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.
Earth-2 MIP will be installable on PyPi upon general release. In the mean time, one can install from source:
git clone git@github.com:NVIDIA/earth2mip.git
cd earth2mip && pip install .See installation documentation for more details and other options.
Earth-2 MIP provides a set of examples which can be viewed on the examples documentation 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 folders.
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):
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()
<xarray.DataArray (time: 11, history: 1, channel: 69, lat: 721, lon: 1440)>
dask.array<xarray-<this-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) <U5 'z1000' 'z925' 'z850' 'z700' ... 'u10m' 'v10m' 't2m'
Dimensions without coordinates: historyAnd you can get ACC/RMSE like this:
>>> 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
<xarray.Dataset>
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) <U5 't850' 'z1000' 'z700' ... 'z300' 'tcwv' 't2m'
Dimensions without coordinates: initial_time
Data variables:
acc (lead_time, channel) float64 1.0 1.0 1.0 ... 0.9686 0.9999
rmse (lead_time, channel) float64 0.0 2.469e-05 0.0 ... 7.07 2.998
initial_times (initial_time) datetime64[ns] 2018-01-01
>>> scores.rmse.sel(channel='z500')
<xarray.DataArray 'rmse' (lead_time: 11)>
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 <U5 'z500'
These notebooks illustrate how-to-use with a few models and this can serve as reference to bring in your own checkpoint as long as it's compatible. There may be additional work to make it compatible with Earth-2 MIP. Earth-2 MIP leverages the model zoo in Modulus to provide a reference set of base-line models. The goal is to enable to community to grow this collection of models as shown in the table below.
| ID | Model | Architecture | Type | Reference | Source | Size |
|---|---|---|---|---|---|---|
| fcn | FourCastNet | Adaptive Fourier Neural Operator | global weather | Arxiv | modulus | 300Mb |
| dlwp | Deep Learning Weather Prediction | Convolutional Encoder-Decoder | global weather | AGU | modulus | 50Mb |
| pangu | Pangu Weather (Hierarchical 6 + 24 hr) | Vision Transformer | global weather | Nature | onnx | 2Gb |
| pangu_6 | Pangu Weather 6hr Model | Vision Transformer | global weather | Nature | onnx | 1Gb |
| pangu_24 | Pangu Weather 24hr Model | Vision Transformer | global weather | Nature | onnx | 1Gb |
| fcnv2_sm | FourCastNet v2 | Spherical Harmonics Fourier Neural Operator | global weather | Arxiv | modulus | 3.5Gb |
| graphcast | Graphcast, 37 levels, 0.25 deg | Graph neural network | global weather | Science | github | 145MB |
| graphcast_small | Graphcast, 13 levels, 1 deg | Graph neural network | global weather | Science | github | 144MB |
| graphcast_operational | Graphcast, 13 levels, 0.25 deg | Graph neural network | global weather | Science | github | 144MB |
| precipitation_afno | FourCastNet Precipitation | Adaptive Fourier Neural Operator | diagnostic | Arxiv | modulus | 300Mb |
| climatenet | ClimateNet Segmentation Model | Convolutional Neural Network | diagnostic | GMD | modulus | 2Mb |
* = coming soon
Some models require additional dependencies not installed by default. Refer to the installation instructions 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. 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.
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.
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 for in depth information about Earth-2 MIP, functionality, APIs, etc.
- Github Discussions: Discuss new ideas, model integration, support etc.
- GitHub Issues: Bug reports, feature requests, install issues, etc.
Earth-2 MIP is provided under the Apache License 2.0, please see LICENSE.txt for full license text.