Create weather data/iotools page in User's Guide

docs/sphinx/source/reference/iotools.rst

@@ -1,3 +1,6 @@
+
+.. py:module:: pvlib.iotools
+
 .. currentmodule:: pvlib
 
 .. _iotools:

docs/sphinx/source/user_guide/index.rst

@@ -14,6 +14,7 @@ User Guide
    timetimezones
    clearsky
    bifacial
+   weather_data
    comparison_pvlib_matlab
    variables_style_rules
    singlediode

docs/sphinx/source/user_guide/weather_data.rst

@@ -0,0 +1,159 @@
+.. _weatherdata:
+
+Weather data
+============
+
+Simulating the performance of a PV system requires irradiance and meteorological data
+as the inputs to a PV system model.  Weather datasets are available
+from many sources and in many formats.  The :py:mod:`pvlib.iotools` module
+contains functions to easily retrieve and import such datasets in a standardized
+form that is convenient to use with the rest of pvlib.  
+
+The primary focus of :py:mod:`pvlib.iotools` is time series solar resource
+data like the irradiance datasets from PVGIS and the NSRDB, but it also provides
+functionality for other types of data useful for certain aspects of PV modeling.
+For example, precipitation data is available from :py:func:`~pvlib.iotools.get_acis_prism`
+for soiling modeling and horizon profiles can be retrieved from
+:py:func:`~pvlib.iotools.get_pvgis_horizon` for horizon shade modeling.
+
+For a complete list of functions related to retrieving and importing weather
+data, see :ref:`iotools`, and for a detailed comparison of the weather datasets
+available through pvlib, see [1]_.
+
+
+Types of weather data sources
+-----------------------------
+
+Weather data can be grouped into a few fundamental categories.  Which
+type is most useful depends on the application.  Here we provide a high-level
+overview of different types of weather data, and when you might want to use
+them.
+
+1. **Ground station measurements**:
+   From in-situ monitoring equipment. If properly maintained and
+   quality-controlled, these are the highest quality
+   source of weather information. However, the coverage depends on
+   a weather station having been set up in advance for the location and
+   times of interest. Some ground station networks like the BSRN and SURFRAD
+   make their measurement data publicly available. An global overview of ground
+   stations is available at https://SolarStations.org.
+
+   Data from public ground station measurement networks are useful if you
+   want accurate, high-resolution data but have flexibility around the
+   specific measurement location.
+
+2. **Satellite data**: 
+   These sources process satellite imagery (typically from geostationary
+   satellites) to identify and classify clouds, and combine this with solar
+   irradiance models and aerosol data to produce irradiance estimates. The
+   quality is generally much higher than NWP, but still not as good as a well-maintained
+   weather station. They have high spatial and temporal resolution
+   corresponding to the source satellite imagery, and are generally
+   optimised to estimate solar irradiance for PV applications. Free sources
+   such as PVGIS, NSRDB, and CAMS are available, and commerical sources such
+   as SolarAnywhere, Solcast, and Solargis provide paid options though often
+   have free trials.
+
+   Satellite data is useful when suitable ground measurements are
+   not available for the location and/or times of interest.
+
+3. **Numerical Weather Prediction (NWP)**:
+   These are mathematical simulations of weather systems.
+   The data quality is much lower than that of measurements and
+   satellite data, owing in part to coarser spatial and temporal
+   resolution, as well as many models not being optimised for solar
+   irradiance for PV applications. On the plus side, these models typically
+   have worldwide coverage, with some regional models (e.g. HRRR) sacrifice
+   global coverage for somewhat higher spatial and temporal resolution.
+   Various forecast (e.g. GFS, ECMWF, ICON) and reanalysis sources (ERA5,
+   MERRA2) exist.
+
+   NWP datasets are primarily useful for parts of the world not covered
+   by satellite-based datasets (e.g. polar regions) or if extremely long time
+   ranges are needed.
+
+
+Usage
+-----
+
+With some exceptions, the :py:mod:`pvlib.iotools` functions
+provide a uniform interface for accessing data across many formats.
+Specifically, :py:mod:`pvlib.iotools` functions usually return two objects:
+a :py:class:`pandas.DataFrame` of the actual dataset, plus a metadata
+dictionary.  Most :py:mod:`pvlib.iotools` functions also have
+a ``map_variables`` parameter to automatically translate
+the column names used in the data file (which vary widely from dataset to dataset)
+into standard pvlib names (see :ref:`variables_style_rules`).  
+
+Typical usage looks something like this:
+
+.. code-block:: python
+
+    # get_pvgis_tmy returns two additional values besides df and metadata
+    df, _, _, metadata = pvlib.iotools.get_pvgis_tmy(latitude, longitude, map_variables=True)
+
+This code will fetch a Typical Meteorological Year (TMY) dataset from PVGIS,
+returning a :py:class:`pandas.DataFrame` containing the hourly weather data
+and a python dict with information about the dataset.
+
+Most :py:mod:`pvlib.iotools` functions work with time series datasets.
+In that case, the returned ``df`` DataFrame has a datetime index, localized
+to the appropriate time zone where possible.  Make sure to understand each
+dataset's timestamping convention (e.g. center versus end of interval), as
+pvlib will use these timestamps for solar position calculations.
+
+The content of the metadata dictionary varies for each function/dataset.
+
+
+Data retrieval
+**************
+
+Several :py:mod:`pvlib.iotools` functions access the internet to fetch data from
+online web APIs.  For example, :py:func:`~pvlib.iotools.get_pvgis_hourly`
+downloads data from PVGIS's webservers and returns it as a python variable.
+Functions that retrieve data from the internet are named ``get_``, followed
+by the name of the data source: :py:func:`~pvlib.iotools.get_bsrn`,
+:py:func:`~pvlib.iotools.get_psm3`, :py:func:`~pvlib.iotools.get_pvgis_tmy`,
+and so on.
+
+For satellite/reanalysis datasets, the location is specified by latitude and
+longitude in decimal degrees:
+
+.. code-block:: python
+
+    latitude, longitude = 33.75, -84.39  # Atlanta, Georgia, United States
+    df, metadata = pvlib.iotools.get_psm3(latitude, longitude, map_variables=True, ...)
+
+
+For ground station networks, the location identifier is the station ID:
+
+.. code-block:: python
+
+    df, metadata = pvlib.iotools.get_bsrn(station='cab', start='2020-01-01', end='2020-01-31', ...)
+
+Some of these data providers require registration.  In those cases, your
+access credentials must be passed as parameters to the function.  See the
+individual function documentation pages for details.
+
+
+Reading local files
+*******************
+
+:py:mod:`pvlib.iotools` also provides functions for parsing data files
+stored locally on your computer.
+Functions that read and parse local data files are named ``read_``, followed by
+the name of the file format they parse: :py:func:`~pvlib.iotools.read_tmy3`,
+:py:func:`~pvlib.iotools.read_epw`, and so on.
+
+For example, here is how to read a file in the TMY3 file format:
+
+.. code-block:: python
+
+    df, metadata = pvlib.iotools.read_tmy3(r"C:\path\to\file.csv", map_variables=True)
+
+
+References
+----------
+.. [1] Jensen et al. "pvlib iotools—Open-source Python functions for seamless
+   access to solar irradiance data". Solar Energy, 2023.
+   :doi:`10.1016/j.solener.2023.112092`.

docs/sphinx/source/whatsnew/v0.10.3.rst

@@ -24,6 +24,7 @@ Testing
 
 Documentation
 ~~~~~~~~~~~~~
+* Create :ref:`weatherdata` User's Guide page. (:pull:`1754`)
 * Fixed a plotting issue in the IV curve gallery example (:pull:`1895`)
 
 Contributors
@@ -33,3 +34,6 @@ Contributors
 * Will Hobbs (:ghuser:`williamhobbs`)
 * Anton Driesse (:ghuser:`adriesse`)
 * :ghuser:`matsuobasho`
+* Harry Jack (:ghuser:`harry-solcast`)
+* Adam R. Jensen (:ghuser:`AdamRJensen`)
+* Kevin Anderson (:ghuser:`kandersolar`)