OneScience-Group/RF-ClimParam

RF-ClimParam

Model Introduction

RF-ClimParam learns unresolved convection, cloud microphysics, radiation, turbulent diffusion, and surface-flux processes from high-resolution atmospheric simulations and provides stable subgrid parameterizations for coarse climate models at multiple horizontal resolutions. Its primary uses are multi-resolution climate simulation, analysis of parameterization scale dependence, and reconstruction of precipitation climate statistics.

Paper: Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
https://arxiv.org/abs/2001.03151

Model Description

The method reproduced by RF-ClimParam was proposed by Janni Yuval and Paul A. O'Gorman at the Massachusetts Institute of Technology. The paper trains random forests with coarse-grained states, instantaneous physical tendencies, turbulent diffusivity, and surface fluxes from a three-dimensional high-resolution System for Atmospheric Modeling aquaplanet simulation. The model is suitable for multi-resolution atmospheric subgrid parameterization, coarse-resolution climate simulation, and evaluation of mean and extreme precipitation statistics.

Use Cases

Use Case	Description
Multi-resolution parameterization	Predict column tendencies and diffusion quantities separately at x4/x8/x16/x32.
Joint multi-output regression	Preserve cross-variable and cross-level output means in one tree leaf without splitting outputs.
Offline engineering evaluation	Compute per-scale, per-output R2 and RMSE over all four complete coarse-grid fields.
Online-coupling proxy	Evaluate zonal-mean 3 h precipitation and extremes on an additional native x32 18×48 coarse grid.
ModelScope/OneCode execution	Validate data generation, training, inference, evaluation, visualization, and checkpoint workflows in ModelScope or OneCode.
Multi-GPU training	Launch distributed data-parallel training with torchrun.

Usage Instructions

1.OneCode

Experience intelligent, one-click AI4S programming through the OneCode online environment:

Try intelligent, one-click AI4S programming

2. Download and Installation

hf download OneScience-Group/RF-ClimParam --local-dir ./RF-ClimParam
cd RF-ClimParam

Environment Dependencies

Hardware Requirements

• A GPU or DCU is recommended; the random forest itself runs on CPU with NumPy.
• A CPU supports the complete default small-sample connectivity test.
• DCU users must install DTK first. DTK 25.04.2 or later, or the OneScience-recommended version matching the cluster, is recommended.

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

Training Data

python scripts/fake_data.py

This repository uses a small number of structured synthetic atmospheric-column samples to validate the engineering workflow and retains complete 144×360, 72×180, 36×90, and 18×45 coarse-grid snapshots for x4, x8, x16, and x32. The data preserve all 48 levels and the real 145→144 and 62→17 random-forest interfaces while reducing only the number of snapshots, sampled training columns, and trees. Synthetic temperature, moisture, condensate, wind, and flux fields contain spatial and vertical relationships and validate multi-resolution parameterization, training, inference, and evaluation only; they do not represent the official SAM distribution or paper performance.

Training

For single-device training, use:

python scripts/train.py

For multi-GPU training, use:

torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train.py

Training samples a small number of atmospheric columns from the complete spatial field at each resolution and fits the two joint multi-output random forests separately. It produces model parameters and training-sample statistics for all four resolutions, saved to:

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

Trained Weights

This repository does not include weights under weight/. The original paper provides random-forest estimators at different resolutions; refer to the authors' OSF archive for the released weights and model artifacts: https://doi.org/10.17605/OSF.IO/36YPT.

Inference

python scripts/inference.py

Inference results include reference targets and subgrid-process predictions at the x4, x8, x16, and x32 resolutions. They also contain complete spatial fields, diagnosed precipitation, grid-location information for each scale, and native x32-grid results. All numerical results are saved to result/output/predictions.npz.

Evaluation and Visualization

python scripts/result.py

Evaluation results include subgrid-process prediction performance at all four resolutions and an online proxy for coarse-grid precipitation. The visualization compares results across resolutions and shows the zonal distributions of target and predicted precipitation. Structured results and the auxiliary figure are saved to result/evaluation/metrics.json and result/evaluation/comparison.png. Synthetic-data results validate the engineering workflow only and do not represent the paper's formal SAM online performance.

Official OneScience Information

Platform	OneScience Main Repository	Skills Repository
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 RF-ClimParam specifications.

Use of this repository's code, official model weights, and data remains subject to the licenses and terms of their respective projects.