.. _wind_climates:

Wind Climates
=============

Wind climates are central to resource assessment in PyWAsP. They represent wind speed
and direction distributions at locations of interest.

.. seealso::

    :doc:`windkit:user_guide/wind_climates/index`
        Overview, statistics, and Weibull fitting.

    :doc:`windkit:user_guide/wind_climates/tswc`
        Time Series Wind Climate - schema, I/O, creation.

    :doc:`windkit:user_guide/wind_climates/bwc`
        Binned Wind Climate - schema, I/O, creation.

    :doc:`windkit:user_guide/wind_climates/wwc`
        Weibull Wind Climate - schema, I/O, creation.

    :doc:`windkit:user_guide/wind_climates/gwc`
        Generalized Wind Climate - schema, I/O, creation.

PyWAsP builds on WindKit's wind climate support, adding terrain-aware processing
through the WAsP flow model. This page covers PyWAsP-specific workflows.

.. ipython:: python

    import pywasp as pw
    import windkit as wk
    import xarray as xr


Geospatial transforms for wind climates
---------------------------------------

:py:mod:`WindKit's spatial module <windkit.spatial>` makes it easy to work with geospatial data
and to perform typical transforms, such as reprojection, spatial clipping, and spatial masking.

To illustrate this, let's read a binned wind climate and transform it from lat-long coordinates
to UTM 29N coordinates:

.. ipython:: python
    :okwarning:

    bwc = wk.read_bwc("source/tutorials/data/SerraSantaLuzia.omwc", crs="EPSG:4326")
    bwc = wk.spatial.reproject(bwc, to_crs="EPSG:32629")
    print(bwc)

.. note::

    Note that the ``west_east`` and ``south_north`` coordinate values change after reprojection.

To set and get the ``crs`` of an object, use :py:func:`windkit.spatial.set_crs` and :py:func:`windkit.spatial.get_crs`.


Using wind climates as site objects in PyWake
---------------------------------------------

PyWAsP wind climate objects can be used as site objects for wind farm flow modeling in :doc:`PyWake <py_wake:index>`.
This is done by instantiating an ``XRSite`` using the :py:meth:`py_wake.Site.XRSite.from_pwc` method.

.. ipython:: python
    :okwarning:

    # First get a Weibull wind climate
    wwc = wk.weibull_fit(bwc)

    import py_wake
    site = py_wake.site.XRSite.from_pwc(wwc)
    print(site.ds)

Spatial inhomogeneity can be taken into account in PyWake by ``speedup`` and ``turning`` variables in the site object.
These variables track the relative speed-up and turning of the wind in the spatial domain during the wind farm flow modeling.

The :py:meth:`py_wake.Site.XRSite.from_pwc` method can take different ``speedup`` arguments for specifying the way to calculate speed-ups
between spatial points. By default, the "park" method is used, which calculates the sector-wise speed-ups relative
to the maximum mean wind speed in that sector.

Depending on how the speed-up is defined/calculated in the PyWake site object, the post-processing, or aggregation, of
wind farm flow results should take this into account.