A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index
This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric co...
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ftdoajarticles:oai:doaj.org/article:024ed29651524a4bb7ddca3a018cfeff 2024-09-30T14:39:22+00:00 A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index Md Wahiduzzaman Alea Yeasmin 2024-08-01T00:00:00Z https://doi.org/10.3390/atmos15080987 https://doaj.org/article/024ed29651524a4bb7ddca3a018cfeff EN eng MDPI AG https://www.mdpi.com/2073-4433/15/8/987 https://doaj.org/toc/2073-4433 doi:10.3390/atmos15080987 2073-4433 https://doaj.org/article/024ed29651524a4bb7ddca3a018cfeff Atmosphere, Vol 15, Iss 8, p 987 (2024) North Atlantic oscillation generalised additive model deep learning ocean–atmosphere interaction Meteorology. Climatology QC851-999 article 2024 ftdoajarticles https://doi.org/10.3390/atmos15080987 2024-09-02T15:34:38Z This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric conditions over the Northern Hemisphere. The NAO has a prominent impact on winter weather patterns in North America, Europe, and to some extent, Asia. This impact has significant ramifications for civilization, as well as for marine, freshwater, and terrestrial ecosystems, and food chains. Accurate predictions of the surface NAO hold significant importance for society in terms of energy consumption planning and adaptation to severe winter conditions, such as winter wind and snowstorms, which can result in property damage and disruptions to transportation networks. Moreover, it is crucial to improve climate forecasts in order to bolster the resilience of food systems. This would enable producers to quickly respond to expected changes and make the required modifications, such as adjusting their food output or expanding their product range, in order to reduce potential hazards. The forecast centres prioritise and actively research the predictability and variability of the NAO. Nevertheless, it is increasingly evident that conventional analytical methods and prediction models that rely solely on scientific methodologies are inadequate in comprehensively addressing the transdisciplinary dimension of NAO variability. This includes a comprehensive view of research, forecasting, and social ramifications. This study introduces a new framework that combines sophisticated Big Data analytic techniques and forecasting tools using a generalised additive model to investigate the fluctuations of the NAO and the interplay between the ocean and atmosphere. Additionally, it explores innovative approaches to analyze the socio-economic response associated with these phenomena using text mining tools, specifically modern deep learning ... Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Atmosphere 15 8 987 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
North Atlantic oscillation generalised additive model deep learning ocean–atmosphere interaction Meteorology. Climatology QC851-999 |
spellingShingle |
North Atlantic oscillation generalised additive model deep learning ocean–atmosphere interaction Meteorology. Climatology QC851-999 Md Wahiduzzaman Alea Yeasmin A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index |
topic_facet |
North Atlantic oscillation generalised additive model deep learning ocean–atmosphere interaction Meteorology. Climatology QC851-999 |
description |
This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric conditions over the Northern Hemisphere. The NAO has a prominent impact on winter weather patterns in North America, Europe, and to some extent, Asia. This impact has significant ramifications for civilization, as well as for marine, freshwater, and terrestrial ecosystems, and food chains. Accurate predictions of the surface NAO hold significant importance for society in terms of energy consumption planning and adaptation to severe winter conditions, such as winter wind and snowstorms, which can result in property damage and disruptions to transportation networks. Moreover, it is crucial to improve climate forecasts in order to bolster the resilience of food systems. This would enable producers to quickly respond to expected changes and make the required modifications, such as adjusting their food output or expanding their product range, in order to reduce potential hazards. The forecast centres prioritise and actively research the predictability and variability of the NAO. Nevertheless, it is increasingly evident that conventional analytical methods and prediction models that rely solely on scientific methodologies are inadequate in comprehensively addressing the transdisciplinary dimension of NAO variability. This includes a comprehensive view of research, forecasting, and social ramifications. This study introduces a new framework that combines sophisticated Big Data analytic techniques and forecasting tools using a generalised additive model to investigate the fluctuations of the NAO and the interplay between the ocean and atmosphere. Additionally, it explores innovative approaches to analyze the socio-economic response associated with these phenomena using text mining tools, specifically modern deep learning ... |
format |
Article in Journal/Newspaper |
author |
Md Wahiduzzaman Alea Yeasmin |
author_facet |
Md Wahiduzzaman Alea Yeasmin |
author_sort |
Md Wahiduzzaman |
title |
A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index |
title_short |
A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index |
title_full |
A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index |
title_fullStr |
A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index |
title_full_unstemmed |
A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index |
title_sort |
generalised additive model and deep learning method for cross-validating the north atlantic oscillation index |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/atmos15080987 https://doaj.org/article/024ed29651524a4bb7ddca3a018cfeff |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Atmosphere, Vol 15, Iss 8, p 987 (2024) |
op_relation |
https://www.mdpi.com/2073-4433/15/8/987 https://doaj.org/toc/2073-4433 doi:10.3390/atmos15080987 2073-4433 https://doaj.org/article/024ed29651524a4bb7ddca3a018cfeff |
op_doi |
https://doi.org/10.3390/atmos15080987 |
container_title |
Atmosphere |
container_volume |
15 |
container_issue |
8 |
container_start_page |
987 |
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1811641959011319808 |