the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Sep 2025
A new method for estimating cloud optical depth from photovoltaic power measurements
Abstract. A new method was developed to estimate the cloud optical depth (τc) from photovoltaic (PV) power measurements under overcast sky conditions. It is the first fully physical and universally applicable 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, ground albedo and extraterrestrial irradiance, 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 (ECMWF) twentieth century reanalysis ERA5 products are also used as inputs. An algorithm for selecting overcast sky conditions has been designed too. The estimates have been compared to different data sources of retrievals at four experimental PV sites located in various climates. When compared to retrieved from ground–based pyranometer measurements serving as reference, the correlation coefficient is greater than 0.97. The bias ranges between –3 and 4, i.e., −8 % and 12 % in relative value. The root mean square error (RMSE) lies in the interval [3, 8] ([9, 21] % in relative value). When compared to satellite–based retrievals from Meteosat Second Generation (MSG) and Moderate Resolution Imaging Spectroradiometer (MODIS), 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.
- Preprint
(2030 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
- CC1: 'Comment on egusphere-2025-3743', Simone Lolli, 29 Sep 2025
-
RC1: 'Comment on egusphere-2025-3743', Anonymous Referee #1, 05 Oct 2025
This manuscript proposed a new method to estimate the cloud optical depth from PV power measurements. It provided a detailed description of the algorithm mechanism, development, and application. Evaluations at four PV sites all demonstrated good performance of this method. The manuscript is well-organized, clearly written, and easy to follow. The topic is within the scope of Atmospheric Measurement Techniques. My comments are therefore largely focused on a few specific points that require clarification or further discussion.
From the algorithm description, the new method proposed by the authors seems to require building corresponding K-LUT for each experimental PV site. Given this, would it be more appropriate in practical application to consider atmospheric profiles (e.g., from ERA5) that are more aligned with the local conditions rather than using standard atmospheric profiles? Besides, what are the main reasons or impact factors for the generally poorer performance of the method at Cabauw-ID023 site compared to the other three sites (see Figs. 7~10)? Authors should include relevant discussion on this matter.
While this work primarily focuses on cloudy conditions (CF larger than 0.95), I am also concerned about, how the new method perform under partly cloudy conditions compares with satellite retrievals.
The method still relies on other auxiliary data, such as cloud phase, cloud fraction, etc. provided by CAMS/MODIS. Is there potential for achieving algorithmic independence in the future by relying solely on PV observations?
Citation: https://doi.org/10.5194/egusphere-2025-3743-RC1 -
RC2: 'Comment on egusphere-2025-3743', Anonymous Referee #3, 07 Dec 2025
The manuscript by Wandji Nyamsi et al. presented a numerical method to estimate cloud optical depth (COD) from PV measurements. COD is an important factor scientifically for weather and climate studies, and also a key factor influencing solar energy reaching the Earth surface. Thus, great efforts have been devoted to measure or retrieve COP from both ground and space. This study presented a numerical estimation of the COD from PV power measurement under overcast sky conditions. The authors claim the method to be physically and universally applicable, and correlation coefficients over 0.97 are achieved by comparing with ground-based pyranometer measurements. However, as expected, the results between the ground-based retrievals and satellite retrievals agree less well. Overall, the method is clearly presented, and the paper is well organized. The following lists my detailed comments on the paper.
1. The method is solid and well designed, while claiming the method to be the “first fully physical and universally applicable method” is less unsubstantiated. Such statements should be carefully considered.
2. The ice cloud properties could be quite different from model to model, and the model by Fu (1996) is relatively outdated. Would it possible considered a more advanced one that better represented ice cloud radiative effects? For example, Yang et al. (2013,https://doi.org/10.1175/JAS-D-12-039.1) or Liu et al. (2014, doi:10.5194/acp-14-13719-2014) give much improved models. Maybe, the ice cloud model itself will not affect the model results, and it is also worth mention.
3. It is always challenging to compared retrieval results of different kinds, i.e., the ground-based one from this study and the MDOSI one. Thus, the less agreed results in Figures 9 and 10 are expected due to multiple factors that may influence the comparison. Are there results really needed to demonstrate the effectiveness of the current methods?
4. The necessity of wind speed on the method is also not that straightforward, and maybe more physical explanation should be given.
5. The paper is overall long and in significant details. Maybe more materials can be moved to appendix.
6. The last paragraph of the conclusion gives the implementation of the COD retrieval algorithm, which, in my opinion, is less convincing. This may be improved as well.
Citation: https://doi.org/10.5194/egusphere-2025-3743-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,303 | 102 | 19 | 1,424 | 24 | 21 |
- HTML: 1,303
- PDF: 102
- XML: 19
- Total: 1,424
- BibTeX: 24
- EndNote: 21
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The manuscript presents a rigorous approach to retrieve cloud optical depth (τc) directly from photovoltaic power measurements.
The abstract describes it as “the first fully physical and universally applicable method … to estimate τc from PV measurements”.
It may be noted that the broader concept of exploiting PV-panel electrical output in synergy with a radiative-transfer model to infer an atmospheric optical-depth parameter was introduced earlier in Lolli (2021), Sensors 21, 6342; https://doi.org/10.3390/s21196342, which retrieved aerosol optical depth (AOD) at 550 nm from PV power.
Although the methodology in Lolli (2021) differs, i.e. using the Fu–Liou–Gu model and an iterative inversion, the two studies share the same fundamental synergy of radiative-transfer modelling coupled with PV-power measurements. Including Lolli (2021) as a reference could be useful for readers interested in the historical development of PV-based atmospheric-retrieval techniques.