High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning
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...
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ftmdpi:oai:mdpi.com:/2073-4441/15/8/1454/ 2023-08-20T04:08:13+02:00 High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning Charles Galdies Roberta Guerra agris 2023-04-07 application/pdf https://doi.org/10.3390/w15081454 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/w15081454 https://creativecommons.org/licenses/by/4.0/ Water; Volume 15; Issue 8; Pages: 1454 ocean acidification ocean carbonate system dissolved inorganic carbon total alkalinity pH North Atlantic spatiotemporal variability earth observation deep learning Text 2023 ftmdpi https://doi.org/10.3390/w15081454 2023-08-01T09:36:43Z 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 at a high spatiotemporal resolution. Further methodological improvements are being suggested. Text North Atlantic Ocean acidification MDPI Open Access Publishing Water 15 8 1454 |
institution |
Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
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 Charles Galdies Roberta Guerra 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 |
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 at a high spatiotemporal resolution. Further methodological improvements are being suggested. |
format |
Text |
author |
Charles Galdies Roberta Guerra |
author_facet |
Charles Galdies Roberta Guerra |
author_sort |
Charles Galdies |
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 |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/w15081454 |
op_coverage |
agris |
genre |
North Atlantic Ocean acidification |
genre_facet |
North Atlantic Ocean acidification |
op_source |
Water; Volume 15; Issue 8; Pages: 1454 |
op_relation |
https://dx.doi.org/10.3390/w15081454 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/w15081454 |
container_title |
Water |
container_volume |
15 |
container_issue |
8 |
container_start_page |
1454 |
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1774720370887098368 |