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| Titel |
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall |
| VerfasserIn |
D. L. González II, M. P. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, N. F. Samatova |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 1 ; Nr. 22, no. 1 (2015-01-13), S.33-46 |
| Datensatznummer |
250120961
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| Publikation (Nr.) |
 copernicus.org/npg-22-33-2015.pdf |
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| Zusammenfassung |
| Decades of hypothesis-driven and/or first-principles research have been
applied towards the discovery and explanation of the mechanisms that drive
climate phenomena, such as western African Sahel summer rainfall~variability.
Although connections between various climate factors have been theorized, not
all of the key relationships are fully understood. We propose a data-driven
approach to identify candidate players in this climate system, which can help
explain underlying mechanisms and/or even suggest new relationships, to
facilitate building a more comprehensive and predictive model of the
modulatory relationships influencing a climate phenomenon of interest. We
applied coupled heterogeneous association rule mining (CHARM), Lasso
multivariate regression, and dynamic Bayesian networks to find relationships
within a complex system, and explored means with which to obtain a consensus
result from the application of such varied methodologies. Using this fusion
of approaches, we identified relationships among climate factors that
modulate Sahel rainfall. These relationships fall into two categories:
well-known associations from prior climate knowledge, such as the
relationship with the El Niño–Southern Oscillation (ENSO) and putative
links, such as North Atlantic Oscillation, that invite further research. |
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