Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks.
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| Title: | Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks. |
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| Authors: | Soldatenko, S. A.1 (AUTHOR) soldatenko@aari.ru, Angudovich, Ya. I.1,2 (AUTHOR) |
| Source: | Russian Meteorology & Hydrology. Feb2025, Vol. 50 Issue 2, p91-101. 11p. |
| Subject Terms: | *Machine learning, *Long short-term memory, *Nonlinear dynamical systems, *Numerical weather forecasting, *Artificial intelligence |
| Abstract: | The paper examines a methodological approach to simulating nonlinear atmospheric dynamics. The approach implies constructing a surrogate (replacement) model of a physical object (process) based on machine learning. For illustrative purposes, the surrogate model is built for the conceptual model of the coupled ocean–atmosphere system, in which the atmospheric component is represented by a low-dimensional nonlinear dynamic system, and the harmonic oscillator model is used as the oceanic component. The surrogate model is based on unidirectional and bidirectional long short-term memory neural networks (LSTM and BiLSTM, respectively). Nine LSTMs and one BiLSTM, whose structures were determined experimentally, are analyzed to evaluate their ability to predict complex dynamics and chaotic regime in the examined model on time intervals from 5 to 10 days. The best forecast accuracy was obtained using the BiLSTM. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | The paper examines a methodological approach to simulating nonlinear atmospheric dynamics. The approach implies constructing a surrogate (replacement) model of a physical object (process) based on machine learning. For illustrative purposes, the surrogate model is built for the conceptual model of the coupled ocean–atmosphere system, in which the atmospheric component is represented by a low-dimensional nonlinear dynamic system, and the harmonic oscillator model is used as the oceanic component. The surrogate model is based on unidirectional and bidirectional long short-term memory neural networks (LSTM and BiLSTM, respectively). Nine LSTMs and one BiLSTM, whose structures were determined experimentally, are analyzed to evaluate their ability to predict complex dynamics and chaotic regime in the examined model on time intervals from 5 to 10 days. The best forecast accuracy was obtained using the BiLSTM. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10683739 |
| DOI: | 10.3103/S1068373925020013 |
