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Quasi-objective Nonlinear Principal Component Analysis and applications to the atmosphere

NonLinear Principal Component Analysis (NLPCA) using three-hidden-layer
feed-forward neural networks can produce solutions that over-fit the data and
are non-unique. These problems have been dealt with by subjective methods
during the network training. This study shows that these problems are intrinsic
due to the three-hidden-layer architecture. A simplified two-hidden-layer
feed-forward neural network that has no encoding layer and no bottleneck and
output biases is proposed. This new, compact NLPCA model alleviates these
problems without employing the subjective methods and is called
quasi-objective.

The compact NLPCA is applied to the zonal winds observed at seven pressure
levels between 10 and 70 hPa in the equatorial stratosphere to represent the
Quasi-Biennial Oscillation (QBO) and investigate its variability and structure.
The two nonlinear principal components of the dataset offer a clear picture of
the QBO. In particular, their structure shows that the QBO phase consists of a
predominant 28.4-month cycle that is modulated by an 11-year cycle and a
longer-period cycle. The significant difference in variability of the winds
between cold and warm seasons and the tendency for a seasonal synchronization
of the QBO phases are well captured. The one-dimensional NLPCA approximation of
the dataset provides a better representation of the QBO than the classical
principal component analysis and a better description of the asymmetry of the
QBO between westerly and easterly shear zones and between their transitions.

The compact NLPCA is then applied to the Arctic Oscillation (AO) index and
aforementioned zonal winds to investigate the relationship of the AO with the
QBO. The NLPCA of the AO index and zonal-winds dataset shows clearly that, of
covariation of the two oscillations, the phase defined by the two nonlinear
principal components progresses with a predominant 28.4-month periodicity, plus
the 11-year and longer-period modulations. Large positive values of the AO
index occur when westerlies prevail near the middle and upper levels of the
equatorial stratosphere. Large negative values of the AO index arise when
easterlies occupy over half the layer of the equatorial stratosphere.

  1. http://hdl.handle.net/2429/234
Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/234
Date05 1900
CreatorsLu, Beiwei
PublisherUniversity of British Columbia
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation

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