A real-time tephra fallout rate model by a small-compact X-band Multi-Parameter radar

Syarifuddin, Magfira; Oishi, Satoru; Nakamichi, Haruhisa; Maki, Masayuki; Hapsari, Ratih Indri; Mawandha, Hanggar Ganara; Aisyah, Nurnaning; Basuki, Ahmad; Loeqman, Agoes; Shimomura, Masaki; Iguchi, Masato

doi:10.1016/j.jvolgeores.2020.107040

Cited by 6 publications

(29 citation statements)

References 33 publications

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“…A small-compact X-band dual-polarization Doppler weather Multi-Parameter (X-MP) of WR2100 type radar, manufactured by Furuno Electric Co, was installed and operated at Merapi Museum (7.5 km SW from Merapi's vent) in 2014-2019 (Figure 1). The main objective of the installation was for research in volcano hazards such as rain-triggered lahar [16,20] and real-time tephra fallout rate [19]. Figure 1 presents the area covered by the 360 radar scan of plan position indicator (PPI) strategy.…”

Section: Radar Setting and Study Casementioning

confidence: 99%

“…Radar data were heavily affected by ground clutter up to 13 elevation angle scan (Figure 1). While the default clutter cancellation routine by radar can recognize the beam blockage [20], ground clutter still occurred as the result of direct contact between surface and sidelobes due to the presence of Merapi and Turgo Hill in the SW sector (Figure 1, [19]). Sidelobes are unwanted returns from a direction outside the main lobe, which can bias the reflectivity intensity, Doppler velocity, and spectrum width estimates.…”

Section: Radar Setting and Study Casementioning

confidence: 99%

“…The scattering of electromagnetic waves from tephra particles follows the Rayleigh theorem [8][9]. Only coarse ash to lapilli regimes were identified by this type of radar [18][19], resulting in two different tephra class regimes: fine (F) and coarse (C) particle. These two terms should be distinguished from finer ash and coarser ash regimes, mentioned in section 1, as our classification identified class F as a class for particle diameter ranging from 0.015 mm to 0.35 mm and class C for particle diameter ranging from 0.35 mm to 6 mm.…”

Section: Tephra Detectionmentioning

confidence: 99%

“…This study presents the potency and uncertainty of real-time tephra plume detection and tracking applied to two eruption events at Merapi in 2018. First, we overcome previous limitations of unwanted clutter contaminating radar scanning data [19] by applying an automatic noise cancellation. Then, we coupled the modified tephra-radar retrieved model [8][9][18][19] with the EPS of radar nowcasting.…”

Section: Introductionmentioning

confidence: 99%

“…First, we overcome previous limitations of unwanted clutter contaminating radar scanning data [19] by applying an automatic noise cancellation. Then, we coupled the modified tephra-radar retrieved model [8][9][18][19] with the EPS of radar nowcasting. The evaluation was done by comparing the radar-based tephra properties (i.e., concentration and grain size) with groundbased data and satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

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

Syarifuddin¹,

Jenkins²,

Hapsari³

et al. 2021

Preprint

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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.

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Section: Radar Setting and Study Casementioning

confidence: 99%

Section: Radar Setting and Study Casementioning

confidence: 99%

Section: Tephra Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Syarifuddin¹,

Jenkins²,

Hapsari³

et al. 2021

Preprint

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Estimating the velocity of pyroclastic density currents using an operational dual-PRF radar

Syarifuddin

Jenkins

Taisne

et al. 2022

Journal of Volcanology and Geothermal Research

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Prediksi Hujan untuk Manajemen Bencana dan Risiko Pertanian

Syarifuddin¹

2023

Preprint

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Di antara tiga bencana alam utama, geologi, biologi, dan hidrometeorologi, bencana hidrometeorologi terjadi paling sering (~90%) dan berdampak paling merugikan. Bencana hidrometeorologi yang sering terjadi di Indonesia adalah banjir, tanah longsor dan aliran debris akibat hujan lebat, gelombang tinggi, kebakaran hutan dan lahan, kekeringan, serta cuaca ekstrim seperti angin puting beliung maupun siklon tropis. Selama 20 tahun terakhir, setidaknya terdapat rata-rata 42 kematian per 1,000,000 populasi akibat banjir di Indonesia. Sebagai negara kepulauan yang berada di Daerah Konvergensi Antar Tropik (DKAT), Indonesia memiliki kondisi iklim dan cuaca yang sangat kompleks dan dinamis. Berkurangnya wilayah resapan dan proyeksi peningkatan intensitas cuaca ekstrim dan frekuensi banjir sebagai dampak perubahan iklim, semakin menambah kekhawatiran terhadap risiko iklim dan cuaca. Tahun 2021, Nusa Tenggara Timur (NTT) bahkan tidak berdaya menghadapi siklon tropis Seroja. Sebanyak 67.1% dari Produk Domestik Bruto di Indonesia dihasilkan dari sektor yang tergantung kepada cuaca dan iklim dengan pertanian adalah sektor peringkat pertama. Dampak bencana hidrometeorologi pada sektor pertanian dapat berupa penurunan luasan area lahan produktif, gangguan dalam operasional serta kerusakan jaringan irigasi, bendungan, dan infrastruktur pertanian lainnya atau secara tidak langsung akibat kenaikan harga komoditi pertanian di pasar setelah bencana terjadi.

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A real-time tephra fallout rate model by a small-compact X-band Multi-Parameter radar

Cited by 6 publications

References 33 publications

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

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

Estimating the velocity of pyroclastic density currents using an operational dual-PRF radar

Prediksi Hujan untuk Manajemen Bencana dan Risiko Pertanian

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