Create weather data/iotools page in User's Guide

[kandersolar]

• [ ] Closes #xxxx
• I am familiar with the contributing guidelines
• [ ] Tests added
• [ ] Updates entries in docs/sphinx/source/reference for API changes.
• Adds description and name entries in the appropriate "what's new" file in docs/sphinx/source/whatsnew for all changes. Includes link to the GitHub Issue with :issue:`num` or this Pull Request with :pull:`num`. Includes contributor name and/or GitHub username (link with :ghuser:`user`).
• [ ] New code is fully documented. Includes numpydoc compliant docstrings, examples, and comments where necessary.
• Pull request is nearly complete and ready for detailed review.
• Maintainer: Appropriate GitHub Labels (including remote-data) and Milestone are assigned to the Pull Request and linked Issue.

This PR creates a new page in the User's Guide focused on weather data and pvlib.iotools. It's rather bare bones at the moment.

See also #1304

[AdamRJensen]

It seems that most people need weather data from scratch, so I would propose a restructured outline along the lines of:

• Weather data sources - discussing free options in terms of satellite data (NSRDB, PVGIS, CAMS) and measurements (BSRN, SRML, MIDC, SURFRAD, SOLRAD).
• File readers - discussing TMY2, TMY3, and EPW
• Usage - describe the general iotools convention

My main objective for this page is to be able to have a link to send someone every time we get the questions "I need weather data, what do I do?"

[harry-solcast]

Hi, I'd like to suggest putting in a category to help explain what kind of sources of weather data are available.

Disclaimer: I work for Solcast, and we use Pvlib internally, and many of our customers use it too. I hope this suggestion comes across as helping users to understand the types of data available.

Other weather sources
-------------------------
Measurement Data Sources
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. There are datasets such as BSRN and SURFRAD which make their measurement data publicly available.


Numerical Weather Prediction (NWP) Data Sources
These are mathematical simulations of weather systems. The data quality is much lower than that of measurements, 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.


Satellite Data Sources
These sources process satellite imagery (typically from geostationary satellites) to identify and classify clouds, and combine this with solar irradiance models 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 are available, and commerical sources such as SolarAnywhere, Solcast and Solargis provide paid options though often have free trials.

[kandersolar]

Thanks @harry-solcast! Very helpful :)

I suppose this PR is ready for review. Here's the rendered version: https://pvlib-python--1754.org.readthedocs.build/en/1754/user_guide/weather_data.html

[AdamRJensen]

This is awesome. What a great contribution!

[AdamRJensen]

Thank you @kandersolar !