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Keyword: data-driven parameterization ×Clear all

1 claim from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning

   Chattopadhyay, Subel and Hassanzadeh · Journal of Advances in Modeling Earth Systems · 2020

   The paper proposes data-driven super-parameterization, using machine learning for small-scale processes, and compares it with other schemes on Lorenz 96 systems, including how well models generalise and benefit from transfer learning.

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   1. UncheckedIn multiscale Lorenz 96 tests, data-driven super-parameterization beat low-resolution models at equal cost, and beat data-driven parameterization when scales overlap.“We show that with the same computational cost, DD-SP substantially outperforms LR, and is better than DD-P, particularly when scale separation is lacking.”