Towards More Effective Climate Similarity Measures: Unterschied zwischen den Versionen

Version vom 23. Juni 2020, 12:45 Uhr

Vortragende(r)	Pierre Toussing
Vortragstyp	Proposal
Betreuer(in)	Pawel Bielski
Termin	Fr 26. Juni 2020
Vortragssprache
Vortragsmodus
Kurzfassung	Finding dependencies over large distances — known as teleconnections — is an important task in climate science. To find such teleconnections climate scientists usually use Pearson’s Correlation, but often ignore other available similarity measures, mostly because they are not easily comparable: their values usually have different, sometimes even inverted, ranges and distributions. This makes it difficult to interpret their results. We hypothesize that providing the climate scientists with comparable similarity measures would help them find yet uncaptured dependencies in climate. To achieve this we propose a modular framework to present, compare and combine different similarity measures for time series in the climate-related context. We test our framework on a dataset containing the horizontal component of the wind in order to find dependencies to the region around the equator and validate the results qualitatively with climate scientists.

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 |betreuer=Pawel Bielski
 |termin=Institutsseminar/2020-06-26
-|kurzfassung=Finding dependencies over large distances — known as teleconnections — is an important task in climate science. To find such teleconnections climate scientists usually use Pearson’s Correlation as a similarity measure, but often ignore other available ones, mostly because they are not easily comparable: their values usually have different, sometimes even inverted, ranges and distributions. This makes it difficult to interpret their results. We hypothesize that providing the climate scientists with comparable similarity measures would help them find yet uncaptured dependencies in climate. To achieve this we propose a modular framework to present, compare and combine different similarity measures for time series in the climate-related context. We test our framework on a dataset containing the horizontal component of the wind in order to find dependencies to the region around the equator and validate the results qualitatively with climate scientists.
+|kurzfassung=Finding dependencies over large distances — known as teleconnections — is an important task in climate science. To find such teleconnections climate scientists usually use Pearson’s Correlation, but often ignore other available similarity measures, mostly because they are not easily comparable: their values usually have different, sometimes even inverted, ranges and distributions. This makes it difficult to interpret their results. We hypothesize that providing the climate scientists with comparable similarity measures would help them find yet uncaptured dependencies in climate. To achieve this we propose a modular framework to present, compare and combine different similarity measures for time series in the climate-related context. We test our framework on a dataset containing the horizontal component of the wind in order to find dependencies to the region around the equator and validate the results qualitatively with climate scientists.
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