GLOBAL OCEAN FORECAST ACCURACY IMPROVEMENT DUE TO OPTIMAL SENSOR PLACEMENT

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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.

About the authors

N. A. Turko

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: nikitaturko@yandex.ru
ORCID iD: 0000-0002-8039-9087
graduate student of physical and mathematical sciences 2019-2023

A. A. Lobashev

Skolkovo Institute of Science and Technology

ORCID iD: 0000-0002-9522-9996

K. V. Ushakov

Shirshov Institute of Oceanology, Russian Academy of Sciences; Moscow Institute of Physics and Technology

ORCID iD: 0000-0002-8454-9927

M. N. Kaurkin

Shirshov Institute of Oceanology, Russian Academy of Sciences

ORCID iD: 0000-0002-0921-3630

L. Yu. Kalnitskii

Arctic and Antarctic Research Institute

ORCID iD: 0009-0005-4023-2257

S. A. Semin

Nuclear Safety Institute, Russian Academy of Sciences

ORCID iD: 0000-0001-8079-168X
candidate of physical and mathematical sciences

R. A. Ibraev

Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences; Shirshov Institute of Oceanology, Russian Academy of Sciences; Moscow Institute of Physics and Technology

ORCID iD: 0000-0002-9099-4541

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Copyright (c) 2023 Турко Н.A., Лобашев А.A., Ушаков К.V., Кауркин М.N., Кальницкий Л.Y., Сёмин С.A., Ибраев Р.A.

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