Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
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...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
21.05.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
DOI: | 10.48550/arxiv.2405.13180 |