Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement-Reference-Cited by-同舟云学术

Global Ocean Forecast Accuracy Improvement Due to Optimal Sensor Placement

Published:2023-12-31 Issue: Volume: Page:1-21
ISSN:1681-1208
Container-title:Russian Journal of Earth Sciences
language:en
Short-container-title:

Author:

Turko Nikita¹,Lobashev Aleksandr²,Ushakov Konstantin¹³,Kaurkin Maksim¹,Kal'nickiy Leonid⁴,Semin Sergey⁵,Ibraev Rashit⁶¹³

Affiliation:

1. Institut okeanologii im. P.P. Shirshova RAN

2. Skolkovskiy institut nauki i tehnologiy

3. Moskovskiy fiziko-tehnicheskiy institut (NIU)

4. Arkticheskiy i antarkticheskiy nauchno-issledovatel'skiy institut

5. Institut problem bezopasnogo razvitiya atomnoy energetiki RAN

6. Institut vychislitel'noy matematiki im. G.I. Marchuka RAN

Abstract

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.

Publisher

Geophysical Center of the Russian Academy of Sciences

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