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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)

Authors: Barry, James; Meilinger, Stefanie; Pfeilsticker, Klaus; Herman-Czezuch, Anna; Kimiaie, Nicola; Schirrmeister, Christopher; Yousif, Rone; +11 Authors

Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)

Abstract

This dataset contains all the relevant data for the algorithms described in the paper "Irradiance and cloud optical properties from solar photovoltaic systems", which were developed within the framework of the MetPVNet project. Input data: COSMO weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz) COSMO atmospheres for libRadtran (cosmo_atmosphere_libradtran_input.tar.gz) COSMO surface data for calibration (cosmo_pvcal_output.tar.gz) Aeronet data as text files (MetPVNet_Aeronet_Input_Data.zip) Measured data from the MetPVNet measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz) PV power data Horizontal and tilted irradiance from pyranometers Longwave irradiance from pyrgeometer MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc) Output data: Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments) Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz Global horizontal irradiance (GHI) inferred from PV power plants Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz Combined GHI averaged to 60 minutes and compared with COSMO data Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz Cloud optical depth inferred from PV power plants Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz Validation data: COSMO cloud optical depth (cosmo_cod_output.tar.gz) APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz) COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz) CAMS irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip) How to import results: The results files are stored as text files ".dat", using Python multi-index columns. In order to import the data into a Pandas dataframe, use the following lines of code (replace [filename] with the relevant file name): import pandas as pddata = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True) This gives a multi-index Dataframe with the index column the timestamp, the first column label corresponds to the measured variable and the second column to the relevant sensor Note: The output data has been updated to match the latest version of the paper, whereas the input and validation data remains the same as in Version 1.0.0

Keywords

remote sensing, atmospheric radiative transfer, cloud properties, global horizontal irradiance, solar energy, cloud optical depth, energy meteorology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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