We lifted 5 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| openearthlab/orca | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| get_int_from_env | Ran | openearthlab/orca/trainer/args.py pointer only (licence: NONE) · get_code("54b511369641045b") |
| get_ip | Ran | openearthlab/orca/trainer/args.py pointer only (licence: NONE) · get_code("148c0707ea29b99a") |
| get_window_size | Ran | openearthlab/orca/model/utils.py pointer only (licence: NONE) · get_code("aad0ea11cb13d353") |
| window_partition_2d | Ran | openearthlab/orca/model/utils.py pointer only (licence: NONE) · get_code("0d7a6ac9b6aa2b65") |
| window_reverse_2d | Ran | openearthlab/orca/model/utils.py pointer only (licence: NONE) · get_code("5515e24e407e6b0d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Accurate ocean dynamics modeling is crucial for enhancing understanding of ocean circulation, predicting climate variability, and tackling challenges posed by climate change. Despite improvements in traditional numerical models, predicting global ocean variability over multi-year scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), the first data-driven 3D ocean model for seasonal to decadal prediction of global ocean circulation. ORCA-DL accurately simulates three-dimensional ocean dynamics and outperforms state-of-the-art dynamical models in capturing extreme events, including El Niño-Southern Oscillation and upper ocean heatwaves. This demonstrates the high potential of data-driven models for efficient and accurate global ocean forecasting. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.15412")
get_code_for_paper("2405.15412")
have("2405.15412")
Connect an agent — have() is free.