NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation

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NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation
Yuan Gao^† , Hao Wu^{† ‡} ,Fan Xu, Yanfei Xiang, Ruijian Gou, Ruiqi Shu, Qingsong Wen, Xian Wu^* , Kun Wang^* , Xiaomeng Huang^*
(† Equal contribution, ‡ Project lead and technical guidance, * Corresponding author)

Abstract: Long-term, high-fidelity simulation of slow-changing physical systems, such as the ocean and climate, presents a fundamental challenge in scientific computing. Traditional autoregressive machine learning models often fail in these tasks as minor errors accumulate and lead to rapid forecast degradation. To address this problem, we propose NeuralOM, a general neural operator framework designed for simulating complex, slow-changing dynamics. NeuralOM's core consists of two key innovations: (1) a Progressive Residual Correction Framework that decomposes the forecasting task into a series of fine-grained refinement steps, effectively suppressing long-term error accumulation; and (2) a Physics-Guided Graph Network whose built-in adaptive messaging mechanism explicitly models multi-scale physical interactions, such as gradient-driven flows and multiplicative couplings, thereby enhancing physical consistency while maintaining computational efficiency. We validate NeuralOM on the challenging task of global Subseasonal-to-Seasonal (S2S) ocean simulation. Extensive experiments demonstrate that NeuralOM not only surpasses state-of-the-art models in forecast accuracy and long-term stability, but also excels in simulating extreme events. For instance, at a 60-day lead time, NeuralOM achieves a 13.3% lower RMSE compared to the best-performing baseline, offering a stable, efficient, and physically-aware paradigm for data-driven scientific computing. Code link: https://github.com/YuanGao-YG/NeuralOM.

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News 🚀

• 2026.01.22: Training codes are released.
• 2025.11.08: NeuralOM is accepted by AAAI 2026.
• 2025.07.28: Inference codes for global ocean forecasting are released.
• 2025.06.01: Inference codes for global ocean simulation are released.
• 2025.05.27: Paper is released on arXiv.

Notes

The intact project is avilable on Hugging Face, you can find the pretrained models, test data on Hugging Face and put them in the same location.

Quick Start

Installation

• cuda 11.8

# git clone this repository
git clone https://github.com/YuanGao-YG/NeuralOM.git
cd NeuralOM

# create new anaconda env
conda env create -f environment.yml
conda activate neuralom

Inference for Global Ocean Simulation

Preparing the test data as follows:

./data/
|--test
|  |--2020.h5
|--mean_s_t_ssh.npy
|--std_s_t_ssh.npy
|--climate_mean_s_t_ssh.npy
|--land_mask.h5

Run the following script:

sh inference_simulation.sh

Inference for Global Ocean Forecasting

Preparing the test data as follows:

./data/
|--test
|  |--2020.h5
|--test_atmos
|  |--2020.h5
|--mean_s_t_ssh.npy
|--std_s_t_ssh.npy
|--mean_atmos.npy
|--std_atmos.npy
|--climate_mean_s_t_ssh.npy
|--land_mask.h5

Run the following script:

sh inference_forecasting.sh

Training

1. Prepare Data

Preparing the train, valid, and test data as follows:

./data/
|--train
|  |--1993.h5
|  |--1994.h5
|  |--......
|  |--2016.h5
|  |--2017.h5
|--valid
|  |--2018.h5
|  |--2019.h5
|--test
|  |--2020.h5
|--mean_s_t_ssh.npy
|--std_s_t_ssh.npy
|--climate_mean_s_t_ssh.npy
|--land_mask.h5

For data ranging from 1993 to 2020, each h5 file includes a key named 'fields' with the shape [T, C, H, W] (T=365/366, C=97, H=361, W=720). The order of all variables is as follows:

var_idex = {
    "SSS": 0, "S2": 1, "S5": 2, "S7": 3, "S11": 4, "S15": 5, "S21": 6, "S29": 7, "S40": 8, "S55": 9, "S77": 10, "S92": 11, "S109": 12,
    "S130": 13, "S155": 14, "S186": 15, "S222": 16, "S266": 17, "S318": 18, "S380": 19, "S453": 20, "S541": 21, "S643": 22,
    "U0": 23, "U2": 24, "U5": 25, "U7": 26, "U11": 27, "U15": 28, "U21": 29, "U29": 30, "U40": 31, "U55": 32, "U77": 33, "U92": 34, "U109": 35,
    "U130": 36, "U155": 37, "U186": 38, "U222": 39, "U266": 40, "U318": 41, "U380": 42, "U453": 43, "U541": 44, "U643": 45,
    "V0": 46, "V2": 47, "V5": 48, "V7": 49, "V11": 50, "V15": 51, "V21": 52, "V29": 53, "V40": 54, "V55": 55, "V77": 56, "V92": 57, "V109": 58,
    "V130": 59, "V155": 60, "V186": 61, "V222": 62, "V266": 63, "V318": 64, "V380": 65, "V453": 66, "V541": 67, "V643": 68,
    "SST": 69, "T2": 70, "T5": 71, "T7": 72, "T11": 73, "T15": 74, "T21": 75, "T29": 76, "T40": 77, "T55": 78, "T77": 79, "T92": 80, "T109": 81,
    "T130": 82, "T155": 83, "T186": 84, "T222": 85, "T266": 86, "T318": 87, "T380": 88, "T453": 89, "T541": 90, "T643": 91,
    "SSH": 92,
   
}

Regarding the meaning of abbreviated variables, for example, "SSS" means sea surface salinity and "S2" means salinity at depth 2 m.

2. Multi-node Multi-GPU Training

• Training for the base model

(1) 1-step pretraining

sh train_base_model.sh

(2) Modify ./train_base_model.sh file and ./config/Model.yaml file.

For instance, if you intent to finetune ckpt from 1-step ckpt (the start training time is 20250501-000000) with 2-step finetune for 10 eppchs, you can set run_num='20250501-000000', multi_steps_finetune=2, finetune_max_epochs=10, lr: 1E-6. Please note that using a small learning rate (lr) to finetune model may contribute to convergence, you can adjust it according to your total batch size.

If you intent to finetune ckpt from 2-step ckpt (the start training time is 20250501-000000) with 3-step finetune for 10 eppchs, you can set run_num='20250501-000000', multi_steps_finetune=3, finetune_max_epochs=10, lr: 1E-6. In our setup, we conduct 6-step finetuning as an example.

(3) Run the following script for multi-step finetuning:

sh train_base_model.sh

• Training for the residual model

(1) Modify ./train_residual_model.sh file and ./config/Model.yaml file.

For instance, if you intent to correct the base model (the start training time is 20250501-000000), you can set run_num='20250501-000000', lr: 1E-3. And you can adjust the lr according to your total batch size.

(2) Run the following script:

sh train_residual_model.sh

Performance

Global Ocean Simulation

Global Ocean Forecasting and Extreme Event Assessment

Citation

@article{gao2025neuralom,
  title={NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation},
  author={Gao, Yuan and Shu, Ruiqi and Wu, Hao and Xu, Fan and Xiang, Yanfei and Gou, Ruijian and Wen, Qingsong and Wu, Xian and Wang, Kun and Huang, Xiaomeng},
  journal={arXiv preprint arXiv:2505.21020},
  year={2025}
}

Acknowledgement

We appreciate the following open-sourced repositories for their valuable code base:

https://github.com/NVlabs/FourCastNet

https://github.com/NVIDIA/physicsnemo

If you have any questions, please contact [email protected], [email protected].