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https://github.com/Aalto-QuML/ClimODE
ClimODE: Climate and Weather Forecasting With Physics-informed Neural ODEs
https://github.com/Aalto-QuML/ClimODE
Last synced: 3 months ago
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ClimODE: Climate and Weather Forecasting With Physics-informed Neural ODEs
- Host: GitHub
- URL: https://github.com/Aalto-QuML/ClimODE
- Owner: Aalto-QuML
- License: mit
- Created: 2023-11-28T13:28:52.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-08-06T01:22:16.000Z (3 months ago)
- Last Synced: 2024-08-23T00:34:01.910Z (3 months ago)
- Language: Python
- Homepage: https://yogeshverma1998.github.io/ClimODE/
- Size: 12 MB
- Stars: 56
- Watchers: 0
- Forks: 3
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - ClimODE - Models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow, which also enables estimating the uncertainty in predictions. (Climate Change / Earth and Climate Modeling)
README
# ClimODE: Climate and Weather Forecasting With Physics-informed Neural ODEs
[Yogesh verma](https://yoverma.github.io/yoerma.github.io/) | [Markus Heinonen](https://users.aalto.fi/~heinom10/) | [Vikas Garg](https://www.mit.edu/~vgarg/)
The code repository for the paper ClimODE: Climate and Weather Forecasting With Physics-informed Neural ODEs. More information can be found on the project [website](https://yogeshverma1998.github.io/ClimODE/).
## Citation
If you find this repository useful in your research, please consider citing the following paper:
```
@inproceedings{
verma2024climode,
title={Clim{ODE}: Climate Forecasting With Physics-informed Neural {ODE}s},
author={Yogesh Verma and Markus Heinonen and Vikas Garg},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=xuY33XhEGR}
}
```
## Prerequisites
- torchdiffeq : https://github.com/rtqichen/torchdiffeq.
- pytorch >= 1.12.0
- torch-scatter
- torch-sparse
- torch-cluster
- torch-spline-conv
- torchcubicspline: https://github.com/patrick-kidger/torchcubicspline
- properscoring (for CRPS scores) : https://pypi.org/project/properscoring/
## Data Preparation
First, download ERA5 data with 5.625deg from [WeatherBench](https://dataserv.ub.tum.de/index.php/s/m1524895). The data directory should look like the following
```
era5_data
|-- 10m_u_component_of_wind
|-- 10m_v_component_of_wind
|-- 2m_temperature
|-- constants
|-- geopotential_500
|-- temperature_850
```
## Training ERA5
### Global Forecast
To train ClimODE for global forecast use,
```
python train_global.py --scale 0 --batch_size 8 --spectral 0 --solver "euler"
```
### Global Monthly Forecast
To train ClimODE for global monthly forecast use,
```
python train_monthly.py --scale 0 --batch_size 4 --spectral 0 --solver "euler"
```
### Regional Forecast
To train ClimODE for regional forecasts among various regions of earth use,
```
python train_region.py --scale 0 --batch_size 8 --spectral 0 --solver "euler" --region 'NorthAmerica/SouthAmerica/Australia'
```
## Evaluation ERA5
### Global Forecast
To evaluate ClimODE for global forecast on Lat. weighted RMSE and ACC use, (Make sure to change the model path in the file)
```
python evaluation_global.py --spectral 0 --scale 0 --batch_size 8
```
### Global Monthly Forecast
To evaluate ClimODE for global monthly forecast on Lat. weighted RMSE and ACC use, (Make sure to change the model path in the file)
```
python evaluation_monthly.py --spectral 0 --scale 0 --batch_size 4
```
### Regional Forecast
To evaluate ClimODE for regional forecast on Lat. weighted RMSE and ACC use, (Make sure to change the model path in the file)
```
python evaluation_region.py --spectral 0 --scale 0 --region 'NorthAmerica/SouthAmerica/Australia' --batch_size 8
```
## Training on a different custom dataset
To train on a custom dataset, you need to follow the below guidelines
- **Data Loading**: You might want to change the data loading scheme depending on your data (e.g. seasonal, daily, etc., and with many different input channels), which can be found in ```utils.py``` in the data-loading function.
- **Fitting initial velocity**: Depending on the data, you need to estimate the initial velocity to train and test the model (For more details, see the manuscript) via the ```fit_velocity``` function.
- **Model Function**: Depending on the input observable quantities, you might need to modify the number of input channels to model function in ```model_function.py```.
- **Training and evaluation**: Depending on your dataset, you might want to fine-tune and change the various hyper-parameters in training and evaluation files. Make sure to make them consistent in both of them. Also, we report CRPS scores for global hourly forecast only, if you want to compute them for every task please include the ```evaluation_crps_mm``` function.
Note: We are also constantly updating and revising the repo to make it more adaptable in a general way, and finidng bugs and removing them and modifying certain parts.