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Study of The Relationship Between El Niño–Southern Oscillation (ENSO) And Extreme Heat in Taiwan

Tang, Kean LU (2022) In Master's Theses in Mathematical Sciences MASM02 20221
Mathematical Statistics
Abstract
El Niño-Southern Oscillation (ENSO) drives global weather and can be forecasted with a 6-12 months lead time. It’s also widely believed that there’s a relationship between phases of ENSO and extreme weather events such as extreme heat or precipitation.

This thesis focuses on the relationship between Oceanic Nino Index (ONI) in Nino region 3.4 and monthly temperature data in Taiwan, from 1980 to 2019. The temperature data is obtained from the NOAA Global Historical Climatology Network.

First an EM-like algorithm is used to complete the weather data matrix with missing values. Then an SVD decomposition separates the data matrix into temporal patterns and spatial patterns. The temporal patterns are modelled by a long short-term memory... (More)
El Niño-Southern Oscillation (ENSO) drives global weather and can be forecasted with a 6-12 months lead time. It’s also widely believed that there’s a relationship between phases of ENSO and extreme weather events such as extreme heat or precipitation.

This thesis focuses on the relationship between Oceanic Nino Index (ONI) in Nino region 3.4 and monthly temperature data in Taiwan, from 1980 to 2019. The temperature data is obtained from the NOAA Global Historical Climatology Network.

First an EM-like algorithm is used to complete the weather data matrix with missing values. Then an SVD decomposition separates the data matrix into temporal patterns and spatial patterns. The temporal patterns are modelled by a long short-term memory (LSTM) neural network with ONI as an external input signal. The spatial patterns are modelled separately by Universal Kriging with selected covariates. The space-time residuals are obtained and modelled by a Gaussian random field with semi-parametric non-stationary spatial covariance structures. Markov Chain Monte Carlo method is used together with Bayesian estimation to compute parameters in the non-stationary spatial covariance structures. Finally, extreme temperatures can be predicted by combining spatial-temporal patterns and prediction of the time series. Predictions with and without ONI were evaluated showing improved performance when using future ONI values. (Less)
Please use this url to cite or link to this publication:
author
Tang, Kean LU
supervisor
organization
course
MASM02 20221
year
type
H2 - Master's Degree (Two Years)
subject
keywords
ENSO, SVD, Gaussian random field, LSTM, Universal Kriging, Markov Chain Monte Carlo
publication/series
Master's Theses in Mathematical Sciences
report number
LUNFMS-3111-2022
ISSN
1404-6342
other publication id
2022:E52
language
English
id
9091875
date added to LUP
2022-08-15 17:57:59
date last changed
2022-08-15 18:31:11
@misc{9091875,
  abstract     = {{El Niño-Southern Oscillation (ENSO) drives global weather and can be forecasted with a 6-12 months lead time. It’s also widely believed that there’s a relationship between phases of ENSO and extreme weather events such as extreme heat or precipitation. 

This thesis focuses on the relationship between Oceanic Nino Index (ONI) in Nino region 3.4 and monthly temperature data in Taiwan, from 1980 to 2019. The temperature data is obtained from the NOAA Global Historical Climatology Network.

First an EM-like algorithm is used to complete the weather data matrix with missing values. Then an SVD decomposition separates the data matrix into temporal patterns and spatial patterns. The temporal patterns are modelled by a long short-term memory (LSTM) neural network with ONI as an external input signal. The spatial patterns are modelled separately by Universal Kriging with selected covariates. The space-time residuals are obtained and modelled by a Gaussian random field with semi-parametric non-stationary spatial covariance structures. Markov Chain Monte Carlo method is used together with Bayesian estimation to compute parameters in the non-stationary spatial covariance structures. Finally, extreme temperatures can be predicted by combining spatial-temporal patterns and prediction of the time series. Predictions with and without ONI were evaluated showing improved performance when using future ONI values.}},
  author       = {{Tang, Kean}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Study of The Relationship Between El Niño–Southern Oscillation (ENSO) And Extreme Heat in Taiwan}},
  year         = {{2022}},
}