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UID:42295d8c-6f4c-4d4e-bf98-98729338a1f9@nexttoyou
DTSTAMP:20261106T172738Z
DTSTART:20261118T203500Z
DTEND:20261118T212500Z
SUMMARY:EAS Seminar: Daniel Kirk-Davidoff (EPRI)
LOCATION:Snee Hall 2146
DESCRIPTION:Synthesizing accurate long-term wind gust timeseries using rea
 nalysis and satellite data\n\nAccurate estimate of return levels of 3-sec
 ond wind gust for a range of return periods is of great importance to eng
 ineering decisions. Durable structures that are deployed over large areas
  in low roughness length terrain\, such as solar farms\, can be subject t
 o especially strong and extensive wind gusts and suffer costly damages\, 
 so to reduce the possibility of loss of a substantial fraction of the exp
 ected value of a facility\, it is important to design them to resist haza
 rds that are expected to occur on the order of one time over the life of 
 the project. However\, high wind events sufficient to cause substantial d
 amage\, due for example to powerful thunderstorm downbursts\, are rare at
  any given observing station. There is a need for synthetic timeseries ca
 librated to relatively short observational records derived from long rean
 alysis or satellite observation time series.\n\nWe trained an ML algorith
 m to predict the probability of wind gust exceeding a set of thresholds (
 20\, 30\, 40 m/s). The training predictors derived from the ERA5 reanalys
 is\, including humidity and temperature aloft and various convective indi
 ces\, as well as a set of indices calculated from satellite infrared brig
 htness temperature observations that measured cloud top temperature and a
 rea of cloud top below a range of temperature thresholds. The wind gust t
 raining data was derived from ASOS station data and public mesonet\n\nhtt
 p://next-to-u.app/events/42295d8c-6f4c-4d4e-bf98-98729338a1f9
URL:http://next-to-u.app/events/42295d8c-6f4c-4d4e-bf98-98729338a1f9
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