Projects per year
Fingerprint The Fingerprint is created by mining the titles and abstracts of the person's research outputs and projects/funding awards to create an index of weighted terms from discipline-specific thesauri.
- 2 Similar Profiles
Earth sciences
Engineering & Materials Science
Time series
Engineering & Materials Science
general circulation model
Earth & Environmental Sciences
oscillation
Earth & Environmental Sciences
Drought
Engineering & Materials Science
Climate change
Engineering & Materials Science
sea level pressure
Earth & Environmental Sciences
sea surface temperature
Earth & Environmental Sciences
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Projects 2015 2017
- 1 Finished
Scalable Analysis of Earth System Data Using Parallelize
Kumar, V., Atluri, G., Liess, S. & Steinbach, M. S.
10/20/15 → 10/19/17
Project: Research project
analysis
Research Output 2001 2018
A cautionary note on decadal sea level pressure predictions from GCMs
Liess, S., Snyder, P. K., Kumar, A. & Kumar, V., Mar 1 2018, In : Advances in Climate Change Research. 9, 1, p. 43-56 14 p.Research output: Contribution to journal › Article
sea level pressure
general circulation model
prediction
CMIP
train
A Teleconnection between the West Siberian Plain and the ENSO Region
Liess, S., Agrawal, S., Chatterjee, S. & Kumar, V., Jan 1 2017, In : Journal of Climate. 30, 1, p. 301-315Research output: Contribution to journal › Article
3
Citations
(Scopus)
Tripoles: A new class of relationships in time series data
Agrawal, S., Atluri, G., Karpatne, A., Haltom, W., Liess, S., Chatterjee, S. B. & Kumar, V., Aug 13 2017, KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 697-706 10 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; vol. Part F129685).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Time series
7
Citations
(Scopus)
Exploring the predictability of 30-day extreme precipitation occurrence using a global SST-SLP correlation network
Lu, M., Lall, U., Kawale, J., Liess, S. & Kumar, V., Jan 1 2016, In : Journal of Climate. 29, 3, p. 1013-1029 17 p.Research output: Contribution to journal › Article
sea level pressure
sea surface temperature
atmospheric moisture
reciprocity
atmospheric circulation
Understanding dominant factors for precipitation over the great lakes region
Chatterjee, S., Liess, S., Banerjee, A. & Kumar, V., Jan 1 2016, 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, p. 3821-3827 7 p. (30th AAAI Conference on Artificial Intelligence, AAAI 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Lakes
Feature extraction
Learning systems
Composite materials
Experiments
Press/Media
Spring in February: Why was this winter so warm? UMN experts weigh in.
2/27/17
1 item of Media coverage
Press/Media: Expert Comment