Latent Space Data Assimilation by using Deep Learning
| Autor: | Mathis Peyron, Anthony Fillion, Gael Goret, Serge Gratton, Selime Gürol, Pierre Boudier, Victor Marchais |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Atmospheric Science Artificial neural network Computer science business.industry Deep learning Big data Kalman filter Numerical Analysis (math.NA) Prime (order theory) Machine Learning (cs.LG) Surrogate model Data assimilation FOS: Mathematics Errors-in-variables models Mathematics - Numerical Analysis Artificial intelligence business Algorithm |
| DOI: | 10.48550/arxiv.2104.00430 |
| Popis: | Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling, particularly at the time of big data where huge quantities of observations are available. Capitalizing on the ability of Neural Networks techniques for approximating the solution of PDE's, we incorporate Deep Learning (DL) methods into a DA framework. More precisely, we exploit the latent structure provided by autoencoders (AEs) to design an Ensemble Transform Kalman Filter with model error (ETKF-Q) in the latent space. Model dynamics are also propagated within the latent space via a surrogate neural network. This novel ETKF-Q-Latent (thereafter referred to as ETKF-Q-L) algorithm is tested on a tailored instructional version of Lorenz 96 equations, named the augmented Lorenz 96 system: it possesses a latent structure that accurately represents the observed dynamics. Numerical experiments based on this particular system evidence that the ETKF-Q-L approach both reduces the computational cost and provides better accuracy than state of the art algorithms, such as the ETKF-Q. Comment: 15 pages, 7 figures and 3 tables |
| Databáze: | OpenAIRE |
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