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Innhold levert av USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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
Innhold levert av USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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 episoder

Artwork
iconDel
 
Manage episode 434447977 series 1399341
Innhold levert av USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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 episoder

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