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On the Data-driven Inference of Modulatory Networks in Climate Science: an Application to West African Rainfall : Volume 1, Issue 1 (04/04/2014)

By González Ii, D. L.

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Book Id: WPLBN0004020098
Format Type: PDF Article :
File Size: Pages 39
Reproduction Date: 2015

Title: On the Data-driven Inference of Modulatory Networks in Climate Science: an Application to West African Rainfall : Volume 1, Issue 1 (04/04/2014)  
Author: González Ii, D. L.
Volume: Vol. 1, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Bello, G. A., Angus, M. P., Tetteh, I. K., Samatova, N. F., Srinivas, S., Padmanabhan, K.,...Kumar, V. (2014). On the Data-driven Inference of Modulatory Networks in Climate Science: an Application to West African Rainfall : Volume 1, Issue 1 (04/04/2014). Retrieved from

Description: North Carolina State University, Raleigh, North Carolina 27695-8206, USA. 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.

On the data-driven inference of modulatory networks in climate science: an application to West African rainfall

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