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OneScience-Group/deep-learning-weather-prediction

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Model Card

DLWP-CS

Model Overview

DLWP-CS employs cubed-sphere convolutional neural networks for global weather forecasting, mitigating the geometric distortions that conventional latitude-longitude grids suffer near the poles.

Paper: Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

https://doi.org/10.1029/2020MS002109

Model Description

This directory provides an independent PyTorch structural smoke implementation based on the paper and official code, featuring cubed-sphere cross-face padding, convolutions, a simplified U-Net, capped leaky ReLU, and autoregressive inference. It is not a reproduction of the paper's experimental architecture or ERA5 training.

Use Cases

Scenario	Description
Cubed-Sphere Architecture Research	Verify six-face adjacency, flipping, and convolution.
Local Rapid Verification	Run training and rollout with fake data.
ERA5 Global Weather Forecasting	Subsequently interface with ERA5 data processed via Tempest-Remap.

Usage

1. OneCode

Click to experience intelligent one-click AI4S programming

2. Manual Installation & Usage

Hardware Requirements

—CPU can run the current minimum configuration.
—GPU is recommended for training on real data.

Download the Model Package

bash

hf download OneScience-Group/deep-learning-weather-prediction --local-dir ./deep-learning-weather-prediction
cd deep-learning-weather-prediction

Set Up the Runtime Environment

DCU Environment

bash

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

bash

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

Data

The default training uses a deterministic fake Dataset from model/dataset.py and requires no additional download; each sample has shape [C,6,H,W], and the validation set uses an independent seed.

Training

bash

python scripts/train.py

The script performs multi-epoch training, validation, learning rate scheduling, and early stopping:

1.Generates fake data of shape [C,6,H,W] by index;
2.Validates the six-face topology and capped leaky ReLU;
3.Executes U-Net forward/backward, MSE loss, and optimization per epoch;
4.Computes validation loss on an independent fake validation Dataset;
5.Saves latest/best checkpoints and history; supports --resume.

bash

python scripts/train.py --epochs 10
python scripts/train.py --resume weight/training/latest.pth --epochs 20

Checkpoint outputs:

text

weight/model.pth
weight/training/latest.pth
weight/training/best.pth
weight/training/history.json

Inference

bash

python scripts/inference.py

Inference results:

text

result/prediction.pt
result/target.pt
result/inference.json

Result Inspection

bash

python scripts/result.py

The result script produces result/metrics.json and result/comparison.png. The current rmse and spatial_acc serve only as connectivity checks on fake tensors; they do not apply the denormalization, latitude-weighted area averaging, cubed-sphere inverse mapping, or daily climatological anomaly computation required by the paper.

Paper vs. Current Implementation I/O

Item	Paper DLWP-CS	Current Smoke Implementation
Dynamic Input	4 variables at `t-6h,t`, 8 channels	2-channel single state with no physical semantics
Auxiliary Input	Solar radiation, land-sea mask, topography	Not implemented
Spatial Grid	`[6,48,48]` cubed sphere	`[6,8,8]` fake grid
Output	4 variables at `t+6h,t+12h`, 8 channels	2-channel output of the same shape
Network / Training	Two-level U-Net, combined loss over two autoregressive steps	Single-level simplified U-Net, multi-epoch single-step MSE training
Analysis	Physical-unit, latitude-weighted RMSE/ACC	Smoke metrics without physical units

The complete execution flow is train.py -> inference.py -> result.py. The fake Dataset preserves the cubed-sphere input shape but does not represent a continuous weather time series; the model package is distributed without local training weights or result/ artifacts. A production mode further requires an ERA5 Dataset implementation, CS48 remapping, 4 dynamic variables, auxiliary fields, normalization statistics, and the paper's two-step iterative training loss.

Real Data

Real-data training requires ERA5 variables Z500, Z1000, 300–700 hPa geopotential thickness, and 2 m temperature, along with solar radiation, a land-sea mask, topography, and Tempest-Remap offline remapping weights.

OneScience Official 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 & License

—Official Code: https://github.com/jweyn/DLWP-CS
—This directory is an independent adaptation based on the paper and official structure, licensed under GPL-3.0.