High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning
first_pagesettingsOrder Article Reprints Open AccessArticle High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning by Charles Galdies 1,*ORCID andRoberta Guerra 2,3ORCID 1 Institute of Earth Systems, University of Malta, MSD 2080 Msida, Malta 2...
Published in: | Water |
---|---|
Main Authors: | , |
Other Authors: | |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/11585/924721 https://doi.org/10.3390/w15081454 https://www.mdpi.com/2073-4441/15/8/1454 |
id |
ftunibolognairis:oai:cris.unibo.it:11585/924721 |
---|---|
record_format |
openpolar |
spelling |
ftunibolognairis:oai:cris.unibo.it:11585/924721 2024-01-28T10:07:31+01:00 High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning Galdies, Charles Guerra, Roberta Galdies, Charle Guerra, Roberta 2023 ELETTRONICO https://hdl.handle.net/11585/924721 https://doi.org/10.3390/w15081454 https://www.mdpi.com/2073-4441/15/8/1454 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000979441300001 volume:15 issue:8 firstpage:1 lastpage:28 numberofpages:28 journal:WATER https://hdl.handle.net/11585/924721 doi:10.3390/w15081454 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85156198704 https://www.mdpi.com/2073-4441/15/8/1454 info:eu-repo/semantics/openAccess ocean acidification ocean carbonate system dissolved inorganic carbon total alkalinity pH North Atlantic spatiotemporal variability earth observation deep learning info:eu-repo/semantics/article 2023 ftunibolognairis https://doi.org/10.3390/w15081454 2024-01-03T17:36:30Z first_pagesettingsOrder Article Reprints Open AccessArticle High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning by Charles Galdies 1,*ORCID andRoberta Guerra 2,3ORCID 1 Institute of Earth Systems, University of Malta, MSD 2080 Msida, Malta 2 Department of Physics and Astronomy (DIFA), Alma Mater Studiorum—Università di Bologna, 40126 Bologna, Italy 3 Interdepartmental Research Centre for Environmental Sciences (CIRSA), University of Bologna, 48123 Ravenna, Italy * Author to whom correspondence should be addressed. Water 2023, 15(8), 1454; https://doi.org/10.3390/w15081454 Received: 25 February 2023 / Accepted: 3 April 2023 / Published: 7 April 2023 (This article belongs to the Topic Water Management in the Era of Climatic Change) Download Browse Figures Versions Notes Abstract This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution in the North Atlantic region (Long. −61.00° to −50.04° W; Lat. 24.99° to 34.96° N). The in situ reference dataset that was used for this study provided discrete underway measurements of DIC, TA, and pH collected by M/V Equinox in the North Atlantic Ocean. A unique list of co-temporal and co-located global daily environmental drivers derived from independent sources (using satellite remote sensing, model reanalyses, empirical algorithms, and depth soundings) were collected for this study at the highest possible spatial resolution (0.04° × 0.04°). The resulting ANN-estimated DIC, TA, and pH obtained by deep learning shows a high correspondence when verified against observations. This study demonstrates how a select number of geophysical information derived from EO and model reanalysis data can be used to estimate and understand the spatiotemporal variability of the oceanic carbonate system ... Article in Journal/Newspaper North Atlantic Ocean acidification IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) Water 15 8 1454 |
institution |
Open Polar |
collection |
IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
op_collection_id |
ftunibolognairis |
language |
English |
topic |
ocean acidification ocean carbonate system dissolved inorganic carbon total alkalinity pH North Atlantic spatiotemporal variability earth observation deep learning |
spellingShingle |
ocean acidification ocean carbonate system dissolved inorganic carbon total alkalinity pH North Atlantic spatiotemporal variability earth observation deep learning Galdies, Charles Guerra, Roberta High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning |
topic_facet |
ocean acidification ocean carbonate system dissolved inorganic carbon total alkalinity pH North Atlantic spatiotemporal variability earth observation deep learning |
description |
first_pagesettingsOrder Article Reprints Open AccessArticle High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning by Charles Galdies 1,*ORCID andRoberta Guerra 2,3ORCID 1 Institute of Earth Systems, University of Malta, MSD 2080 Msida, Malta 2 Department of Physics and Astronomy (DIFA), Alma Mater Studiorum—Università di Bologna, 40126 Bologna, Italy 3 Interdepartmental Research Centre for Environmental Sciences (CIRSA), University of Bologna, 48123 Ravenna, Italy * Author to whom correspondence should be addressed. Water 2023, 15(8), 1454; https://doi.org/10.3390/w15081454 Received: 25 February 2023 / Accepted: 3 April 2023 / Published: 7 April 2023 (This article belongs to the Topic Water Management in the Era of Climatic Change) Download Browse Figures Versions Notes Abstract This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution in the North Atlantic region (Long. −61.00° to −50.04° W; Lat. 24.99° to 34.96° N). The in situ reference dataset that was used for this study provided discrete underway measurements of DIC, TA, and pH collected by M/V Equinox in the North Atlantic Ocean. A unique list of co-temporal and co-located global daily environmental drivers derived from independent sources (using satellite remote sensing, model reanalyses, empirical algorithms, and depth soundings) were collected for this study at the highest possible spatial resolution (0.04° × 0.04°). The resulting ANN-estimated DIC, TA, and pH obtained by deep learning shows a high correspondence when verified against observations. This study demonstrates how a select number of geophysical information derived from EO and model reanalysis data can be used to estimate and understand the spatiotemporal variability of the oceanic carbonate system ... |
author2 |
Galdies, Charle Guerra, Roberta |
format |
Article in Journal/Newspaper |
author |
Galdies, Charles Guerra, Roberta |
author_facet |
Galdies, Charles Guerra, Roberta |
author_sort |
Galdies, Charles |
title |
High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning |
title_short |
High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning |
title_full |
High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning |
title_fullStr |
High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning |
title_full_unstemmed |
High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning |
title_sort |
high resolution estimation of ocean dissolved inorganic carbon, total alkalinity and ph based on deep learning |
publishDate |
2023 |
url |
https://hdl.handle.net/11585/924721 https://doi.org/10.3390/w15081454 https://www.mdpi.com/2073-4441/15/8/1454 |
genre |
North Atlantic Ocean acidification |
genre_facet |
North Atlantic Ocean acidification |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000979441300001 volume:15 issue:8 firstpage:1 lastpage:28 numberofpages:28 journal:WATER https://hdl.handle.net/11585/924721 doi:10.3390/w15081454 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85156198704 https://www.mdpi.com/2073-4441/15/8/1454 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/w15081454 |
container_title |
Water |
container_volume |
15 |
container_issue |
8 |
container_start_page |
1454 |
_version_ |
1789335327411798016 |