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SUMMARY:Extracting trends and correlations from noisy environmental data -
  John Wettlaufer\, Oxford
DTSTART:20140501T103000Z
DTEND:20140501T113000Z
UID:TALK51597@talks.cam.ac.uk
CONTACT:Catherine Pearson
DESCRIPTION:Typically when we observe time series of what we believe is a 
 representative parameter in\na system a key question concerns how well cor
 related such data are in time.  For example\, \nhow likely is the rainfall
  this month to be the same as that next month or next year.  Here\, \nI di
 scuss the long-term correlations and properties of daily satellite retriev
 als of Arctic sea ice \nalbedo and extent taken over the last three decade
 s. The interpretation harnesses a recent \ndevelopment called multi fracta
 l temporally weighted detrended fluctuation analysis\, which \nexploits th
 e intuition that points closer in time are more likely to be related than 
 distant points.\nThe method goes beyond treatments that assume a single de
 cay scale process\, such as \na first-order autoregression\, which cannot 
 be justifiably fitted to these observations despite \nthe commonality of d
 oing so. It is found that long-term persistence is re-entrant\nbeyond the 
 seasonal scale and that on the seasonal scale the system is governed by \n
 white noise. 
LOCATION:Open Plan Area\, BP Institute\, Madingley Rise CB3 0EZ
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