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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

Agrawal, R. and Srikant, R.: Fast algorithms for mining association rules in large databases, in: VLDB 1994, 487–499, 1994.; Agrawal, R., Imieli\'nski, T., and Swami, A.: Mining association rules between sets of items in large databases, Sigmod Record, 22, 207–216, 1993.; Bailey, T. L. and Gribskov, M.: Combining evidence using p-values: application to sequence homology searches, Bioinformatics, 14, 48–54, doi:10.1093/bioinformatics/14.1.48, 1998.; Borboudakis, G., Triantafilou, S., Lagani, V., and Tsamardinos, I.: A constraint-based approach to incorporate prior knowledge in causal models, in: ESANN, 2011.; Bühlmann, P.: Causal statistical inference in high dimensions, Math. Method. Oper. Res., 77, 357–370, 2013.; Chang, P., Yamagata, T., Schopf, P., Behera, S. K., Carton, J., Kessler, W. S., Meyers, G., Qu, T., Schott, F., Shetye, S., and Xie, S.-P.: Climate Fluctuations of Tropical Coupled Systems – The Role of Ocean Dynamics, J. Climate, 19, 5122–5174, doi:10.1175/JCLI3903.1, 2006.; Dean, T. and Kanazawa, K.: A Model for Reasoning About Persistence and Causation, Tech. rep., Brown University, Providence, RI, USA, 1989.; Fisher, R. A.: Statistical Methods for Research Workers, Oliver & Boyd, Edinburgh, 1932.; Friedman, N., Murphy, K., and Russell, S.: Learning the structure of dynamic probabilistic networks, in: UAI'98, 139–147, 1998.; Gonçalves, G.: Analysis of interpolation errors in urban digital surface models created from Lidar data, Eionet, 160–168, 2002.; Gonzalez, D. L., Pendse, S. V., Padmanabhan, K., Angus, M. P., Tetteh, I. K., Srinivas, S., Villanes, A., Semazzi, F., Kumar, V., and Samatova, N. F.: Coupled Heterogeneous Association Rule Mining (CHARM): Application toward Inference of Modulatory Climate Relationships, in: 2013 IEEE 13th International Conference on Data Mining (ICDM'13), IEEE, 1055–1060, 2013.; Grossman, I. and Klotzbach, P.: A review of North Atlantic modes of natural variability and their driving mechanisms, J. Geophys. Res., 114, D24107, doi:10.1029/2009JD012728, 2009.; Hallett, T., Coulson, T., Pilkington, J., Clutton, T., Pemberton, J., and Grenfell, B.: Why large-scale climate indices seem to predict ecological processes better than local weather, Nature, 430, 71–75, 2004.; Havlin, S., Kenett, D., Ben-Jacob, E., Bunde, A., Cohen, R., Hermann, H., Kantelhardt, J., Kertész, J., Kirkpatrick, S., Kurths, J., Portugali, J., and Solomon, S.: Challenges in network science: Applications to infrastructures, climate, social systems and economics, The European Physical Journal Special Topics, 214, 273–293, 2012.; Huang, Y., Zhang, L., and Zhang, P.: A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets, IEEE T. Knowl. Data En., 20, 433–448, 2008.; Hurrell, J.: Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation, Science, 269, 676–679, 1995.; Hyttinen, A., Hoyer, P. O., Eberhardt, F., and Järvisalo, M.: Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure, CoRR, abs/1309.6836, 2013.; Janicot, S., Moron, V., and Fontaine, B.: Sahel droughts and ENSO dynamics, Geophys. Res. Lett., 23, 515–518, 1996.; Kidson, J. W. and Newell, R. E.: African rainfall and its relation to the upper air circulation, Q. J. Roy. Meteor. Soc., 103, 441–456, 1977.; Li, X., Ren, Q., Weng, Y., Cai, H., Zhu, Y., and Zhang, Y.: SCGPred: A Score-based Method for Gene Structure Prediction by Combining Multiple Sources of Evidence, Genomics, Proteomics & Bioinformatics, 6, 175 – 185, 2008.; Lu, J.: The dynamics of the Indian Ocean sea surface temperature forcing of Sahel drought, Clim. Dynam., 33, 445–460, 2009.; Manatsa, D., Chipindu, B., and Behera, S. K.: Shifts in IOD and their impacts East Africa rainfall, Theor. Appl. Climatol., 110


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