Mrxin/FuXi_MultiLayer

Coupled Earth-System Forecast Sample and Temporal Downscaling Model

本仓库提供一个全球海陆气冰耦合预报样例，以及配套的时间降尺度模型权重。样例包含 2020-01-04 起报的初始场和 48 个集合成员、40 个预报时次的离线结果。

This repository provides a global atmosphere-land-ocean-sea-ice coupled forecast sample and a temporal downscaling model checkpoint. The sample is initialized on 2020-01-04 and contains 48 ensemble members with 40 forecast steps.

Repository Structure

.
├── model/
│   └── 8000_G.pth
└── data/
    ├── inp_data/
    │   └── era5_oras_20200103_20200104.zarr/
    └── oup_data/
        └── 20200104/
            ├── 0.nc
            ├── 1.nc
            ├── ...
            └── 47.nc

oup_data follows the existing artifact name and means output data.

Artifact Relationship

The files represent different stages of the forecasting workflow:

Two-day normalized initial state
  -> FuXi coupled forecast model
  -> 48-member daily coupled forecasts
  -> temporal downscaling model
  -> 6-hourly coupled forecasts

• inp_data is a normalized, model-ready initial state for the FuXi coupled forecast model.
• oup_data contains the stored daily FuXi coupled forecast results.
• model/8000_G.pth is the downstream temporal downscaling checkpoint. It is not the FuXi coupled forecast checkpoint.

Temporal Downscaling Model

model/8000_G.pth is a PyTorch OrderedDict state dictionary with 544 tensors. It uses a SwinIR-based temporal downscaling architecture.

Item	Value
Checkpoint size	129,715,342 bytes
Input channels	358 (175 x 2 + 8)
Output channels	700 (175 x 4)
Earth-system variables	175 packed variables
Output times	00, 06, 12, and 18 UTC
Embedding dimension	180
Block depths	[6, 6, 6, 6, 6, 6]
Attention heads	[6, 6, 6, 6, 6, 6]
Window size	8
MLP ratio	2
Residual connection	1conv

The model consumes two packed daily fields plus eight static/time features and predicts four 6-hourly residual fields. Architecture code and preprocessing logic are required before loading the state dictionary into a model instance.

Input Data

data/inp_data/era5_oras_20200103_20200104.zarr contains a normalized atmosphere-land-ocean-sea-ice initial-state sample.

Item	Value
Dates	2020-01-03 and 2020-01-04
Array shape	(2, 211, 721, 1440)
Dimensions	time, channel, lat, lon
Data type	float16
Spatial grid	Global 0.25 degrees
Latitude	90 to -90 degrees
Longitude	0 to 359.75 degrees
Compression	Blosc LZ4, level 5
Stored size	Approximately 479 MB

The channels cover pressure-level geopotential, temperature, wind and humidity; single-level atmospheric and land variables; ocean salinity, temperature and currents at depth; and sea-ice/ocean surface variables such as sea-ice thickness, sea-surface height, sea-ice concentration, and mixed-layer temperature.

Values are normalized model inputs rather than physical-unit observations. Channel order must be preserved.

Forecast Output

data/oup_data/20200104 contains one NetCDF file for each ensemble member.

Item	Value
Initialization time	2020-01-04
Ensemble members	48 (0.nc to 47.nc)
Forecast steps	40 daily steps
Shape per member	(1, 40, 211, 721, 1440)
Dimensions	time, step, channel, lat, lon
Data type	float32
Spatial grid	Global 0.25 degrees
Approximate size	35.05 GB per member; 1.68 TB in total

Because the complete output is large, download only the required ensemble members whenever possible. Repositories hosting these files should use Hugging Face Xet storage.

Loading the Files

Install the basic readers:

pip install torch xarray zarr netcdf4 huggingface_hub

Load the input sample:

import xarray as xr

initial_state = xr.open_zarr(
    "data/inp_data/era5_oras_20200103_20200104.zarr"
)
print(initial_state)

Load one forecast member:

import xarray as xr

member = xr.open_dataset("data/oup_data/20200104/0.nc")
print(member)

Inspect the checkpoint:

import torch

state_dict = torch.load("model/8000_G.pth", map_location="cpu")
print(f"Number of tensors: {len(state_dict)}")

Download from Hugging Face

Replace YOUR_ORG/YOUR_REPO with the published repository ID.

Download the checkpoint and input example:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="YOUR_ORG/YOUR_REPO",
    allow_patterns=["model/*", "data/inp_data/*"],
    local_dir="coupled_forecast_sample",
)

Download one forecast member:

from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="YOUR_ORG/YOUR_REPO",
    filename="data/oup_data/20200104/0.nc",
    local_dir="coupled_forecast_sample",
)

If this is published as a Hugging Face dataset repository, add repo_type="dataset" to the download calls.

Limitations

• This release contains one initialization date and is an example rather than a climatologically representative benchmark.
• The stored arrays are normalized/model-ready values and cannot be converted reliably to physical units without the matching normalization metadata.
• The temporal downscaling checkpoint is a state dictionary only; it requires the matching model definition and packing/preprocessing code.
• The FuXi checkpoint, complete inference pipeline, normalization constants, and static fields are not included in the current artifact set.
• The daily ensemble outputs are intermediate FuXi results, not direct outputs of 8000_G.pth.

Citation

Please replace this placeholder with the project publication before release:

@misc{coupled_earth_system_forecast,
  title  = {Coupled Earth-System Forecast Sample and Temporal Downscaling Model},
  author = {Project Team},
  year   = {2026}
}

License

No license is declared in this model/data card. A license covering the model weights, derived data, and upstream dependencies must be selected and added before public release.