injected into the atmosphere. Estimation of the source term of eruptive plumes by reflectivity measurements using a single frequency radar depends upon assumption of a how PSD and concentrations are combined. The chosen assumptions are however ambiguous as, for example, a high concentration of smaller particles can produce the same reflectivity as that of a low concentration of larger particles. R4AsH is a triple frequency laboratory FMCW radar that simultaneously measures a controlled column of airborne ash at three frequencies: 10, 35 and 94 GHz. Coincident optical measurement of the PSD within the column are also taken to inform analysis. The aim of the R4AsH experiment is to develop a triple-frequency inversion algorithm to enable simultaneous retrieval of the ash PSD and particle concentration by combining radar reflectivity data across the Rayleigh – Mie scattering regime. Following on from our previously reported system design, we will present a review of the radar system performance and preliminary testing for the R4AsH experiments scheduled for the spring/summer of 2022.

Proceedings Article | 12 April 2021 Presentation + Paper

R4AsH: a triple frequency laboratory radar for characterizing falling volcanic ash

David Macfarlane, Ducan Robertson, Antonio Capponi

Proceedings Volume 11742, 1174219 (2021) https://doi.org/10.1117/12.2587613

KEYWORDS: Radar, Atmospheric particles, Reflectivity, Particles, Algorithm development, Optical imaging, Modeling, Explosives, Calibration, Atmospheric modeling

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Airborne ash generated by explosive volcanic eruptions presents a significant danger to aviation. Accurate modelling and predictions of the dispersal of hazardous ash into the atmosphere are currently hampered by uncertainties in the ‘source term’ parameters associated with the initial eruption plume, specifically the amount and size of ash particles released into the atmosphere. Ground based radar offers the means to remotely measure ash reflectivity, however estimation of source term parameters from reflectivity measured by single frequency radar is limited by ambiguity between the contribution of particle size distribution (PSD) and ash concentration in the plume. This means that one of these parameters must be assumed rather than measured directly, leading to uncertainties in forecasting eruption hazards. We report on R4AsH, a close range FMCW radar designed to resolve this ambiguity by simultaneous characterization of falling volcanic ash in a laboratory-controlled environment at three different frequencies: 10, 35 and 94 GHz. The R4AsH design uses a single DDS based chirp generator as a common source, multiplied and upconverted to feed three sets of transmit-receive horn antennas directed at a common target volume such that measurements will give spatially and temporally coincident measurements of falling ash. In addition, there will be independent measurement of the PSD using optical imaging and logging of the landing particle mass to calibrate results and inform analysis. The aim of R4AsH is to develop a triplefrequency inversion algorithm to enable simultaneous retrieval of PSD and ash concentration from radar data suitable for future volcano monitoring systems.

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