Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 16-24

  Das, Debasish ^a,b   Ganguly, Auroop ^a   Banerjee, Arindam ^c   Obradovic, Zoran ^b  

^a Northeastern University   (United States)

^b TEMPLE UNIVERSITY   (United States)

^c UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Dominant processes; Graphical models; Precipitation extremes; Predictive models

Indexed keywords

CLIMATE VARIABILITY; COMPLEX DYNAMICAL SYSTEMS; DATA-DRIVEN APPROACH; DATA-DRIVEN METHODS; DIMENSIONALITY REDUCTION; DOMINANT PROCESS; EXPLORATORY DATA ANALYSIS; GRAPHICAL MODEL; HYBRID APPROACH; KEY INDICATOR; MULTIPLE PROCESS; MULTIPLE SCALE; NON-LINEAR REGRESSION; PHYSICAL MODEL; PHYSICS-BASED MODELS; PRECIPITATION EXTREMES; PREDICTIVE MODELS; VARIABLE SELECTION;

CLIMATOLOGY; COMPUTER SIMULATION; DATA MINING; GRAPHIC METHODS; MODELS; SENSORS;

DIGITAL STORAGE;

EID: 84866629911     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2350182.2350184     Document Type: Conference Paper

References (29)

1. M. Sugiyama, H. Shiogama, and S. Emori. Precipitation extreme changes exceeding moisture content increases in MIROC and IPCC climate models. PNAS. 2010.
2. P. A. O'Gorman and T. Schneider. The physical basis for increases in precipitation extremes in simulations of 21stcentury climate change. PNAS 2009.
3. P. A. O'Gorman and T. Schneider. Scaling of precipitation extremes over a wide range of climates simulated with an idealized GCM. Journal of Climate 22, 5676-5685.
4. Q. Schiermeier. The real holes in climate science. Nature (2010).
5. X. Chen, Y. Liu, H. Liu, J. G. Carbonell. Learning Spatial- Temporal Varying Graphs with Applications to Climate Data Analysis. AAAI. 2010.
6. A. C. Lozano, H. Li, A. Niculescu-Mizil, Y. Liu, C. Perlich, J. Hosking and N. Abe, Spatial-temporal causal modeling for climate change attribution. In ACM SIGKDD, 2009.
7. Y. Liu, A. Niculescu-Mizil, A.C. Lozano and Y. Lu Learning Temporal Causal Graphs for Relational Time- Series Analysis, in ICML. 2010.
8. Kalnay et al., The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 1996.
9. M. Steinbach, P.-N. Tan, V. Kumar, S. Klooster, C. Potter, Discovery of climate indices using clustering, in KDD 2003, .
10. K. Steinhaeuser, N. V. Chawla and A. R. Ganguly, Complex Networks as a Unified Framework for Descriptive Analysis and Predictive Modeling in Climate Science. Statistical Analysis and Data Mining, 2010.
11. J. Kawale et al. Data Guided Discovery of Dynamic Climate Dipoles NASA Conference on Intelligent Data Understanding (CIDU), 2011.
12. S. Chatterjee, K. Steinhaeuser, A Banerjee, S. Chatterjee, A. Ganguly. Sparse Group Lasso: Consistency and Climate Applications. SDM. 2012.
13. P. C. D. Milly. Stationarity Is Dead: Whither Water Management?. Science, 2008.
14. C. J. Muller and P. A. O'Gorman. An energetic perspective on the regional response of precipitation to climate change. Nature Climate Change, 2011.
15. S. C. Liu et al. Temperature dependence of global precipitation extremes. Geophysical Research Letters. 2009.
16. R. P. Allan and B. J. Soden. Atmospheric Warming and the Amplification of Precipitation Extremes. Science, 2008.
17. V. V. Kharin et al. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. Journal of Climate, 2007.
18. S. C. Kao, and A. R. Ganguly, Intensity, duration, and frequency of precipitation extremes under 21st-century warming scenarios, Journal of Geophysical Research - Atmospheres, 2011.
19. R. E. Benestad, D. Nychka and L. O. Mearns, Spatially and temporally consistent prediction of heavy precipitation from mean values. Nature Climate Change, 2012.
20. B. Sivakumar, Dominant processes concept, model simplification and classification framework in catchment hydrology, Stochastic Environmental Research and Risk Assessment, 2008.
21. S. Ghosh, D. Das, S.-C. Kao, and A.R. Ganguly, (2012): Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nature Climate Change 2012.
22. CPC US Unified Precipitation data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. website: http://www.esrl.noaa.gov/psd/.
23. 2 warming. Climate Dynamics, 2007.
24. K. Riemann-Campe, K. Fraedrich, F. Lunkeit, Global climatology of Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) in ERA-40 reanalysis. Atmospheric Research. 2009.
25. K. A. Emanuel, Atmospheric Convection. Oxford University Press. 2005.
26. D. Coumou, S. Rahmstorf. A decade of weather extremes. Nature Climate Change 2012.
27. C.F. Bohren, and B. Albrecht . Atmospheric Thermodynamics. Oxford University Press. 1998.
28. Field, C.B., et al., IPCC, Summary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change, 2012, Cambridge University Press, pp. 1-19.
29. D. Koller, N. Friedman. Probabilistic Graphical Models. The MIT Press 2009.