Probabilistic clustering of extratropical cyclones using regression mixture models
By Scott Gaffney, Andrew Robertson, Padhraic Smyth, Suzana J. Camargo, Michael Ghil. 2007
201 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/b4a3fd842a7f628025f75c67d487c43f40dc6f49
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Zhenhui Jessie Li
- R. Kays
- Jiawei Han
- Kyu-Young Whang
- Bolin Ding
- Jae-Gil Lee
- Abdelwaheb Hannachi
- T. Woollings
In Global and Planetary Change
- T. Woollings
- Christian L. E. Franzke
- D. Straus
- Abdelwaheb Hannachi
- Jiawei Han
- S. Corti
- Andrew J. Majda
Citations in the corpus
- Low‐frequency nonlinearity and regime behavior in the Northern Hemisphere extratropical atmosphere (2017, background): “…climate [e.g., Plaut and Simonnet, 2001; Simonnet and Plaut, 2001; Robertson and Ghil, 1999] and intraseasonal variability [Ponater et al., 1994] there is also evidence that midlatitude cyclone tracks can be conditioned by large midlatitude flow regimes [Blender et al., 1997; Gaffney et al., 2007].”
- Trajectory clustering: a partition-and-group framework (2007, methodology): “[7, 8] have proposed a model-based clustering algorithm for trajectories.”
- Swarm (2010, methodology): “[8] propose trajectory clustering methods based on probabilistic modeling of a set of trajectories.”