DocumentCode
3609297
Title
Can Topic Modeling Shed Light on Climate Extremes?
Author
Cheng Tang ; Monteleoni, Claire
Volume
17
Issue
6
fYear
2015
Firstpage
43
Lastpage
52
Abstract
Understanding changes in climate extremes is an urgent challenge. Topic modeling techniques from natural language processing can help scientists learn climate patterns from data. The authors´ work extracts global climate patterns from multivariate climate data, modeling relations between variables via latent topics and discovering the probability of each climate topic appearing at different geographical locations.
Keywords
climatology; data analysis; geophysics computing; natural language processing; climate extremes; climate pattern probability; geographical locations; global climate pattern extraction; latent topics; multivariate climate data; natural language processing; topic modeling techniques; Atmospheric modeling; Computational modeling; Data models; Hidden Markov models; Meteorology; Tensile stress; climate extremes; climate informatics; latent Dirichlet allocation; machine learning; scientific computing; topic models; unsupervised learning;
fLanguage
English
Journal_Title
Computing in Science Engineering
Publisher
ieee
ISSN
1521-9615
Type
jour
DOI
10.1109/MCSE.2015.128
Filename
7310910
Link To Document