A Review of Self-Organizing Map Applications in Meteorology and Oceanography

Liu, Yonggang; Weisberg, Robert H.

doi:10.5772/13146

Cited by 151 publications

(116 citation statements)

References 107 publications

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“…Although SOMs may theoretically be best suited for decomposing a continuum and have been successively used in regionalization (Lin and Chen 2006) in addition to classifying atmospheric circulation (Chávez-Arroyo et al 2014;Sheridan and Lee 2011;Guèye et al 2011;Espinoza et al 2012), they have not always been able to sufficiently represent large-scale circulation variability because of the two-dimensional constraint (Jacobeit 2010). Furthermore, the parameters of a SOM may be difficult to tune to produce a reliable classification in regions where there has not been sufficient work on the atmospheric circulation as different parameter choices may lead to different SOM patterns (Liu and Weisberg 2011).…”

Section: Classification Methodsmentioning

confidence: 99%

Daily near-surface large-scale atmospheric circulation patterns over the wider Caribbean

Chadee

Clarke

2015

Clim Dyn

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Section: Classification Methodsmentioning

confidence: 99%

Daily near-surface large-scale atmospheric circulation patterns over the wider Caribbean

Chadee

Clarke

2015

Clim Dyn

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“…It may be considered as a nonlinear generalization, which has many advantages over the conventional feature extraction techniques such as empirical orthogonal functions (EOF) or PCA (e.g., Liu et al, 2006). SOM applications are becoming increasingly useful in geosciences (e.g., Liu and Weisberg, 2011). As stated by Uriarte and Martín (2008): "The SOM provides a nonlinear, ordered, smooth mapping of high-dimensional input data manifolds onto the elements of a regular, low-dimensional array.…”

Section: Methodsologymentioning

confidence: 99%

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

et al. 2017

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Abstract. Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of "rain events". The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective-stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.

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“…To understand how circulation and transport are connected to other meteorological variables, the Self-Organizing Map (SOM) method has been shown to be a viable cluster and pattern extraction tool (Liu et al, 2006;Liu and Weisberg, 2011). Cassano et al (2006) evaluated model representations and projections of the SLP patterns over the Arctic.…”

mentioning

confidence: 99%

Heat Transport Pathways into the Arctic and their Connections to Surface Air Temperatures

Mewes¹,

Jacobi²

2018

Preprint

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A Review of Self-Organizing Map Applications in Meteorology and Oceanography

Cited by 151 publications

References 107 publications

Daily near-surface large-scale atmospheric circulation patterns over the wider Caribbean

Daily near-surface large-scale atmospheric circulation patterns over the wider Caribbean

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

Heat Transport Pathways into the Arctic and their Connections to Surface Air Temperatures

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