Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

Version 1 : Received: 22 November 2021 / Approved: 23 November 2021 / Online: 23 November 2021 (13:00:31 CET)

How to cite: Syarifuddin, M.; Jenkins, S.F.; Hapsari, R.I.; Yang, Q.; Taisne, B.; Aji, A.B.; Aisyah, N.; Mawanda, H.G.; Legono, D. Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar. Preprints 2021, 2021110422 (doi: 10.20944/preprints202111.0422.v1). Syarifuddin, M.; Jenkins, S.F.; Hapsari, R.I.; Yang, Q.; Taisne, B.; Aji, A.B.; Aisyah, N.; Mawanda, H.G.; Legono, D. Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar. Preprints 2021, 2021110422 (doi: 10.20944/preprints202111.0422.v1).

Abstract

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

Keywords

tephra; ground-based weather radar; Bayesian approach; nowcasting; ensemble prediction system

Subject

EARTH SCIENCES, Geophysics

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