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JDR Vol.14 No.1 pp. 135-150
(2019)
doi: 10.20965/jdr.2019.p0135

Paper:

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Estimating the Volcanic Ash Fall Rate from the Mount Sinabung Eruption on February 19, 2018 Using Weather Radar

Magfira Syarifuddin*1,†, Satoru Oishi*2, Ratih Indri Hapsari*3, Jiro Shiokawa*4, Hanggar Ganara Mawandha*4, and Masato Iguchi*1

*1Sakurajima Volcano Research Center, Disaster Prevention Research Institute (DPRI), Kyoto University
1722-19 Sakurajima-Yokoyama, Kagoshima 891-1419, Japan

†Corresponding Author,

*2Research Centre for Urban Safety and Security, Kobe University, Hyogo, Japan

*3Department of Civil Engineering, State Polytechnic of Malang, Malang, Indonesia

*4Graduate School of Engineering, Kobe University, Hyogo, Japan

Received:
July 31, 2018
Accepted:
January 21, 2019
Published:
February 1, 2019
Creative Commons License
Keywords:
volcanic ash, X-MP radar, ash microphysical model, radar remote sensing, volcanic eruption
Abstract

This paper presents a theoretical method for estimating volcanic ash fall rate from the eruption of Sinabung Volcano on February 19, 2018 using an X-band multi-parameter radar (X-MP radar). The X-MP radar was run in a sectoral range height indicator (SRHI) scan mode for 6° angular range (azimuth of 221°–226°) and at an elevation angle of 7° to 40° angular range. The distance of the radar is approximately 8 km in the Southeastern direction of the vent of Mount Sinabung. Based on a three-dimensional (3-D) image of the radar reflectivity factor, the ash column height was established to be more than 7.7 km, and in-depth information on detectable tephra could be obtained. This paper aims to present the microphysical parameters of volcanic ash measured by X-MP radar, which are the tephra concentration and the fall-out rate. These parameters were calculated in a two-step stepwise approach microphysical model using the scaled gamma distribution. The first step was ash classification based on a set of training data on synthetic ash and its estimated reflectivity factor. Using a naïve Bayesian classification, the measured reflectivity factors from the eruption were classified into the classification model. The second step was estimating the volcanic ash concentration and the fall-out rate by power-law function. The model estimated a maximum of approximately 12.9 g·m-3 of ash concentration from the coarse ash class (mean diameter Dn= 0.1 mm) and a minimum of approximately 0.8 megatons of volcanic ash mass accumulation from the eruption.

Cite this article as:
M. Syarifuddin, S. Oishi, R. Hapsari, J. Shiokawa, H. Mawandha, and M. Iguchi, “Estimating the Volcanic Ash Fall Rate from the Mount Sinabung Eruption on February 19, 2018 Using Weather Radar,” J. Disaster Res., Vol.14 No.1, pp. 135-150, 2019.
Data files:
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