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A tool to predict hourly global solar radiation from limited daily meteorological data

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SolarCalc

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The SolarcCalc module is used to predict hourly global solar radiation on a horizontal surface from daily average temperature extremes and total precipitation records. The method used in the calculations is based on that presented in An Introduction to Environmental Biophysics by Campbell, G.S. and J.M. Norman (1998).

Version 1.0 of the SolarcCalc module is based on the source code of SolarCalc.jar (version 1.1, Jan. 2006), a computer program that was developped by the USDA Agricultural Research Service and that is available free of charge at https://data.nal.usda.gov/dataset/solarcalc-10.

Subsequent versions of the SolarcCalc module implemented performance improvements and fixed some issues with the original code and method.

Installation

Install and update using pip:

pip install solarcalc

Requirements

  • numpy : The fundamental package for scientific computing with Python.
  • pandas : Flexible and powerful data analysis / manipulation library for Python.

Example

>>> from solarcalc import load_demo_climatedata, calc_solar_rad
>>> climate_data = load_demo_climatedata()
>>> solarcalc = calc_solar_rad(
>>>     lon_dd=-76.4687209,
>>>     lat_dd=56.5213541,
>>>     alt=100,
>>>     climate_data=climate_data)
>>> print(solarcalc)

                     solar_rad_W/m2  deltat_degC       tau
1980-01-01 00:00:00             0.0          8.0  0.233333
1980-01-01 01:00:00             0.0          8.0  0.233333
1980-01-01 02:00:00             0.0          8.0  0.233333
1980-01-01 03:00:00             0.0          8.0  0.233333
1980-01-01 04:00:00             0.0          8.0  0.233333
                            ...          ...       ...
2020-12-31 19:00:00             0.0          5.0  0.116667
2020-12-31 20:00:00             0.0          5.0  0.116667
2020-12-31 21:00:00             0.0          5.0  0.116667
2020-12-31 22:00:00             0.0          5.0  0.116667
2020-12-31 23:00:00             0.0          5.0  0.116667

[359424 rows x 3 columns]

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A tool to predict hourly global solar radiation from limited daily meteorological data

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