Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.12054 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912197306744832 |
|---|---|
| author | Garcia, Pierre Larroche, Inès Pesnec, Amélie Bull, Hannah Archambault, Théo Moschos, Evangelos Stegner, Alexandre Charantonis, Anastase Béréziat, Dominique |
| author_facet | Garcia, Pierre Larroche, Inès Pesnec, Amélie Bull, Hannah Archambault, Théo Moschos, Evangelos Stegner, Alexandre Charantonis, Anastase Béréziat, Dominique |
| contents | We present ORCAst, a multi-stage, multi-arm network for Operational high-Resolution Current forecAsts over one week. Producing real-time nowcasts and forecasts of ocean surface currents is a challenging problem due to indirect or incomplete information from satellite remote sensing data. Entirely trained on real satellite data and in situ measurements from drifters, our model learns to forecast global ocean surface currents using various sources of ground truth observations in a multi-stage learning procedure. Our multi-arm encoder-decoder model architecture allows us to first predict sea surface height and geostrophic currents from larger quantities of nadir and SWOT altimetry data, before learning to predict ocean surface currents from much more sparse in situ measurements from drifters. Training our model on specific regions improves performance. Our model achieves stronger nowcast and forecast performance in predicting ocean surface currents than various state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ORCAst: Operational High-Resolution Current Forecasts Garcia, Pierre Larroche, Inès Pesnec, Amélie Bull, Hannah Archambault, Théo Moschos, Evangelos Stegner, Alexandre Charantonis, Anastase Béréziat, Dominique Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics We present ORCAst, a multi-stage, multi-arm network for Operational high-Resolution Current forecAsts over one week. Producing real-time nowcasts and forecasts of ocean surface currents is a challenging problem due to indirect or incomplete information from satellite remote sensing data. Entirely trained on real satellite data and in situ measurements from drifters, our model learns to forecast global ocean surface currents using various sources of ground truth observations in a multi-stage learning procedure. Our multi-arm encoder-decoder model architecture allows us to first predict sea surface height and geostrophic currents from larger quantities of nadir and SWOT altimetry data, before learning to predict ocean surface currents from much more sparse in situ measurements from drifters. Training our model on specific regions improves performance. Our model achieves stronger nowcast and forecast performance in predicting ocean surface currents than various state-of-the-art methods. |
| title | ORCAst: Operational High-Resolution Current Forecasts |
| topic | Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2501.12054 |