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https://github.com/NVIDIA/earth2mip
Earth-2 Model Intercomparison Project (MIP) is a python framework that enables climate researchers and scientists to inter-compare AI models for weather and climate.
https://github.com/NVIDIA/earth2mip
climate deep-learning weather
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Earth-2 Model Intercomparison Project (MIP) is a python framework that enables climate researchers and scientists to inter-compare AI models for weather and climate.
- Host: GitHub
- URL: https://github.com/NVIDIA/earth2mip
- Owner: NVIDIA
- License: apache-2.0
- Created: 2023-08-25T20:22:59.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-25T04:56:29.000Z (2 months ago)
- Last Synced: 2024-10-29T16:17:25.703Z (5 days ago)
- Topics: climate, deep-learning, weather
- Language: Python
- Homepage: https://nvidia.github.io/earth2mip/
- Size: 16.4 MB
- Stars: 195
- Watchers: 4
- Forks: 46
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
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- Awesome-LWMs - NVIDIA Earth2Mip - 2 Model Intercomparison Project (MIP) is a python framework that enables climate researchers and scientists to inter-compare AI models for weather and climate. (🚀 Code / More)
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README
# Earth-2 MIP (Beta)
[](https://www.repostatus.org/#active)
[](https://github.com/NVIDIA/earth2mip/blob/master/LICENSE.txt)
[](https://nvidia.github.io/earth2mip/)
[](https://codecov.io/gh/NickGeneva/earth2mip/tree/main)
[](https://github.com/NVIDIA/earth2mip)
[](https://github.com/psf/black)
Earth-2 Model Intercomparison Project (MIP) is a Python based AI framework that
enables climate researchers and scientists to explore and experiment with the use of AI
models for weather and climate.
It provides reference workflows for understanding how AI models capture the physics of
the Earth's atmosphere and how they can work with traditional numerical weather
forecasting models.
For instance, the repo provides a uniform interface for running inference using
pre-trained model checkpoints and scoring the skill of such models using certain
standard metrics.
This repository is meant to facilitate the weather and climate community to come up with
good reference baseline of events to test the models against and to use with a variety
of data sources.
## Installation
Earth-2 MIP will be installable on PyPi upon general release.
In the mean time, one can install from source:
```bash
git clone [email protected]:NVIDIA/earth2mip.git
cd earth2mip && pip install .
```
See [installation documentation](https://nvidia.github.io/earth2mip/userguide/install.html)
for more details and other options.
## Getting Started
Earth-2 MIP provides a set of examples which can be viewed on the [examples documentation](https://nvidia.github.io/earth2mip/examples/index.html)
page which can be used to get started with various workflows.
These examples can be downloaded both as Jupyer Notebooks and Python scripts.
The source Python scripts can be found in the [examples](./examples/) folders.
### Basic Inference
Earth-2 MIP provides high-level APIs for running inference with AI models.
For example, the following can be used to run Pangu weather using an initial state from
the climate data store (CDS):
```bash
python
>>> import datetime
>>> from earth2mip.networks import get_model
>>> from earth2mip.initial_conditions import cds
>>> from earth2mip.inference_ensemble import run_basic_inference
>>> time_loop = get_model("e2mip://dlwp", device="cuda:0")
>>> data_source = cds.DataSource(time_loop.in_channel_names)
>>> ds = run_basic_inference(time_loop, n=10, data_source=data_source, time=datetime.datetime(2018, 1, 1))
>>> ds.chunk()
dask.array, shape=(11, 1, 69, 721, 1440), dtype=float32, chunksize=(11, 1, 69, 721, 1440), chunktype=numpy.ndarray>
Coordinates:
* lon (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8
* lat (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0
* time (time) datetime64[ns] 2018-01-01 ... 2018-01-03T12:00:00
* channel (channel) >> from earth2mip.inference_medium_range import score_deterministic
>>> import numpy as np
>>> scores = score_deterministic(time_loop,
data_source=data_source,
n=10,
initial_times=[datetime.datetime(2018, 1, 1)],
# fill in zeros for time-mean, will typically be grabbed from data.
time_mean=np.zeros((7, 721, 1440))
)
>>> scores
Dimensions: (lead_time: 11, channel: 7, initial_time: 1)
Coordinates:
* lead_time (lead_time) timedelta64[ns] 0 days 00:00:00 ... 5 days 00:...
* channel (channel) >> scores.rmse.sel(channel='z500')
array([ 0. , 150.83014446, 212.07880612, 304.98592282,
381.36510987, 453.31516952, 506.01464974, 537.11092269,
564.79603347, 557.22871627, 586.44691243])
Coordinates:
* lead_time (lead_time) timedelta64[ns] 0 days 00:00:00 ... 5 days 00:00:00
channel
| ID | Model | Architecture | Type | Reference | Source | Size |
|:-----:|:-----:|:-------------------------------------------:|:--------------:|:---------:|:-------:|:---:|
| fcn | FourCastNet | Adaptive Fourier Neural Operator | global weather | [Arxiv](https://arxiv.org/abs/2202.11214) | [modulus](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/models/modulus_fcn) | 300Mb |
| dlwp | Deep Learning Weather Prediction | Convolutional Encoder-Decoder | global weather | [AGU](https://doi.org/10.1029/2020MS002109) | [modulus](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/models/modulus_dlwp_cubesphere) | 50Mb |
| pangu | Pangu Weather (Hierarchical 6 + 24 hr) | Vision Transformer | global weather | [Nature](https://doi.org/10.1038/s41586-023-06185-3) | onnx | 2Gb |
| pangu_6 | Pangu Weather 6hr Model | Vision Transformer | global weather | [Nature](https://doi.org/10.1038/s41586-023-06185-3) | onnx | 1Gb |
| pangu_24 | Pangu Weather 24hr Model | Vision Transformer | global weather | [Nature](https://doi.org/10.1038/s41586-023-06185-3) | onnx | 1Gb |
| fcnv2_sm | FourCastNet v2 | Spherical Harmonics Fourier Neural Operator | global weather | [Arxiv](https://arxiv.org/abs/2306.03838) | [modulus](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/models/modulus_fcnv2_sm) | 3.5Gb |
| graphcast | Graphcast, 37 levels, 0.25 deg | Graph neural network | global weather | [Science](https://www.science.org/doi/10.1126/science.adi2336) | [github](https://github.com/google-deepmind/graphcast) | 145MB |
| graphcast_small | Graphcast, 13 levels, 1 deg | Graph neural network | global weather | [Science](https://www.science.org/doi/10.1126/science.adi2336) | [github](https://github.com/google-deepmind/graphcast) | 144MB |
| graphcast_operational | Graphcast, 13 levels, 0.25 deg| Graph neural network | global weather | [Science](https://www.science.org/doi/10.1126/science.adi2336) | [github](https://github.com/google-deepmind/graphcast) | 144MB |
| precipitation_afno | FourCastNet Precipitation | Adaptive Fourier Neural Operator | diagnostic | [Arxiv](https://arxiv.org/abs/2202.11214) | [modulus](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/models/modulus_diagnostics) | 300Mb |
| climatenet | ClimateNet Segmentation Model | Convolutional Neural Network | diagnostic | [GMD](https://doi.org/10.5194/gmd-14-107-2021) | [modulus](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/models/modulus_diagnostics) | 2Mb |
\* = coming soon
Some models require additional dependencies not installed by default.
Refer to the [installation instructions](https://nvidia.github.io/earth2mip/userguide/install.html)
for details.
*Note* : Each model checkpoint may have its own unique license. We encourage users to
familiarize themselves with each to understand implications for their particular use
case.
We want to integrate your model into the scoreboard to show the community!
The best way to do this is via [NVIDIA Modulus](https://github.com/NVIDIA/modulus).
You can contribute your model (both the training code as well as model checkpoint) and
we can ensure that it is maintained as part of the reference set.
## Contributing
Earth-2 MIP is an open source collaboration and its success is rooted in community
contribution to further the field.
Thank you for contributing to the project so others can build on your contribution.
For guidance on making a contribution to Earth-2 MIP, please refer to the
[contributing guidelines](./CONTRIBUTING.md).
## More About Earth-2 MIP
This work is inspired to facilitate similar engagements between teams here at
NVIDIA - the ML experts developing new models and the domain experts in Climate science
evaluating the skill of such models.
For instance, often necessary input data such as normalization constants and
hyperparameter values are not packaged alongside the model weights.
Every model typically implements a slightly different interface. Scoring routines are
specific to the model being scored and may not be consistent across groups.
Earth-2 MIP addresses these challenges and bridges the gap between the domain experts
who most often are assessing ML models, and the ML experts producing them.
Compared to other projects in this space, Earth-2 MIP focuses on scoring models
on-the-fly.
It has python APIs suitable for rapid iteration in a jupyter book, CLIs for scoring
models distributed over many GPUs, and a flexible
plugin framework that allows anyone to use their own ML models.
More importantly Earth-2 MIP aspires to facilitate exploration and collaboration within
the climate research community to evaluate the potential of AI models in climate and
weather simulations.
Please see the [documentation page](https://nvidia.github.io/earth2mip/) for in depth
information about Earth-2 MIP, functionality, APIs, etc.
## Communication
- Github Discussions: Discuss new ideas, model integration, support etc.
- GitHub Issues: Bug reports, feature requests, install issues, etc.
## License
Earth-2 MIP is provided under the Apache License 2.0, please see
[LICENSE.txt](./LICENSE.txt) for full license text.
### Additional Resources
- [Earth-2 Website](https://www.nvidia.com/en-us/high-performance-computing/earth-2/)
- [NVIDIA Modulus](https://github.com/NVIDIA/modulus)