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Open Sustainable Technology

A curated list of open technology projects to sustain a stable climate, energy supply, biodiversity and natural resources.

MOOC Machine Learning in Weather & Climate

Explore the application of Machine Learning across the main stages of numerical weather and climate prediction.
https://github.com/ecmwf-projects/mooc-machine-learning-weather-climate

Category: Sustainable Development
Sub Category: Education

Last synced: about 2 hours ago
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README.md

MOOC Machine Learning in Weather & Climate - Jupyter notebook exercises

This repository hosts the Jupyter notebook based exercises of the Massive Open Online Course (MOOC) on Machine Learning in Weather & Climate, which can now be found on ECMWF's learning platform https://learning.ecmwf.int/.

The notebook files can be found in the subdirectories corresponding to each tier of the MOOC. These include the following:

Tier 1 notebooks (ML in Weather & Climate)

In this tier there is only one notebook that demonstrates how to build a simple neural network on the WeatherBench dataset.

Tier 2 notebooks (Concepts of Machine Learning)

In this tier there are notebooks for each module that provide practical guidance on key concepts of Machine Learning.

Tier 3 notebooks (Practical ML Applications in Weather & Climate)

Each module of this tier contains notebooks that demonstrate practical applications of Machine Learning in the various stages of Numerical Weather and Climate prediction.

How to run the notebooks

The notebooks can either be downloaded and run on participants' own computers, or they can be run directly in various cloud environments. The advantage of the latter is that no software needs to be installed locally. In each notebook a number of options are provided where the notebook can be run. These may include the following:

Colab Kaggle Deepnote
Colab Kaggle Deepnote
Colab requires a Google account, which can easily be set-up for free. Requires (free) registration with Kaggle. Once in, "switch on the internet" via settings. Requires (free) registration. Deepnote is a good platform also for collaboration.

License

Unless otherwise stated, the notebooks fall under Apache License 2.0. In applying this licence, ECMWF does not waive the privileges and immunities granted to it by virtue of its status as an intergovernmental organisation nor does it submit to any jurisdiction.

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gpanegrossi 1****i 2
Marc Bocquet m****t@e****r 1
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Dependencies

.github/workflows/test-notebooks.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
tests/requirements_tests.txt pypi
  • ipykernel * test
  • nbconvert * test
  • nbformat * test
  • pytest * test
tier_2/data_handling/climetlab-my-plugin-solution/requirements.txt pypi
  • climetlab *
tier_2/data_handling/climetlab-my-plugin-solution/requirements_dev.txt pypi
  • black * development
  • flake8 * development
  • isort * development
tier_2/data_handling/climetlab-my-plugin-solution/setup.py pypi
  • climetlab >=0.10.0
tier_2/data_handling/climetlab-my-plugin-solution/tests/requirements_tests.txt pypi
  • ipykernel * test
  • nbconvert * test
  • nbformat * test
  • pytest * test
tier_2/deep_learning/requirements-pytorch.txt pypi
  • climetlab-mltc-surface-observation-postprocessing >=0.3.0
  • matplotlib *
  • numpy *
  • pytorch-lightning *
  • scikit-learn *
  • torch *
tier_2/deep_learning/requirements-tensorflow.txt pypi
  • climetlab-mltc-surface-observation-postprocessing >=0.3.0
  • matplotlib *
  • numpy *
  • scikit-learn *
  • tensorflow *
tier_3/ocean_climate/e3ci/requirements.txt pypi
  • folium *
  • geopandas *
  • mapclassify *
  • matplotlib *
  • numpy *
  • openpyxl *
  • pandas *
  • sklearn *
  • tqdm *
  • tslearn *
tier_1/environment.yml conda
  • numpy
  • tensorflow
  • xarray
tier_3/data_assimilation/environment.yaml conda
  • ca-certificates
  • certifi
  • ipywidgets
  • jupyterlab
  • matplotlib
  • numpy
  • openssl
  • pydot
  • scikit-learn
  • seaborn
  • tensorflow
  • tqdm
tier_3/observations/environment.yml conda
  • _py-xgboost-mutex 2.0
  • appdirs 1.4.4
  • appnope 0.1.2
  • asttokens 2.0.5
  • atk-1.0 2.38.0
  • backcall 0.2.0
  • brotli 1.0.9
  • brotli-bin 1.0.9
  • brotlipy 0.7.0
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  • ca-certificates 2023.01.10
  • cairo 1.16.0
  • catboost 1.1.1
  • cctools_osx-64 973.0.1
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  • clang 13.0.1
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  • cloudpickle 2.2.1
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  • lightgbm 3.3.5
  • lightning-utilities 0.6.0.post0
  • llvm-openmp 15.0.7
  • llvm-tools 13.0.1
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  • make 4.3
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  • mkl 2022.2.1
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  • numpy 1.23.5
  • openjpeg 2.5.0
  • openssl 3.0.7
  • packaging 23.0
  • pandas 1.5.3
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  • pcre2 10.40
  • pexpect 4.8.0
  • pickleshare 0.7.5
  • pillow 9.4.0
  • pip 23.0
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  • platformdirs 2.5.2
  • plotly 5.13.0
  • pmdarima 2.0.2
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  • prompt-toolkit 3.0.36
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  • pthread-stubs 0.4
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  • pure_eval 0.2.2
  • py-xgboost 1.7.1
  • pycparser 2.21
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  • pymeeus 0.5.12
  • pyod 1.0.7
  • pyopenssl 23.0.0
  • pyparsing 3.0.9
  • pysocks 1.7.1
  • python 3.10.8
  • python-dateutil 2.8.2
  • python-graphviz 0.20.1
  • python_abi 3.10
  • pytorch 1.13.1
  • pytorch-lightning 1.9.0
  • pytz 2022.7.1
  • pyyaml 6.0
  • pyzmq 23.2.0
  • readline 8.1.2
  • requests 2.28.2
  • scikit-learn 1.2.1
  • scipy 1.10.0
  • setuptools 66.1.1
  • shap 0.41.0
  • sigtool 0.1.3
  • six 1.16.0
  • sleef 3.5.1
  • slicer 0.0.7
  • stack_data 0.2.0
  • statsforecast 1.4.0
  • statsmodels 0.13.5
  • tapi 1100.0.11
  • tbats 1.1.1
  • tbb 2021.7.0
  • tbb-devel 2021.7.0
  • tenacity 8.1.0
  • threadpoolctl 3.1.0
  • tk 8.6.12
  • torchmetrics 0.11.1
  • tornado 6.2
  • tqdm 4.64.1
  • traitlets 5.7.1
  • typing-extensions 4.4.0
  • typing_extensions 4.4.0
  • tzdata 2022g
  • u8darts 0.23.1
  • u8darts-all 0.23.1
  • unicodedata2 15.0.0
  • urllib3 1.26.14
  • wcwidth 0.2.5
  • wheel 0.38.4
  • xarray 2023.1.0
  • xgboost 1.7.1
  • xorg-libxau 1.0.9
  • xorg-libxdmcp 1.1.3
  • xz 5.2.6
  • yaml 0.2.5
  • zeromq 4.3.4
  • zlib 1.2.13
  • zstd 1.5.2
tier_3/ocean_climate/unsupervised_ml_ocean/environment.yml conda
  • cartopy
  • conda
  • matplotlib
  • numpy
  • scikit-learn
  • scipy
  • seaborn

Score: 8.026170194946426