Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2405.13180 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909486797553664 |
|---|---|
| author | Adrian, Melissa Sanz-Alonso, Daniel Willett, Rebecca |
| author_facet | Adrian, Melissa Sanz-Alonso, Daniel Willett, Rebecca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet Adrian, Melissa Sanz-Alonso, Daniel Willett, Rebecca Signal Processing Machine Learning Chaotic Dynamics Atmospheric and Oceanic Physics Applications 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. |
| title | Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet |
| topic | Signal Processing Machine Learning Chaotic Dynamics Atmospheric and Oceanic Physics Applications |
| url | https://arxiv.org/abs/2405.13180 |