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A Bayesian Structural Time Series Approach for Predicting Red Sea Temperatures
- Source :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 1996-2009 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Sea surface temperature (SST) is a leading factor impacting coral reefs and causing bleaching events in the Red Sea. A long-term prediction of temperature patterns with an estimate of uncertainty is thus essential for environment management of the Red Sea ecosystem. In this work, we build a data-driven Bayesian structural time series model and show its effectiveness in predicting future SST seasons with a high accuracy, and identifying the main predictive factors of future SST variability among a large number of factors, including regional SST and large-scale climate indices. The modeling scheme proposed here applies an efficient hierarchical clustering to identify interconnected subregions that display distinct SST variability over the Red Sea, and a Markov Chain Monte Carlo algorithm to simultaneously select the main predictors while the time series model is being trained. In particular, numerical results indicate that monthly SST can be reliably predicted for five months ahead.
- Subjects :
- Atmospheric Science
010504 meteorology & atmospheric sciences
factor selection
0208 environmental biotechnology
Bayesian probability
Geophysics. Cosmic physics
02 engineering and technology
01 natural sciences
symbols.namesake
red sea
Markov chain Monte Carlo (MCMC)
Computers in Earth Sciences
Time series
TC1501-1800
0105 earth and related environmental sciences
geography
geography.geographical_feature_category
QC801-809
Time series approach
Markov chain Monte Carlo
Coral reef
020801 environmental engineering
Hierarchical clustering
Markov chain monte carlo algorithm
Ocean engineering
Sea surface temperature
Climatology
symbols
Environmental science
hierarchical clustering
predictive modeling
Bayesian structural time series (BSTS)
Subjects
Details
- Language :
- English
- ISSN :
- 21511535
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Accession number :
- edsair.doi.dedup.....cc235605b64da13c3f098155f94e1b5a