Nonlinear extensions of linear inverse models under memoryless or persistent random forcing
This study extends the linear inverse modeling (LIM) framework to nonlinear settings by presenting White-nLIM and Colored-nLIM, statistics-based empirical methods that construct approximate stochastic systems incorporating quadratic deterministic dynamics with either memoryless Gaussian white noise...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-08-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/ds1j-fx3v |
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| Summary: | This study extends the linear inverse modeling (LIM) framework to nonlinear settings by presenting White-nLIM and Colored-nLIM, statistics-based empirical methods that construct approximate stochastic systems incorporating quadratic deterministic dynamics with either memoryless Gaussian white noise or persistent Ornstein-Uhlenbeck colored noise. Beyond the evident improvements over linear models, Colored-nLIM offers a robust approach to parameter estimation and statistical modeling under persistent stochastic forcing. Together with White-nLIM, these methods provide a systematic framework to assess the role of noise persistence in inverse modeling. Applications to the Lorenz 63 system and a simplified El Niño-Southern Oscillation model demonstrate their potential to capture chaotic behavior and climate variability. |
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| ISSN: | 2643-1564 |