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Kandungan disediakan oleh USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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Combining earthquake and tsunami early warnings along the west coast of the United States

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Manage episode 434447977 series 1399341
Kandungan disediakan oleh USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

Amy Williamson, University of California Berkeley

Alerts sent through earthquake early warning (EEW) programs provide precious seconds for those alerted to take simple protective actions to mitigate their seismic risk. Programs like ShakeAlert have been providing alerts for felt earthquakes across the west coast of the US for almost 5 years. Earthquakes are also one part of a multihazard system and can trigger secondary natural hazards such as tsunamis and landslides. However in order to be effective and timely, EEW and tsunami forecast algorithms must rely on the smallest amount of data available, often with variable quality and without analyst input. This talk focuses on potential advances to EEW algorithms to better constrain earthquake location and magnitude in real time, providing improved alerts, particularly in network sparse regions. Additionally, this talk highlights work using real time data to generate rapid tsunami early warning forecasts, its feasibility, and the benefit of unifying earthquake and tsunami alerts into one cohesive public-facing alerting structure.

  continue reading

20 episod

Artwork
iconKongsi
 
Manage episode 434447977 series 1399341
Kandungan disediakan oleh USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

Amy Williamson, University of California Berkeley

Alerts sent through earthquake early warning (EEW) programs provide precious seconds for those alerted to take simple protective actions to mitigate their seismic risk. Programs like ShakeAlert have been providing alerts for felt earthquakes across the west coast of the US for almost 5 years. Earthquakes are also one part of a multihazard system and can trigger secondary natural hazards such as tsunamis and landslides. However in order to be effective and timely, EEW and tsunami forecast algorithms must rely on the smallest amount of data available, often with variable quality and without analyst input. This talk focuses on potential advances to EEW algorithms to better constrain earthquake location and magnitude in real time, providing improved alerts, particularly in network sparse regions. Additionally, this talk highlights work using real time data to generate rapid tsunami early warning forecasts, its feasibility, and the benefit of unifying earthquake and tsunami alerts into one cohesive public-facing alerting structure.

  continue reading

20 episod

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