A graph‐based approach to find teleconnections in climate data
By Jaya Kawale, S. Liess, Arjun Kumar, Michael S. Steinbach, P. Snyder, Vipin Kumar, Auroop Ratan Ganguly, Nagiza F. Samatova, Fredrick H. M. Semazzi. 2013
43 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
- Vipin Kumar
- Anuj Karpatne
- Gowtham Atluri
- Michael S. Steinbach
- Shashi Shekhar
- James H. Faghmous
- Arindam Banerjee
- Auroop Ratan Ganguly
In Statistical and Nonlinear Physics
- Michael S. Steinbach
- Vipin Kumar
- Arindam Banerjee
- Nagiza F. Samatova
- Anuj Karpatne
- Gowtham Atluri
- Poulomi Ganguli
- Pavel Berkhin
Citations in the corpus
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “ples include the discovery of novel climate patterns and relationships [18], [19], closure of knowledge gaps in turbulence modeling efforts [20], [21], discovery of novel com-”
- Machine Learning for the Geosciences: Challenges and Opportunities (2017, background): “One of the widely-studied category of teleconnections is dipoles [27], [80], which are pairs of regions with strong negative correlations (e.”
- Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, methodology): “Figure 6a–b provides an example where new data mining methods (Kawale et al., 2011, 2013) for dipole iscovery were used to extract information about climate oscillators that may be useful for model evaluation.”