Name
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 geogra... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MCSE.2015.128 | Computing in Science & Engineering |
Keywords | Field | DocType |
Meteorology,Hidden Markov models,Data models,Tensile stress,Computational modeling,Atmospheric modeling | Global climate,Data science,Data modeling,Latent Dirichlet allocation,Computer science,Climate pattern,Unsupervised learning,Topic model | Journal |
Volume | Issue | ISSN |
17 | 6 | 1521-9615 |
Citations | PageRank | References |
2 | 0.42 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Tang | 1 | 4 | 1.46 |
| Claire Monteleoni | 2 | 327 | 24.15 |