Articles | Volume 21, issue 4
https://doi.org/10.5194/npg-21-777-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/npg-21-777-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
28 Jul 2014
Research article |
| 28 Jul 2014
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques
A. R. Ganguly
Northeastern University, Boston, MA, USA
E. A. Kodra
Northeastern University, Boston, MA, USA
A. Agrawal
Northwestern University, Evanston, IL, USA
A. Banerjee
University of Minnesota, Twin Cities, MN, USA
S. Boriah
University of Minnesota, Twin Cities, MN, USA
Sn. Chatterjee
University of Minnesota, Twin Cities, MN, USA
So. Chatterjee
University of Minnesota, Twin Cities, MN, USA
A. Choudhary
Northwestern University, Evanston, IL, USA
D. Das
Northeastern University, Boston, MA, USA
J. Faghmous
University of Minnesota, Twin Cities, MN, USA
P. Ganguli
Northeastern University, Boston, MA, USA
S. Ghosh
Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
K. Hayhoe
Texas Tech University, Lubbock, TX, USA
C. Hays
University of Nebraska, Lincoln, NE, USA
W. Hendrix
Northwestern University, Evanston, IL, USA
Q. Fu
University of Minnesota, Twin Cities, MN, USA
J. Kawale
University of Minnesota, Twin Cities, MN, USA
D. Kumar
Northeastern University, Boston, MA, USA
V. Kumar
University of Minnesota, Twin Cities, MN, USA
W. Liao
Northwestern University, Evanston, IL, USA
University of Minnesota, Twin Cities, MN, USA
R. Mawalagedara
Northeastern University, Boston, MA, USA
V. Mithal
University of Minnesota, Twin Cities, MN, USA
R. Oglesby
University of Nebraska, Lincoln, NE, USA
K. Salvi
Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
P. K. Snyder
University of Minnesota, Twin Cities, MN, USA
K. Steinhaeuser
University of Minnesota, Twin Cities, MN, USA
D. Wang
Northeastern University, Boston, MA, USA
D. Wuebbles
University of Illinois, Urbana-Champaign, IL, USA
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- Review of Statistical Analysis of Hydrologic Variables: Methods and Applications edited by Ramesh S. V. Teegavarapu, Jose D. Salas, and Jery R. StedingerASCE, Reston, VA; 2019; ISBN: 9780784415177; 556 pp.; $195.00. C. Prakash Khedun & V. Singh https://doi.org/10.1061/(ASCE)HE.1943-5584.0002082
- Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes P. Bhasme et al. https://doi.org/10.1016/j.jhydrol.2022.128618
- Intercomparison of model response and internal variability across climate model ensembles D. Kumar & A. Ganguly https://doi.org/10.1007/s00382-017-3914-4
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- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard et al. https://doi.org/10.1145/3514228
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