Assimilation of heterogeneous measurements at different spatial scales in the Arctic ocean and Norwegian sea
The assimilation of ocean temperature measurements into ocean models provides useful insights on how to design heterogeneous ocean observation systems. In systems of this kind, ocean models can be complemented with multiscale operational assets such as satellites and in-situ unmanned vehicles. In th...
| Published in: | OCEANS 2022, Hampton Roads |
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| Main Authors: | , , |
| Format: | Book Part |
| Language: | English |
| Published: |
IEEE
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/11250/3055529 https://doi.org/10.1109/OCEANS47191.2022.9977376 |
| Summary: | The assimilation of ocean temperature measurements into ocean models provides useful insights on how to design heterogeneous ocean observation systems. In systems of this kind, ocean models can be complemented with multiscale operational assets such as satellites and in-situ unmanned vehicles. In this article, the authors simulate three different ocean model domains with horizontal resolutions of 20 km, 4 km and 800 m. The assimilated data sets are a global observation product including sea surface temperature, a vertical temperature profile measurement data set from the Norwegian Sea, and sea surface temperature measurements from an unmanned surface vehicle operating in the coastal waters of Frohavet (Central Norway). The key outcomes of the study suggest that global covering data sets should be assimilated in coarse model domains when available, while the intermediate and local data sets can be assimilated if they are covering areas of specific interest, and can be omitted otherwise. acceptedVersion |
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