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.. half-hourly historical surface radiation on a 0.05 x 0.05 deg grid available
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.. for Europe and Africa (automatically interpolated to a 0.2 deg grid and
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.. combined with ERA5 temperature).
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atlite can process the following weather data fields and can convert them into following power-system relevant time series for any subsets of a full weather database.
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.. image:: doc/workflow_chart.png
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.. * Temperature
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.. * Downward short-wave radiation
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.. * Upward short-wave radiation
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.. * Wind
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.. * Runoff
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.. * Surface roughness
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.. * Height maps
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.. * Soil temperature
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.. * Dewpoint temperature
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.. * Wind power generation for a given turbine type
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.. * Solar PV power generation for a given panel type
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.. * Solar thermal collector heat output
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.. * Hydroelectric inflow (simplified)
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.. * Heating demand (based on the degree-day approximation)
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atlite was initially developed by the `Renewable Energy Group
With `atlite` we want to provide an interface between the meteorological and energy systems modelling communities.
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Traditionally the MET and ESM communities have not been interacting much.
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The outputs and learning of one community were only slowly adapted into the other community.
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With `atlite` we want bridge between the communities:
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We want to make it easy to use and integrate outputs of the MET communities into energy system models, by offering standardized ways of accessing weather/climate datasets and converting them to weather-dependent inputs for ESMs.
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For major next development goals, consult our `vision and roadmap project <https://github.com/orgs/PyPSA/projects/12/views/1>`_ or check our list of possible `enhancements <https://github.com/PyPSA/atlite/issues/?q=is%3Aissue%20state%3Aopen%20label%3A%22type%3A%20enhancement%22>`_.
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Installation
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============
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@@ -93,18 +67,6 @@ to install the most recent upstream version from GitHub
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Documentation
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===============
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.. * Install atlite from conda-forge or pypi.
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.. * Download one of the weather datasets listed above (ERA5 is downloaded
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.. automatically on-demand after the ECMWF
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.. `cdsapi<https://cds.climate.copernicus.eu/api-how-to>` client is
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.. properly installed)
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.. * Create a cutout, i.e. a geographical rectangle and a selection of
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.. times, e.g. all hours in 2011 and 2012, to narrow down the scope -
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.. see `examples/create_cutout.py <examples/create_cutout.py>`_
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.. * Select a sparse matrix of the geographical points inside the cutout
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.. you want to aggregate for your time series, and pass it to the
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.. appropriate converter function - see `examples/ <examples/>`_
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Please check the `documentation <https://atlite.readthedocs.io/en/latest>`_.
For major next development goals, you can consult our `vision and roadmap project <https://github.com/orgs/PyPSA/projects/12/views/1>`_
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or check the list of possible `enhancements <https://github.com/PyPSA/atlite/issues/?q=is%3Aissue%20state%3Aopen%20label%3A%22type%3A%20enhancement%22>`_.
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For linting, formatting and checking your code contributions
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against our guidelines (e.g. we use `Black <https://github.com/psf/black>`_ as code style
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and aim for `REUSE compliance <https://reuse.software/>`_,
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