Forecasting El Niño - Southern Oscillation (ENSO) events : a neural network approach
department or school: Science, Faculty of
department or school: Earth, Ocean and Atmospheric Sciences, Department of
data provider: DSpace
degree campus: UBCV
creator: Tangang, Fredolin T.
issued: 2009-04-20T23:36:33Z
issued: 1997
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Neural network models were used to seasonally forecast the tropical Pacific sea surface temperature anomalies (SSTA) in the Nino 3.4 region (6°S - 6°N, 120°W - 170°W). The inputs to the neural networks (i.e. the predictors) were the first seven wind stress empirical orthogonal function (EOF) modes of the tropical Pacific (20°S - 20°N, 120°E - 70°W) for four seasons and the Nino 3.4 SSTA persistence for the final season. The period 1952-1981 was used for training the neural network models, and the period 1982-1992 for forecast validation. At 6-month lead time, neural networks attained forecast skills comparable to the other El Nino - Southern Oscillation (ENSO) models. The results suggested that neural network models were viable for ENSO forecasting even at longer lead times of 9 to 12 months. It appeared that at these longer leads, the underlying relationship between the wind stress and Nino 3.4 SSTA became increasingly nonlinear. Two types of neural network models were further compared for forecasting the SSTA over several standard equatorial Pacific regions (Nino 3, 3.4, 3.5, 4, P2, P4 and P5). The first type used the sea level pressure (SLP) as predictor, while the second one used the wind stress. By ensemble averaging over 20 forecasts with random initial weights, the resulting forecasts were much less noisy than those in the earlier models. The wind stress models had better forecast skill at short lead times, while the SLP models generally had better skill at lead times of 6 months or longer. The western-central regions of the equatorial Pacific Ocean were best forecasted, with the Nino 4 region (6°S - 6°N, 160°E - 150°W) having the highest skiU, foUowed by Nino 3.4 and 3.5 (10°S - 6°N, 120°W - 180°), then Nino 3 (6°S - 6°N, 90° W - 150°W). The eastern boundary regions P4 (0 - 10°N, 80°W - 100°W) and P5 (10°S - 0, 80°W - 100° W) had much lower skill, while the western boundary region P2 (10°S - 10°N, 140°E - 180°) had no forecast skill. In an attempt to underst…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/7441
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note: F O R E C A S T I N G E L NINO - S O U T H E R N OSCILLATION (ENSO) E V E N T S : A N E U R A L N E T W O R K A P P R O A C H by Fredolin T. Tangang B. Sc. (Mathematics) University of Malaya, 1985 M . Sc. (Statistics) Iowa State University, 1988 A T H E S I S S U B M I T T E D IN P A R T I A L F U L F I L L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F D O C T O R O F P H I L O S O P H Y in T H E F A C U L T Y O F G R A D U A T E S T U D I E S E A R T H A N D O C E A N S C I E N C E S We accept this thesis as conforming to the required standard T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A July 1997 © Fredolin T. Tangang, 1997 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Earth and Ocean Sciences The University of British Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1Z1 Date: Abstract Neural network models were used to seasonally forecast the tropical Pacific sea surface temperature anomalies (SSTA) in the Nino 3.4 region (6°S - 6°N, 120°W - 170°W). The inputs to the neural networks (i.e. the predictors) were the first seven wind stress empirical orthogonal function (EOF) modes of the tropical Pacific (20°S - 20°N, 120°E -70°W) for four seasons and the Nino 3.4 SSTA persistence for the final season. The period 1952-1981 was used for training the neural network models, and the period 1982-1992 for forecast validation. At 6-month lead time, neural networks attained forecast skills comparable to the other E l Nino - Southern Oscillation (ENSO) models. The results sugge…
has type: Thesis/Dissertation
date issued: 1997-11
is shown at: 10.14288/1.0053339
language: eng
degree discipline: Oceanography
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/7441