Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet

• URL: http://arxiv.org/abs/2405.13180v1
• Date: Tue, 21 May 2024 20:06:12 GMT
• Title: Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
• Authors: Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett,
• Abstract summary: This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations. We empirically demonstrate and theoretically justify that, despite the long-time instability of the surrogates, filtering estimates can remain accurate in the long-time horizon.
• Score: 10.773673764125439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations. We empirically demonstrate and theoretically justify that, despite the long-time instability of the surrogates and the sparsity of the observations, filtering estimates can remain accurate in the long-time horizon. As a case study, we integrate FourCastNet, a state-of-the-art weather surrogate model, within a variational data assimilation framework using partial, noisy ERA5 data. Our results show that filtering estimates remain accurate over a year-long assimilation window and provide effective initial conditions for forecasting tasks, including extreme event prediction.

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