Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning

• URL: http://arxiv.org/abs/2002.11167v1
• Date: Tue, 25 Feb 2020 20:43:42 GMT
• Title: Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning
• Authors: Ashesh Chattopadhyay, Adam Subel, Pedram Hassanzadeh
• Abstract summary: We propose a data-driven SP (DD-SP) to make weather/climate modeling computationally affordable. With the same computational cost, DD-SP substantially outperforms LR, and is better than DD-P, particularly when scale separation is lacking. DD-SP is much cheaper than SP, yet its accuracy is the same in reproducing long-term statistics and often comparable in short-term forecasting.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To make weather/climate modeling computationally affordable, small-scale processes are usually represented in terms of the large-scale, explicitly-resolved processes using physics-based or semi-empirical parameterization schemes. Another approach, computationally more demanding but often more accurate, is super-parameterization (SP), which involves integrating the equations of small-scale processes on high-resolution grids embedded within the low-resolution grids of large-scale processes. Recently, studies have used machine learning (ML) to develop data-driven parameterization (DD-P) schemes. Here, we propose a new approach, data-driven SP (DD-SP), in which the equations of the small-scale processes are integrated data-drivenly using ML methods such as recurrent neural networks. Employing multi-scale Lorenz 96 systems as testbed, we compare the cost and accuracy (in terms of both short-term prediction and long-term statistics) of parameterized low-resolution (LR), SP, DD-P, and DD-SP models. 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. DD-SP is much cheaper than SP, yet its accuracy is the same in reproducing long-term statistics and often comparable in short-term forecasting. We also investigate generalization, finding that when models trained on data from one system are applied to a system with different forcing (e.g., more chaotic), the models often do not generalize, particularly when the short-term prediction accuracy is examined. But we show that transfer-learning, which involves re-training the data-driven model with a small amount of data from the new system, significantly improves generalization. Potential applications of DD-SP and transfer-learning in climate/weather modeling and the expected challenges are discussed.

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