.. The Eumetnet postprocessing benchmark dataset Climetlab plugin documentation master file, created by sphinx-quickstart on Wed Nov 16 09:51:42 2022. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. The EUPP postprocessing benchmark dataset documentation ======================================================= This website document the EUMETNET postprocessing benchmark datasets, an initiative to provide high-quality datasets to derive easily analysis-ready datasets that can be used to perform benchmarking tasks of weather forecast postprocessing methods. The main tool to download and manage the data is a Python plugin. It can however convert the data to formats that can then be processed by other languages, and a few line of Python codes suffice to obtain the datasets. .. note:: * **Climetlab plugin version**: 0.2.5 * **EUPPBench dataset version**: v1.0 * **Base dataset version**: v1.0 * **Dataset status**: :ref:`files/datasets_status:Datasets status` .. toctree:: :maxdepth: 1 :caption: Currently available datasets: files/EUPPBench_datasets files/base_datasets Using climetlab to access the data ---------------------------------- A plugin for `climetlab `__ to retrieve the Eumetnet postprocessing benchmark datasets is available. |PyPI version| |PyPI pyversions| |build| It facilitates the download of the dataset time-aligned forecasts, reforecasts (hindcasts) and observations (`ERA5 reanalysis `__). See the `demo notebooks `__ |Binder| - `demo_training_data_forecasts.ipynb `__ |nbviewer| |Open in colab| |image1| |image6| - `demo_ensemble_forecasts.ipynb `__ |image2| |image3| |image4| |image7| - `demo_EUPPBench_germany_station_data.ipynb `__ |image15| |image16| |image17| |image21| - `demo_EUPPBench_gridded_data.ipynb `__ |image18| |image19| |image20| |image22| The climetlab python plugin allows users to easily access the data with a few lines of code such as: .. code:: python # Uncomment the line below if climetlab and the plugin are not yet installed #!pip install climetlab climetlab-eumetnet-postprocessing-benchmark import climetlab as cml ds = cml.load_dataset('eumetnet-postprocessing-benchmark-training-data-gridded-forecasts-surface', "2017-12-02", "2t", "highres") fcs = ds.to_xarray() which for instance download the deterministic (high-resolution) forecasts for the 2 metres temperature. Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. |PyPI version| image:: https://badge.fury.io/py/climetlab-eumetnet-postprocessing-benchmark.svg :target: https://badge.fury.io/py/climetlab-eumetnet-postprocessing-benchmark .. |PyPI pyversions| image:: https://img.shields.io/pypi/pyversions/climetlab-eumetnet-postprocessing-benchmark.svg :target: https://pypi.org/project/climetlab-eumetnet-postprocessing-benchmark/ .. |build| image:: https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/actions/workflows/check-and-publish.yml/badge.svg?branch=main :target: https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/actions/workflows/check-and-publish.yml .. |Binder| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?urlpath=lab .. |nbviewer| image:: https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg :target: https://nbviewer.jupyter.org/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_training_data_forecasts.ipynb .. |Open in colab| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_training_data_forecasts.ipynb .. |image1| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?filepath=notebooks/demo_training_data_forecasts.ipynb .. |image2| image:: https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg :target: https://nbviewer.jupyter.org/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_ensemble_forecasts.ipynb .. |image3| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_ensemble_forecasts.ipynb .. |image4| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?filepath=notebooks/demo_ensemble_forecasts.ipynb .. |image6| image:: https://deepnote.com/buttons/launch-in-deepnote-small.svg :target: https://deepnote.com/launch?name=MyProject&url=https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_training_data_forecasts.ipynb .. |image7| image:: https://deepnote.com/buttons/launch-in-deepnote-small.svg :target: https://deepnote.com/launch?name=MyProject&url=https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_ensemble_forecasts.ipynb .. |image15| image:: https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg :target: https://nbviewer.jupyter.org/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_EUPPBench_germany_station_data.ipynb .. |image16| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_EUPPBench_germany_station_data.ipynb .. |image17| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?filepath=notebooks/demo_EUPPBench_germany_station_data.ipynb .. |image18| image:: https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg :target: https://nbviewer.jupyter.org/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_EUPPBench_gridded_data.ipynb .. |image19| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/Climdyn/climetlab-eumetnet-postprocessing-benchmark/blob/main/notebooks/demo_EUPPBench_gridded_data.ipynb .. |image20| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/Climdyn/climetlab-eumetnet-postprocessing-benchmark/main?filepath=notebooks/demo_EUPPBench_gridded_data.ipynb .. |image21| image:: https://deepnote.com/buttons/launch-in-deepnote-small.svg :target: https://deepnote.com/launch?name=MyProject&url=https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_EUPPBench_germany_station_data.ipynb .. |image22| image:: https://deepnote.com/buttons/launch-in-deepnote-small.svg :target: https://deepnote.com/launch?name=MyProject&url=https://github.com/Climdyn/climetlab-eumetnet-postprocessing-benchmark/tree/main/notebooks/demo_EUPPBench_gridded_data.ipynb