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SUMMARY:Extreme weather events and water security as emerging challenges f
 or global society: How can we use deep machine learning\, topological data
  analysis and causal time series analysis to study weather phenomena in la
 rge climate datasets? - Grzegorz Muszynski | British Antarctic Survey
DTSTART:20210511T100000Z
DTEND:20210511T113000Z
UID:TALK159055@talks.cam.ac.uk
CONTACT:87364
DESCRIPTION:Extreme weather events and water security are challenges for g
 lobal society across multiple dimensions\, from water-related disasters an
 d complex variations in the global water cycle to the unsustainable water 
 supply for growing populations and highly irrigated agricultural systems. 
 The first step towards addressing these challenges is to accurately identi
 fy weather phenomena that often lead to extreme events. Being able to reco
 gnise these phenomena in space and time can facilitate understanding of th
 eir developing mechanisms and life cycles. Machine and deep learning\, top
 ological data analysis and causal time series analysis offer a wide range 
 of automatic methods that can help recognise weather phenomena and their q
 uantitative assessment in a rapidly increasing amount of observational and
  simulated climate data. In this talk\, I will present recent applications
  of machine and deep learning\, topological data analysis and causal time 
 series analysis to study weather phenomena\, such as atmospheric rivers\, 
 the Indian summer monsoon\, and atmospheric blocks. I will also give persp
 ectives on major challenges where machine learning methods and causal anal
 ysis have the potential to advance the state-of-the-art in extreme weather
  detection.
LOCATION:https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09
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