Towards More Effective Climate Similarity Measures: Unterschied zwischen den Versionen
Keine Bearbeitungszusammenfassung |
Keine Bearbeitungszusammenfassung |
||
| Zeile 2: | Zeile 2: | ||
|vortragender=Pierre Toussing | |vortragender=Pierre Toussing | ||
|email=ulokb@student.kit.edu | |email=ulokb@student.kit.edu | ||
|vortragstyp= | |vortragstyp=Bachelorarbeit | ||
|betreuer=Pawel Bielski | |betreuer=Pawel Bielski | ||
|termin=Institutsseminar/2020- | |termin=Institutsseminar/2020-09-11 | ||
|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. | |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. | ||
}} | }} | ||
Aktuelle Version vom 8. September 2020, 09:52 Uhr
| Vortragende(r) | Pierre Toussing | |
|---|---|---|
| Vortragstyp | Bachelorarbeit | |
| Betreuer(in) | Pawel Bielski | |
| Termin | Fr 11. September 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. | |