Jurnal Internasional Deteksi Otomatis Letusan Gunung Api Explosive Menggunakan Tingkat Pertumbuhan Vertikal Awan yang Diperoleh dari Satelit – Pavolonis – – Bumi dan Ilmu Luar Angkasa

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Jurnal Internasional Deteksi Otomatis Letusan Gunung Api Explosive Menggunakan Tingkat Pertumbuhan Vertikal Awan yang Diperoleh dari Satelit – Pavolonis – – Bumi dan Ilmu Luar Angkasa

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Rich cloud ash, produced by explosive volcanic eruptions, is a big danger to flight. Unfortunately, explosive volcanic eruptions are not always detected in a timely manner in satellite data. The large optical depth of volcanic clouds that emerge greatly limits the effectiveness of multispectral infrared-based techniques to distinguish between volcanic and non-volcanic clouds. Short-wave radiation techniques require sufficient sunlight and a large amount of volcanic ash, relative to the hydrometeor, to be effective. Given this fundamental limitation, new automated techniques for detecting emerging clouds, produced by explosive volcanic eruptions, have been developed. The Cloud Growth Anomaly (CGA) technique uses geostationary satellite data to identify cloud objects, near volcanoes, which grow rapidly in relatively vertical clouds that are formed through meteorological processes. Explosive volcanic events have often proven to be a source of rapidly developing clouds that, at least, reach the upper troposphere. Thus, the CGA algorithm is effective in determining when recently formed clouds may be the result of explosive explosions. While the CGA technique can be applied to any geostationary satellite sensor, it is most effective when applied to the latest generation of meteorological satellites, which provide more frequent images with better spatial resolution. Using a large collection of diverse geographical explosive explosions, and several geostationary satellites, the CGA technique is explained and demonstrated. A CGA-based eruption warning tool, designed to improve the timeliness of volcanic ash guidance, is also explained.

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