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BEGIN:VEVENT
DTSTAMP:20210916T132452Z
LOCATION:Jean Calvin
DTSTART;TZID=Europe/Stockholm:20210708T120000
DTEND;TZID=Europe/Stockholm:20210708T123000
UID:submissions.pasc-conference.org_PASC21_sess114_msa217@linklings.com
SUMMARY:Toward Enhanced Seasonal Forecasts Using Machine learning: Underst
 anding the Polar Vortex Variability
DESCRIPTION:Minisymposium\n\nToward Enhanced Seasonal Forecasts Using Mach
 ine learning: Understanding the Polar Vortex Variability\n\nde Fondeville\
 n\nIn both hemispheres, the atmosphere above polar regions is characterize
 d by a vortex of winds, whose intensity fluctuates seasonally and peaks du
 ring winter. In average once every two years, a sudden warming of the stra
 tosphere disturbs the wind field causing displacements and deformations of
  the vortex. The impact of these events, known as Sudden Stratospheric War
 mings (SSW), is not limited to the stratosphere but also strongly influenc
 e the weather at the earth surface up to three months after their occurren
 ce. Such influence increases the predictability of weather and can be expl
 oited to produce better long range forecasts.  Understanding the structure
  of these perturbations is key to their prediction, and so to improve the 
 quality of seasonal weather forecasts. However, studying the stratosphere 
 requires to analyse large quantity of data with complex interactions. For 
 this reason, the conditions yielding to SSW, the different states of the p
 olar vortex, and their interactions are still not fully understood. In thi
 s talk, we conduct of thorough analysis of different stratospheric paramet
 ers, use supervised learning to find potential SSW precursors and quantify
  predictability of such perturbations.\n\nDomain: CS and Math, Emerging Ap
 plications, Climate and Weather, Solid Earth Dynamics
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