Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
This addresses the challenge of maintaining forecast accuracy in weather prediction using machine learning surrogates, though it appears incremental as it applies existing data assimilation methods to a specific model.
This paper tackles the problem of inaccurate long-term forecasts from data-driven weather surrogate models by integrating them with partial, noisy observations through data assimilation. The results show that filtering estimates remain accurate over a year-long window and provide effective initial conditions for forecasting tasks.
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 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.