Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar
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 a...
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/24/5174 |
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| author | Magfira Syarifuddin Susanna F. Jenkins Ratih Indri Hapsari Qingyuan Yang Benoit Taisne Andika Bayu Aji Nurnaning Aisyah Hanggar Ganara Mawandha Djoko Legono |
| author_facet | Magfira Syarifuddin Susanna F. Jenkins Ratih Indri Hapsari Qingyuan Yang Benoit Taisne Andika Bayu Aji Nurnaning Aisyah Hanggar Ganara Mawandha Djoko Legono |
| author_sort | Magfira Syarifuddin |
| collection | DOAJ |
| description | 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 performed by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity factor. 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 an ensemble prediction system (EPS). Cross-validation was performed using field-survey data, radar observations, and Himawari-8 imageries. 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 are in agreement with ground-based data, where the radar-based estimated grain size distribution falls within the range of in situ grain size. 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. |
| first_indexed | 2024-03-10T03:10:34Z |
| format | Article |
| id | doaj.art-5763de54b23047a2a18b73b34dac1605 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:10:34Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5763de54b23047a2a18b73b34dac16052023-11-23T10:25:49ZengMDPI AGRemote Sensing2072-42922021-12-011324517410.3390/rs13245174Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather RadarMagfira Syarifuddin0Susanna F. Jenkins1Ratih Indri Hapsari2Qingyuan Yang3Benoit Taisne4Andika Bayu Aji5Nurnaning Aisyah6Hanggar Ganara Mawandha7Djoko Legono8Earth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore 639798, SingaporeEarth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore 639798, SingaporeDepartment of Civil Engineering, State Polytechnic of Malang, Kota Malang 65141, IndonesiaEarth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore 639798, SingaporeEarth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore 639798, SingaporeEarth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore 639798, SingaporeCentre for Volcanology and Geological Hazards Mitigation, Yogyakarta 55166, IndonesiaDepartment of Agricultural and Biosystem Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, IndonesiaDepartment of Civil and Environmental Engineering, Engineering Faculty, Gadjah Mada University, Yogyakarta 55284, IndonesiaTephra 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 performed by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity factor. 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 an ensemble prediction system (EPS). Cross-validation was performed using field-survey data, radar observations, and Himawari-8 imageries. 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 are in agreement with ground-based data, where the radar-based estimated grain size distribution falls within the range of in situ grain size. 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.https://www.mdpi.com/2072-4292/13/24/5174tephraground-based weather radarBayesian approachnowcastingensemble prediction system |
| spellingShingle | Magfira Syarifuddin Susanna F. Jenkins Ratih Indri Hapsari Qingyuan Yang Benoit Taisne Andika Bayu Aji Nurnaning Aisyah Hanggar Ganara Mawandha Djoko Legono Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar Remote Sensing tephra ground-based weather radar Bayesian approach nowcasting ensemble prediction system |
| title | Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar |
| title_full | Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar |
| title_fullStr | Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar |
| title_full_unstemmed | Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar |
| title_short | Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar |
| title_sort | real time tephra detection and dispersal forecasting by a ground based weather radar |
| topic | tephra ground-based weather radar Bayesian approach nowcasting ensemble prediction system |
| url | https://www.mdpi.com/2072-4292/13/24/5174 |
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