🌟 FengWu-GHR: Kilometer-scale Medium-range Global Weather Forecasting

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🚀 Paper

Title:
FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting

We now support two version of FengWu-GHR:

Model	Resolution	Precision	Size	Pretrained Model	Demo
FengWu-GHR (meta_model_0.25°)	721x1440, 0.25°	fp16	9.0GB	OneDrive	fengwu_ghr_inference_25km.py
FengWu-GHR (0.09° )	2001x4000, 0.09°	fp16	20 GB	hdd_new:nwp_predictions/GHR_Checkpoint	fengwu_ghr_cfg_74v_0.09_torch_v2.py

News

2024/09/03 Fix the checkerboard effect in fengwu_ghr_v2 ⭐

2024/07/12 add FengWu-GHR (full model) onnx-fp16 model ⭐

2024/04/04 add FengWu-GHR (Meta Model) onnx-fp16 model and its demos ⭐

2024/03/22 init project ⭐

📌 Highlights

• 🌍 Kilometer-scale resolution: First global weather forecasting model at 0.09°.
• ⚡ Novel Approach: Leverages a pretrained low-resolution model to achieve high-resolution forecasts.
• 📊 Real-world performance: Excels in both station observations and extreme event scenarios.

Getting started

1. Clone the code and prepare environment (if necessary) using the following command:

$ git clone https://github.com/taohan10200/FengWu-GHR.onnx.git
$ conda create -n fengwu_ghr python=3.10 -y
$ conda activate fengwu_ghr
$ python3 -m pip install -r requirements.txt

2. Download the Pretrained ONNX model.

Download meta_model_0.25 and unzip it in $FengWu-GHR.onnx/onnx/ (the root of this repository).

3. Get the input data (initial field)

We support the input with grib or netcdf format, which waives a complex data prepareness. What you should do is to organize your data as a packed grib file after getting the initial field for ECMWF or other data sources. Below is a sample we provided:

• For 0.09° analysis data: Download sample input from here.

import xarray as xr
input_data = xr.open_dataset('./data/input/analysis/2024-07-08T18:00:00.grib')
input_data
Out[1]: 
<xarray.Dataset>
Dimensions:        (latitude: 1801, longitude: 3600, isobaricInhPa: 13)
Coordinates:
  * latitude       (latitude) float32 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0
  * longitude      (longitude) float32 0.0 0.1 0.2 0.3 ... 359.7 359.8 359.9
  * isobaricInhPa  (isobaricInhPa) int32 1000 925 850 700 600 ... 200 150 100 50
Data variables: (12/16)
    v10            (latitude, longitude) float32 ...
    u10            (latitude, longitude) float32 ...
    v100           (latitude, longitude) float32 ...
    u100           (latitude, longitude) float32 ...
    t2m            (latitude, longitude) float32 ...
    tcc            (latitude, longitude) float32 ...
    ...             ...
    z              (isobaricInhPa, latitude, longitude) float32 ...
    q              (isobaricInhPa, latitude, longitude) float32 ...
    u              (isobaricInhPa, latitude, longitude) float32 ...
    v              (isobaricInhPa, latitude, longitude) float32 ...
    t              (isobaricInhPa, latitude, longitude) float32 ...
    w              (isobaricInhPa, latitude, longitude) float32 ...

Note: The requirement for tp6h in the initial field is the accumulated precipitation over the past six hours from the analysis time. It can be derived from the predictions intilized at the last time.

• For 0.25° ERA5 data：We provide the download and preprocessed script.

python era5_downloader.py --st='2024-11-04T00:00:00' --et='2024-1
1-04T00:00:00'  --local_root='./data/input/era5'

4. Organize your project as following structure.

$ FengWu-GHR.onnx/
├── data/
│   ├── demos
│   ├── input
│   └── output
├── onnx/   
│     └── meta_model_0.25/
│           ├── block_0.onnx
│           ├── ...
│           └── encoder.onnx
│     └── ghr_0.09/
│           ├── block_0.onnx
│           ├── ...
│           └── encoder.onnx
├── fengwu_ghr_inference_9km.py
├── fengwu_ghr_inference_25km.py
├── LICENSE
└── README.md

5. Inference

Single GPU

1. Run 0.09° Torch Model

python -u fengwu_ghr_inference_torch.py \
    --timestamp=2024-09-13T00:00:00 \
    --config=config/fengwu_ghr_cfg_88v_0.09_torch_v2.py \
    --gpu=0

# Try more options
$ python -u fengwu_ghr_inference_torch.py --help

Multi GPU (for slurm)

sh run.sh ai4earth inference \
    fengwu_ghr_inference_torch_dist.py \  # Inference script
    config/fengwu_ghr_cfg_74v_0.09_torch.py \  # Configuration file
    4 \  # Number of GPUs
    2023-10-11 \  # Start date
    2024-03-31 \  # End date
    spot  # Mode (e.g., spot or reserved)

# Try more options
$ python -u fengwu_ghr_inference_torch_dist.py --help

6. Plot demo

• Without ground truth

python -u plot_demo_fc_only.py --timestamp=2024-11-04T00:00:00 --dataset=analysis --inference_steps=40

• With analysis as GT

# for analysis initial field
python -u plot_demo_gif.py --timestamp=2024-09-13T06:00:00 --dataset=analysis --inference_steps=40

• With ERA5 as GT

python -u plot_demo_gif.py --timestamp=2024-07-08T00:00:00 --dataset=era5 --inference_steps=40

Reminder: After runing this script, the forecast results will be saved as netcdf format in data/output/${timestamp}. Feel free to change the inference_steps and save_cfg in fengwu_ghr_cfg.py for rollout length and saved variables.

inference_steps = 40 # one step is 6 hour interval
save_cfg = dict(
    save_path='./data/output' ,   
    variables_list =[
        'z_1000','z_850','z_500','z_100','z_50',
        'q_1000','q_850','q_500','q_100','q_50',
        'u_1000','u_850','u_500','u_100','u_50',
        'v_1000','v_850','v_500','v_100','v_50',
        't_1000','t_850','t_500','t_100','t_50',
        'v10','u10','v100', 'u100', 't2m','tcc', 'sp','tp6h', 'msl'
        ]
    )

5. Notes

1. This demo is runing under the given input samples. If you want to inference under the forcing of other initial fields, please download them from ERA5 pressure-level dataset and ERA5 single-level dataset.
2. The input and forecast of precipitation are 6-hour accumulations. However, ERA5 provides it with hourly accumulation, please process it with care if you are using your own data.
3. This version of FengWu-GHR (Meta Model) receives input data with a shape of 74x721x1440, where 74 represents 74 atmospheric features. The latitude range is the [90N, 90S], and the longitude range is [-180, 180]. The first 65 variables are pressure-level variables in the order of ['z', 'q', 'u', 'v', 't'], where each variable has 13 levels: [1000., 925., 850., 700., 600., 500., 400., 300., 250., 200., 150., 100., 50.]. The remain 9 variable are surface variables:['v10','u10','v100', 'u100', 't2m','tcc', 'sp','tp6h', 'msl']. Refer to fengwu_ghr_cfg.py for more information.

Demos

The following are demos of 10-days lead forecasting after running above script:

Demo 1: Some Surface Variables: 10 meter wind (v10), 100 meter wind (v100), 2 meter temperature (t2m), surface pressure (sp), 6-hour precipitation (tp6h), mean sea-level pressure (msl)

Demo 2: Geopotential

Demo 3: Specific Humidity

Demo 4: U Component of Wind

Demo 5: V Component of Wind

Demo 6: Temperature

License and attribution

The copyright of this project belongs to Shanghai Artificial Intelligence Laboratory, and the commercial use of these models is forbidden.

The code are licensed under the Apache License, Version 2.0. You may obtain a copy of the License at: https://www.apache.org/licenses/LICENSE-2.0.

The model weights are made available for use under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). You may obtain a copy of the License at: https://creativecommons.org/licenses/by-nc-sa/4.0/.

The weights were trained on ECMWF's ERA5 and HRES data. The repo includes a few examples of ERA5 and HRES data that can be used as inputs to the models. ECMWF data product are subject to the following terms:

1. Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)".
2. Source www.ecmwf.int
3. Licence Statement: ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/
4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.

📚 Citation

If you find this work useful, please cite:

@article{han2024fengwughr,
title={FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting}, 
author={Tao Han and Song Guo and Fenghua Ling and Kang Chen and Junchao Gong and Jingjia Luo and Junxia Gu and Kan Dai and Wanli Ouyang and Lei Bai},
year={2024},
eprint={2402.00059},
archivePrefix={arXiv},
primaryClass={cs.LG}
}