A new method for estimating cloud optical depth from photovoltaic power measurements

Journal Article (2026)
Author(s)

William Wandji Nyamsi (Finnish Meteorological Institute (FMI))

Anders V. Lindfors (Finnish Meteorological Institute (FMI))

Angela Meyer (TU Delft - Atmospheric Remote Sensing, Bern University of Applied Sciences)

Antti Lipponen (Finnish Meteorological Institute (FMI))

Antti Arola (Finnish Meteorological Institute (FMI))

Research Group
Atmospheric Remote Sensing
DOI related publication
https://doi.org/10.5194/amt-19-899-2026
More Info
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Publication Year
2026
Language
English
Research Group
Atmospheric Remote Sensing
Issue number
3
Volume number
19
Pages (from-to)
899-922
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Abstract

A new method was developed to estimate the cloud optical depth (τc) from photovoltaic (PV) power measurements under overcast sky conditions. It is a fully physical method utilizing directly PV power measurements. It exploits the recent advances and real-time availability at global scale of aerosol properties, downwelling shortwave irradiance and its direct and diffuse components received at ground level under clear-sky conditions and ground albedo, altogether provided by the Copernicus Atmosphere Monitoring Service (CAMS) radiation service. In addition to CAMS data, wind speed and air temperature from European Centre for Medium-Range Weather Forecasts twentieth century reanalysis ERA5 products are also used as inputs. The τc estimates have been compared to different data sources of τc retrievals at four experimental PV sites located in various climates. When compared to τc retrieved from groundbased pyranometer measurements serving as reference, the correlation coefficient is greater than 0.97. The bias ranges between -3 and 4, i.e., -8 % and 14 % in relative value. The root mean square error (RMSE) lies in the interval [3,8] ([9,21] % in relative value). When compared to satellitebased retrievals from Meteosat Second Generation and Moderate Resolution Imaging Spectroradiometer, both relative errors become comprehensively greater. Nevertheless, our method remarkably reduces the relative bias and RMSE, by up to 10 % and 20 % respectively, compared to the existing state-of-the-art approach. This work demonstrates the accuracy of the method and clearly shows its great potential use whenever PV power measurements are available.