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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 |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 1 ; Nr. 1, no. 1 (2014-04-04), S.479-517 |
| Datensatznummer |
250115083
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| Publikation (Nr.) |
 copernicus.org/npgd-1-479-2014.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, including well-known associations from prior climate
knowledge, as well as promising discoveries that invite further research by
the climate science community. |
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