2019/03/18 by Julien Brajard, Anastase Charantonis, Brajard, Julien +3
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Meteorological Phenomena and Simulations #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1903.07358
openalex publication_date 2019/03/18 · openalex created_date 2019/03/22 · openalex updated_date 2026/07/28
In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but sometimes can be observed. This paper proposes a methodology to produce a hybrid model combining a physical-based model (forecasting the well-known processes) with a neural-net model trained from observations (forecasting the remaining processes). The approach is applied to a shallow-water model in which the forcing, dissipative and diffusive terms are assumed to be unknown. We show that the hybrid model is able to reproduce with great accuracy the unknown terms (correlation close to 1). For long term simulations it reproduces with no significant difference the mean state, the kinetic energy, the potential energy and the potential vorticity of the system. Lastly it is able to function with new forcings that were not encountered during the training phase of the neural network.