Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement
The paper examines the impact of sensor placement on the accuracy of the Global ocean state forecasting. A comparison is made between various sensor placement methods, including the arrangement obtained by the Concrete Autoencoder method. To evaluate how sensor placement affects forecast accuracy, a...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Russian Academy of Sciences, The Geophysical Center
2023-12-01
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| Series: | Russian Journal of Earth Sciences |
| Subjects: | |
| Online Access: | http://doi.org/10.2205/2023ES000883 |
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| _version_ | 1849246100329857024 |
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| author | Turko Nikita Lobashev Aleksandr Ushakov Konstantin Viktorovich Kaurkin Maksim Nikolaevich Kal'nickiy Leonid Yur'evich Semin Sergey Ibraev Rashit |
| author_facet | Turko Nikita Lobashev Aleksandr Ushakov Konstantin Viktorovich Kaurkin Maksim Nikolaevich Kal'nickiy Leonid Yur'evich Semin Sergey Ibraev Rashit |
| author_sort | Turko Nikita |
| collection | DOAJ |
| description | The paper examines the impact of sensor placement on the accuracy of the Global ocean state forecasting. A comparison is made between various sensor placement methods, including the arrangement obtained by the Concrete Autoencoder method. To evaluate how sensor placement affects forecast accuracy, a simulation was conducted that emulates a scenario where the initial state of the global ocean significantly deviates from the ground truth. In the experiment, initial conditions for the ocean and ice model were altered, while atmospheric forcing was retained from the control experiment. Subsequently, the model was integrated with the assimilation of data about the ground truth state at the sensor locations. The results showed that the sensor placement obtained using deep learning methods is superior in forecast accuracy to other considered arrays with a comparable number of sensors. |
| format | Article |
| id | doaj-art-5bf9bcbebd0045a8a3dd9ee059f134d5 |
| institution | Kabale University |
| issn | 1681-1208 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Russian Academy of Sciences, The Geophysical Center |
| record_format | Article |
| series | Russian Journal of Earth Sciences |
| spelling | doaj-art-5bf9bcbebd0045a8a3dd9ee059f134d52025-08-20T03:58:36ZengRussian Academy of Sciences, The Geophysical CenterRussian Journal of Earth Sciences1681-12082023-12-0123612110.2205/2023ES000883Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor PlacementTurko Nikita0https://orcid.org/0000-0002-8039-9087Lobashev Aleksandr1https://orcid.org/0000-0002-9522-9996Ushakov Konstantin Viktorovich2https://orcid.org/0000-0002-8454-9927Kaurkin Maksim Nikolaevich3https://orcid.org/0000-0002-0921-3630Kal'nickiy Leonid Yur'evich4https://orcid.org/0009-0005-4023-2257Semin Sergey5https://orcid.org/0000-0001-8079-168XIbraev Rashit6https://orcid.org/0000-0002-9099-4541Institut okeanologii im. P.P. Shirshova RANSkolkovskiy institut nauki i tehnologiyInstitut okeanologii im. P.P. Shirshova RANInstitut okeanologii im. P.P. Shirshova RANArkticheskiy i antarkticheskiy nauchno-issledovatel'skiy institutInstitut problem bezopasnogo razvitiya atomnoy energetiki RANInstitut vychislitel'noy matematiki im. G.I. Marchuka RANThe paper examines the impact of sensor placement on the accuracy of the Global ocean state forecasting. A comparison is made between various sensor placement methods, including the arrangement obtained by the Concrete Autoencoder method. To evaluate how sensor placement affects forecast accuracy, a simulation was conducted that emulates a scenario where the initial state of the global ocean significantly deviates from the ground truth. In the experiment, initial conditions for the ocean and ice model were altered, while atmospheric forcing was retained from the control experiment. Subsequently, the model was integrated with the assimilation of data about the ground truth state at the sensor locations. The results showed that the sensor placement obtained using deep learning methods is superior in forecast accuracy to other considered arrays with a comparable number of sensors.http://doi.org/10.2205/2023ES000883operational forecast Global ocean optimal sensor placement Concrete Autoencoder data assimilation |
| spellingShingle | Turko Nikita Lobashev Aleksandr Ushakov Konstantin Viktorovich Kaurkin Maksim Nikolaevich Kal'nickiy Leonid Yur'evich Semin Sergey Ibraev Rashit Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement Russian Journal of Earth Sciences operational forecast Global ocean optimal sensor placement Concrete Autoencoder data assimilation |
| title | Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement |
| title_full | Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement |
| title_fullStr | Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement |
| title_full_unstemmed | Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement |
| title_short | Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement |
| title_sort | global ocean forecast accuracy improvement due to optimal sensor placement |
| topic | operational forecast Global ocean optimal sensor placement Concrete Autoencoder data assimilation |
| url | http://doi.org/10.2205/2023ES000883 |
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