Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 22, issue 1
Articles | Volume 22, issue 1
https://doi.org/10.5194/npg-22-33-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/npg-22-33-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 22, issue 1
Research article
 | 
13 Jan 2015
Research article |  | 13 Jan 2015

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

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, and N. F. Samatova

Viewed

Total article views: 2,845 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,472 1,175 198 2,845 197 194
  • HTML: 1,472
  • PDF: 1,175
  • XML: 198
  • Total: 2,845
  • BibTeX: 197
  • EndNote: 194
Views and downloads (calculated since 04 Apr 2014)
Cumulative views and downloads (calculated since 04 Apr 2014)

Cited

Saved (final revised paper)

Latest update: 12 Nov 2024
Download
Short summary
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 fall into two categories: well-known associations from prior knowledge, and putative links that invite further research.
Read more
Special issue
Physics-driven data mining in climate change and weather...