OneScience-Group/WRF-ML

WRF-ML

Model Introduction

WRF-ML bridges WRF v4.3 and Python machine-learning parameterizations. This reproduction focuses on the recommended Model D, using a bidirectional LSTM to emulate RRTMG shortwave and longwave outputs.

Paper: WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer
https://doi.org/10.5194/gmd-16-199-2023

Model Description

The model was proposed by researchers at Alibaba Group's Damo Academy. It was trained with atmospheric-column inputs and RRTMG outputs generated by WRF simulations, with 57 vertical levels per sample. Through layout adaptation and a synchronous request-response protocol, it supports shortwave and longwave flux and heating-rate emulation as an online WRF physical parameterization.

Use Cases

Use Case	Description
Radiation emulation	Emulate shortwave and longwave fluxes and heating rates.
WRF parameterization	Validate atmospheric-column batching and output layout restoration.
Offline evaluation	Compute RMSE for six radiation outputs.
Coupling protocol	Demonstrate synchronous blocking request, inference, and response.
ModelScope/OneCode execution	Validate structured data, training, inference, probabilistic precipitation metrics, and visualization in ModelScope or OneCode.
Multi-GPU training	Start multi-process training through torchrun.

Usage Instructions

Download

hf download OneScience-Group/WRF-ML --local-dir ./WRF-ML
cd WRF-ML

Environment Dependencies

Hardware Requirements

• A GPU or DCU is recommended.
• A CPU can run the default small-sample connectivity configuration.
• DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first.

DCU Environment

# Activate DTK and Conda first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai

GPU Environment

# Activate Conda first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai

Synthetic Data

The paper uses a 190x170 horizontal domain, 57 vertical levels, and WRF-RRTMG columns sampled every 30 minutes. Synthetic data preserve 57 levels, WRF layout conversion, and six output semantics while reducing column count, hidden width, and epochs; the ten inputs are explicitly an engineering ledger because the paper does not enumerate all input variables. Results verify the workflow only and do not represent paper performance.

python scripts/fake_data.py

Training

python scripts/train.py
torchrun --nproc_per_node=2 --nnodes=1 --master_addr="localhost" --master_port=29500 scripts/train.py

The default synthetic run jointly optimizes 57-level radiation profiles and boundary fluxes, and both single-process and two-process DDP training have been verified. It produces one recoverable Model-D-style checkpoint and records normalized mean squared error. Training results are saved to:

result/checkpoints/wrf_ml.pt
result/training/metrics.json

Weights

The paper's code and data are archived at https://doi.org/10.5281/zenodo.7407487. This reproduction does not link a pretrained checkpoint because a separately licensed official weight artifact was not confirmed.

Inference

python scripts/inference.py

Inference restores the checkpoint, runs the synchronous layout-adapted coupler, and produces 57-level profiles and boundary fluxes. Profile and boundary output shapes are [6,57,4] and [6,2], respectively, and both have passed finite-value checks. Inference results are saved to:

result/output/predictions.npz

Evaluation

python scripts/result.py

Evaluation reports RMSE for shortwave and longwave fluxes, heating rates, and boundary shortwave fluxes and creates a vertical-profile comparison. All metrics are finite, and the PNG has passed format and non-empty-pixel checks. Evaluation results are saved to:

result/evaluation/metrics.json
result/evaluation/comparison.png

Official OneScience Information

Platform	OneScience	OneSkills
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation and License

This repository is an independent engineering reproduction of the public WRF-ML specifications, with code licensed under the Apache License 2.0.

The original paper is licensed under CC BY 4.0; the paper, WRF, RRTMG, ONNX Runtime, and related data remain subject to their respective licenses and terms.