FuXi-RTM Inference Dataset

This dataset contains the inference results of the FuXi-RTM family of models — global medium-range weather forecasting systems with explicit shortwave radiation prediction. We release per–initialization-time forecast bundles produced by three model variants, plus the RRTMG-generated shortwave radiation training/test set used to fit the MLRTM neural radiation emulator.

The companion inference script (infer_onnx_release.py) is included so users can reproduce the rollouts on their own initial conditions.

Repository Layout

qiusheng/FuXi-RTM/
├── README.md
├── infer_onnx_release.py        # ONNX Runtime inference script
├── data_util.py                 # Data I/O helpers used by the inference script
├── test.onnx                    # Small example model file for quick smoke tests
│
├── RTM_base_6h_tar_gz/          # → Inference results from FuXi-base
├── RTM_mlp_6h_tar_gz/           # → Inference results from FuXi-base+ (MLP head)
├── RTM_exp_re4_6h_tar_gz/       # → Inference results from FuXi-RTM (full model)
└── RTM_tar_gz/                  # → RRTMG shortwave outputs (training/test data for MLRTM)

Model → Folder Mapping

The three forecast directories share an identical layout (same 80 initialization times, same internal structure) so they can be compared side-by-side with simple folder-swapping in your evaluation pipeline.

Folder	Source Model	Description
RTM_base_6h_tar_gz/	FuXi-base	Baseline FuXi global forecasting model. Predicts standard atmospheric prognostic variables only (no radiation outputs). Serves as the reference baseline for ablations.
RTM_mlp_6h_tar_gz/	FuXi-base+ (MLP)	FuXi-base extended with a lightweight MLP head that diagnoses surface shortwave radiation fluxes from the predicted atmospheric state. A simpler, cheaper alternative to the full RTM model.
RTM_exp_re4_6h_tar_gz/	FuXi-RTM	The full FuXi-RTM model with an integrated Radiative Transfer Module (RTM). Jointly predicts atmospheric prognostics and physically-consistent shortwave radiation fluxes. This is the headline model of the paper.
RTM_tar_gz/	(physics-model output, not a NN forecast)	RRTMG shortwave radiation fields (ssrd, swdflx, swdflxc, swuflx, swuflxc) for 2017–2018. Generated offline by running the RRTMG radiative-transfer scheme; used as the training and test data for the MLRTM neural emulator.

File Naming Convention

Each forecast file is named after its initialization datetime (起报时刻), not a forecast step:

YYYYMMDDHH.tar.gz   →   one full forecast trajectory launched at that init time

For example, 2024010112.tar.gz contains the entire multi-step rollout launched from 2024-01-01 12:00 UTC — i.e. all lead times (T+6h, T+12h, …, up to the configured --total_step) for that single initialization. To evaluate a model at, say, the 72-hour lead time, you read the corresponding step inside each per-init archive and pair it against the verifying analysis.

Sampling Strategy

Per-init forecast bundles are ~9 GB each. To stay within the Hugging Face free-tier storage budget, we release a temporally-thinned subset:

• 80 initialization times per folder
• One init per day, every 5 days, at 12 UTC
• Date range: 2024-01-01 → 2025-02-19

Need denser cadence or the full archive? Open a discussion on this dataset page or contact the authors — we are happy to release more on demand.

Inner Data Structure of Each .tar.gz

After extraction, each archive expands to a per-init directory whose contents follow the layout produced by infer_onnx_release.py:

<init_time>/                              # e.g. 2024010112/
├── 1.zarr/        ← lead step 1 (T+6h  if --hour_interval 6)
├── 2.zarr/        ← lead step 2 (T+12h)
├── 3.zarr/        ← lead step 3 (T+18h)
├── ...
└── N.zarr/        ← lead step N

• Format: Zarr v2 directory stores (one per lead step). Use the --save_type nc flag in the inference script if you prefer NetCDF.
• Tensor dimensions: (time, channel, lat, lon)
• Temporal cadence: 6-hourly (matches the _6h_ in the folder name)
• Multi-member ensembles (when produced with --total_member > 1) follow the pattern m<member_id>_<step>.zarr.

RTM_tar_gz/ — RRTMG training/test data for MLRTM

This directory contains shortwave radiation fields produced by running the RRTMG (Rapid Radiative Transfer Model for GCMs) radiative-transfer scheme offline. It is the training and test set for the MLRTM neural radiation emulator, not a forecasting result.

Files are organized by variable and year:

File	Variable	Description
ssrd_<year>.tar.gz	ssrd	Surface solar radiation downwards (single-level, all-sky)
swdflx_<year>.tar.gz	swdflx	Shortwave downward flux profile, all-sky
swdflxc_<year>.tar.gz	swdflxc	Shortwave downward flux profile, clear-sky
swuflx_<year>.tar.gz	swuflx	Shortwave upward flux profile, all-sky
swuflxc_<year>.tar.gz	swuflxc	Shortwave upward flux profile, clear-sky

• Years released: 2017 (training) and 2018 (testing).
• All-sky / clear-sky pairs let the emulator learn cloud radiative effects as a residual.

How to Use

1. Download

pip install huggingface_hub
hf download --repo-type dataset qiusheng/FuXi-RTM \
    --include "RTM_exp_re4_6h_tar_gz/2024010112.tar.gz" \
    --local-dir ./fuxi_rtm

Or pull the whole dataset (≈ several TB; not recommended unless you really need it):

hf download --repo-type dataset qiusheng/FuXi-RTM --local-dir ./fuxi_rtm

2. Extract a forecast bundle

mkdir -p forecasts && tar -xzf fuxi_rtm/RTM_exp_re4_6h_tar_gz/2024010112.tar.gz -C forecasts/

3. Read with xarray

import xarray as xr
ds = xr.open_zarr("forecasts/2024010112/24.zarr")   # 24 × 6h = T+144h forecast
print(ds)

Reproducing the Inference

The included infer_onnx_release.py is the exact script used to produce these archives.

Dependencies

pip install numpy xarray pandas onnxruntime-gpu  # or onnxruntime (CPU)

Basic usage

python infer_onnx_release.py \
    --model <model.onnx> \
    --input <era5_initial_conditions/> \
    --save_dir <output_dir> \
    --total_step 400 \
    --hour_interval 6

Key arguments

Argument	Default	Description
--model	exps/RTM_onnx/base_tf/model/test.onnx	Path to ONNX model file
--input	datasets/era5.rtm.02_25.6h.c109.new3/	Input data path (NetCDF/Zarr)
--save_dir	eval/save	Output directory
--save_type	zarr	nc or zarr
--device	cuda	cuda or cpu
--dtype	fp32	fp16 or fp32
--batch_size	1	Number of parallel processes (>1 enables multi-process mode)
--total_step	400	Total prediction steps
--total_member	1	Number of ensemble members
--hour_interval	6	Time interval (hours)
--time_splite	20240101 20250531	Time range
--init_time_hour	0 12	Initialization hours

Execution modes

• Single-process (--batch_size 1): sequential rollout — useful for debugging or limited GPU memory.
• Multi-process (--batch_size N): launches N independent worker processes, each loading the model and running one ensemble member in parallel.

Input data requirements

• Format: Zarr or NetCDF.
• Dimensions: (time, channel, lat, lon).
• Must include mean.nc and std.nc files for input normalization / output denormalization.

Citation

If you use this dataset, please cite the FuXi-RTM paper (citation block to be added on publication).

License

Released under the MIT license.

Contact

Questions, denser-cadence requests, or issues with the data — please open a discussion on this dataset page.