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For project development and monitoring of large-scale solar power plants, solar irradiance ground measurements are of crucial importance. Photodiode pyranometers are widely used for such applications, especially at remote locations. The advantages compared to most thermopile pyranometers are a fast response time in the order of microseconds, lower costs, no thermal offsets, and a higher robustness against sensor soiling. In contrast to most thermopile sensors, which use clear domes as entrance windows, most photodiode pyranometers use diffusor disks as entrance windows. The scattering contribution of the soiling losses is smaller in the case of a diffusor and therefore the soiling effect is reduced for most photodiode pyranometers. [1,2] Note that ventilators might also reduce the soiling losses. However, photodiode pyranometers have higher measurement errors compared to thermopile pyranometers which are partly systematic and can cause a noticeable bias in solar resource measurements. [3] In comparison with the typical domes of thermopile pyranometers, the diffusor disk of photodiode pyranometers causes a comparably high incidence angle dependence, although its shape is already designed to limit the effect. [4] A fully flat diffusor disk, only exposed to the radiation via its up-facing surface, would cause lower sensitivities for high incidence angles. This is related to the increase of the surface reflectance with larger incidence angles and the extension of the light path through the diffusor. Most diffusor disks are therefore curved or use not only the upper circular surface of a cylinder-shaped receiver, but also the lateral area. Furthermore, photodiode pyranometers show a higher temperature dependence than modern thermopile pyranometers and less stability of the calibration. [5] The errors of photodiode pyranometers, which are probably most complex to correct, stem from the photodiode's wavelength-dependent spectral response (SR). [6] The responsivity to broadband irradiance therefore depends on the solar spectrum. Deviations between the current spectrum and the spectrum used for calibration will hence cause measurement errors, the so-called spectral error. This error depends on the temperature-dependent SR of the pyranometer, the air mass (AM), and the atmospheric conditions at the site at the time of measurement.The correction factors and calibration of rotating shadowband irradiometers (RSIs) are discussed in Section 2 and the uncertainty of photodiode pyranometer measurements is discussed in Section 3. The calculation method for the uncertainty of GHI, DNI, and DHI is presented in Section 4, first regarding the spectral uncertainty with respect to uncorrected and corrected measurements and then regarding further uncertainty contributions. In Section 5, the validation of the method is presented with the application of the method at the six measurement stations with RSI and reference data in different climates. Finally, conclusions are presented and summarized.

Ground-based radiation measurements are required for all large solar projects and for evaluating the accuracy of solar radiation models and datasets. Ground data almost always contain low-quality periods caused by instrumental issues, logging errors, or maintenance deficiencies. Therefore, quality control (QC) is needed to detect and eventually flag or exclude such suspicious or erroneous data before any subsequent analysis. The few existing automatic QC methods are not perfect, thus expert visual inspection of the data is still required. In this work, we present a harmonized QC procedure, which is a combination of various available methods, including some that include an expert visual inspection. In the framework of IEA PVPS Task 16, these tests are applied to 161 world stations that are equipped with various radiometer models, and are candidates for an ongoing benchmark of irradiance datasets derived from satellite or weather models. Because the implementation of these methods by experts, and their subsequent decisions, might lead to different QC results, the independently obtained results from nine evaluators are compared for two test sites. The QC results are found similar and more stringent than purely automated tests, even though some deviations exist due to differences in manual flagging.

A recent update of the ISO 9060 standard for the classification of solar radiometers introduces the use of the clear sky spectral error. The spectral error is the change in responsivity that may occur when the spectral distribution of the incident solar radiation differs from the spectral distribution present at the time of calibration. Spectral errors may occur if the radiometer does not have a completely uniform spectral responsivity. This use of the clear sky spectral error is a significant change compared to the previous version of the 9060 standard as now not only thermopile radiometers, but also photodiode radiometers are covered by the standard. We explain the method used to derive the spectral error and present spectral errors for several radiometers of different technologies. The results demonstrate that the new method is helpful to classify radiometers, distinguishing between different radiometer types and excluding inappropriate instruments from the classification as pyranometers and pyrheliometers. We recommend that the method is also used for WMO's radiometer classification which up to our knowledge is currently not fulfilled by any field pyrheliometer and many pyranometers on the market due to its demanding requirements for the spectral responsivity.