BSRN solar radiation data for the testing, validation and benchmarking of solar irradiance components separation models

Résumé

The dataset is an excerpt of the validation dataset used in: Ruiz-Arias JA, Gueymard CA. Review and performance benchmarking of 1-min solar irradiance components separation methods: The critical role of dynamically-constrained sky conditions. Submitted for publication to Renewable and Sustainable Energy Reviews. and it is ready to use in the Python package splitting_models developed during that research. See the documentation in the Python package for usage details. Below, there is a detailed description of the dataset. The data is in a single parquet file that contains 1-min time series of solar geometry, clear-sky solar irradiance simulations, solar irradiance observations and CAELUS sky types for 5 BSRN sites, one per primary Köppen-Geiger climate, namely: Minamitorishima (mnm), JP, for equatorial climate; Alice Springs (asp), AU, for dry climate; Carpentras (car), FR, for temperate climate; Bondville (bon), US, for continental climate; and Sonnblick (son), AT, for cold/polar/snow climate. It includes one calendar year per site. The BSRN data is publicly available. See download instructions in https://bsrn.awi.de/data. The specific variables included in the dataset are:  climate: primary Köppen-Geiger climate. Values are: A (equatorial), B (dry), C (temperate), D (continental) and E (polar/snow). longitude: longitude, in degrees east. latitude: latitude, in degrees north. sza: solar zenith angle, in degrees. eth: extraterrestrial solar irradiance (i.e., top of atmosphere solar irradiance), in W/m2. ghics: clear-sky global solar irradiance, in W/m2. It is evaluated with the SPARTA clear-sky model and MERRA-2 clear-sky atmosphere. difcs: clear-sky diffuse solar irradiance, in W/m2.It is evaluated with the SPARTA clear-sky model and MERRA-2 clear-sky atmosphere. ghicda: clean-and-dry clear-sky global solar irradiance, in W/m2. It is evaluated with the SPARTA clear-sky model and MERRA-2 clear-sky atmosphere, prescribing zero aerosols and zero precipitable water. ghi: observed global horizontal irradiance, in W/m2. dif: observed diffuse irradiance, in W/m2. sky_type: CAELUS sky type. Values are: 1 (unknown), 2 (overcast), 3 (thick clouds), 4 (scattered clouds), 5 (thin clouds), 6 (cloudless) and 7 (cloud enhancement). The dataset can be easily loaded in a Python Pandas DataFrame as follows: import pandas as pd data = pd.read_parquet(<filename>) The dataframe has a multi-index with two levels: times_utc and site. The former are the UTC timestamps at the center of each 1-min interval. The latter is each site's label.