# Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

## 1. Introduction

^{1}) to forecast satellite-derived effective cloud albedo up to 4 h, and using SPECMAGIC NOW, surface irradiances were calculated. In addition, in the context of short-term forecasting of cloud albedo, a comparison of two optical flow methods TV-L

^{1}and Farnebäck, was performed, with the first method performing better than the former one. Teerakawanich et al. [30] used the optical flow algorithm proposed by Farnebäck to track cloud movement on sky images and the calculated vectors used as input in a convolution neural network (CNN) in order to predict cloud cover events 1 to 2 min in advance. The method showed better results during wintertime when the clear sky and thick clouds are the prevailing sky conditions. During those days, the prediction error was as good as 5%, with this error increasing in the presence of thin clouds. Li et al. [31] performed cloud tracking and forecasting to satellite images using Shi–Tomasi’s method to select features to track, image filtering and finally Horn and Schunck (HS) and Lucas–Kanade (LK) optical flow method to track those features. Although the optical flow methods used originally are dense, the segmentation performed by Shi–Tomasi’s method resulted in a sparse vector field, which requires less computational time. They found that LK with a Gaussian filter as a preprocessor was the most accurate and efficient method. However, their main finding was that satellite cloud images could be useful for irradiance predictions, providing predictable features in wide areas with long-time movement. In this study, for the calculation of the dense optical flow, the Farnebäck [32] and TV-L

^{1}[33] algorithms have been implemented using OpenCV open-source library.

## 2. Materials and Methods

^{1}(FRB and TVL hereafter) applied either on COT and on CMF. We select to treat both variables in order to investigate differences in the final product. COT is a satellite-based product that can be directly retrieved in real time without any post-processing. It describes the attenuation of DSSI reaching the surface through a cloud. On the other hand, CMF describes more directly the cloud effect on DSSI (it describes the attenuation as a fraction compared with cloudless sky DSSI) and especially the important variations caused by low COT values. Cloud inputs are then fed to the FRTM to produce the operational forecast.

#### 2.1. Clouds Observation

_{cls},

_{cls}is the irradiance under clear sky conditions as simulated by the FRTM [36].

#### 2.2. Cloud Motion Vector

#### 2.2.1. Optical Flow

_{1}and u

_{2}, so it cannot be solved, and in order to obtain the solution of Equation (3), an additional constrain can provide the other equation needed.

^{1}norm and a regularization term using the total variation of the flow. In the bibliography of the aforementioned methods, both algorithms were evaluated on a widely used test sequence with known velocity filed, the Yosemite sequence of images, and FRB and TVL methods had small average angular error compared to other techniques [32,33].

#### 2.2.2. FRB

**x**= (x,y)

^{T}) are expressed as:

**x**), which is the desirable field; if local polynomial approximations are used instead of global and after a series of approximations and parameterizations, a robust algorithm is provided, described by [32] in detail. One of the basic assumptions of the FRB method is that the displacement field is slowly varying in space in order to implement the method over the wider areas than point-wise, which is too noisy. In addition, the FRB method works well with small displacements, in contrast to large displacements, which introduce errors in the model. Using iterations and a priori displacement field in the first step is one-way to obtain more accurate results, and especially the approach of multiscaled displacement estimation can obtain increasingly more accurate estimates starting from a coarse scale to get the first estimate of the displacement field and propagate this through finer scales.

_{max}− X)/(X

_{max}− X

_{min}), where X represents each parameter value before the normalization, and the X

_{max}and X

_{min}are the maximum and minimum values of the range of the parameter, respectively.

#### 2.2.3. TVL

#### 2.3. Fast Radiative Transfer Model

#### 2.4. Statistical Tools

_{i}= x

_{f}− x

_{0}are the residuals calculated as the difference between the forecasted values (x

_{f}) and the real observed values (x

_{0}), and N is the total number of values. RMSE describes the overall spread of the error distribution, while MBE quantifies the bias and additionally detects overestimations (MBE > 0) or underestimations (MBE < 0). Finally, the mean absolute error (MAE), the correlation coefficient (r), as well as the standard deviation (σ) were used to represent the proportion of the variability between the forecasted and real observed values.

## 3. Results

#### 3.1. Short-Term Forecasting Overview

#### 3.2. FRB vs. TVL

#### 3.3. COT vs. CMF

#### 3.4. CMV vs. PRS

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

ACS | Average cloud speed |

CCM | Cross-correlation method |

CDC | Cloud direction change |

CMF | Cloud modification factor |

CMV | Cloud motion vector |

CNN | convolution neural network |

COT | Cloud optical thickness |

CPP-SICCS | Cloud physical properties of the surface insolation under clear and cloudy skies by SEVIRI |

CPU | Central processing unit |

DSSI | Downwelling surface solar irradiation |

EO | Earth observation |

EUMETSAT | European Organisation for the Exploitation of Meteorological Satellites |

FRB | Farnebäck motion flow method |

FRTM | Fast radiative transfer model |

FY-4A | FengYun-4A |

HPC | High-performance computing |

HS | Horn and Schunck |

IEA | International Energy Agency |

LK | Lucas–Kanade |

LUT | Lookup table |

MAE | Mean absolute error |

MBE | Mean bias error |

MSG | Meteosat second generation |

NWP | Numerical weather prediction |

OpenCV | Open-source computer vision library |

PIV | Particle image velocimetry |

PRS | Persistent |

PV | Photovoltaic |

r | Correlation coefficient |

RAM | Random-access memory |

RMSE | Root mean square error |

SAFNWC | Satellite application facilities to support nowcasting and very short range forecasting |

SEVIRI | Spinning enhanced visible and infrared imager |

SPECMAGIC | Spectrally resolved mesoscale atmospheric global irradiance code |

TVL | Total variation of the motion flow method using the L^{1} norm |

TRS | Transition |

UTC | Coordinated universal time |

VOF | Variational optical flow |

σ | Standard deviation |

## References

- IEA. Renewables 2019; IEA: Paris, France, 2019; Available online: https://www.iea.org/reports/renewables-2019 (accessed on 16 November 2020).
- IEA. Solar Energy: Mapping the Road Ahead; IEA: Paris, France, 2019; Available online: https://www.iea.org/reports/solar-energy-mapping-the-road-ahead (accessed on 16 November 2020).
- IEA. Getting Wind and Sun onto the Grid: A Manual for Policy Makers; IEA: Paris, France, 2017; Available online: https://euagenda.eu/upload/publications/untitled-77295-ea.pdf (accessed on 16 November 2020).
- Diagne, M.; David, M.; Lauret, P.; Boland, J.; Schmutz, N. Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renew. Sustain. Energy Rev.
**2013**, 27, 65–76. [Google Scholar] [CrossRef][Green Version] - Antonanzas, J.; Osorio, N.; Escobar, R.; Urraca, R.; Martinez-de-Pison, F.J.; Antonanzas-Torres, F. Review of photovoltaic power forecasting. Sol. Energy
**2016**, 136, 78–111. [Google Scholar] [CrossRef] - Sengupta, M.; Habte, A.; Gueymard, C.; Wilbert, S.; Renné, D. Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications; Issue NREL/TP-5D00-68886; National Renewable Energy Laboratory (NREL): Denver, CO, USA, 2017. Available online: https://www.nrel.gov/docs/fy18osti/68886.pdf (accessed on 16 November 2020).
- Menzel, W.P. Cloud Tracking with Satellite Imagery: From the Pioneering Work of Ted Fujita to the Present. Bull. Am. Meteorol. Soc.
**2001**, 82, 33–47. [Google Scholar] [CrossRef] - Schmetz, J.; Holmlund, K.; Hoffman, J.; Strauss, B.; Mason, B.; Gaertner, V.; Koch, A.; Van De Berg, L. Operational cloud-motion winds from Meteosat infrared images. J. Appl. Meteorol.
**1993**, 32, 1206–1225. [Google Scholar] [CrossRef][Green Version] - Hammer, A.; Heinemann, D.; Lorenz, E.; Lückehe, B. Short-term forecasting of solar radiation: A statistical approach using satellite data. Sol. Energy
**1999**, 67, 139–150. [Google Scholar] [CrossRef] - Pelland, S.; Remund, J.; Kleissl, J.; Oozeki, T.; De Brabandere, K. Photovoltaic and Solar Forecasting: State of the Art; IEA PVPS Task 14, Subtask 3.1. Report Iea-PVPS T14–01; International Energy Agency: Paris, France, 2013; Available online: https://iea-pvps.org/key-topics/photovoltaics-and-solar-forecasting-state-of-art-report-t1401-2013/ (accessed on 16 November 2020).
- Hammer, A.; Heinemann, D.; Hoyer, C.; Kuhlemann, R.; Lorenz, E.; Müller, R.; Beyer, H.G. Solar energy assessment using remote sensing technologies. Remote Sens. Environ.
**2003**, 86, 423–432. [Google Scholar] [CrossRef] - Lorenz, E.; Hammer, A.; Heinemann, D. Short term forecasting of solar radiation based on satellite data. In Proceedings of the EUROSUN2004 ISES Europe Solar Congress, Freiburg, Germany, 20–23 June 2004; pp. 841–848. Available online: https://www.osti.gov/etdeweb/biblio/20637868 (accessed on 10 December 2020).
- Perez, R.; Kivalov, S.; Schlemmer, J.; Hemker, K.; Renné, D.; Hoff, T.E. Validation of short and medium term operational solar radiation forecasts in the US. Sol. Energy
**2010**, 84, 2161–2172. [Google Scholar] [CrossRef] - Bosch, J.L.; Kleissl, J. Cloud motion vectors from a network of ground sensors in a solar power plant. Sol. Energy
**2013**, 95, 13–20. [Google Scholar] [CrossRef] - Alonso-Montesinos, J.; Batlles, F.J.; Portillo, C. Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images. Energy Convers. Manag.
**2015**, 105, 1166–1177. [Google Scholar] [CrossRef] - Chow, C.W.; Urquhart, B.; Lave, M.; Dominguez, A.; Kleissl, J.; Shields, J.; Washom, B. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed. Sol. Energy
**2011**, 85, 2881–2893. [Google Scholar] [CrossRef][Green Version] - Wang, P.; van Westrhenen, R.; Meirink, J.F.; van der Veen, S.; Knap, W. Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations. Sol. Energy
**2019**, 177, 47–58. [Google Scholar] [CrossRef] - Yang, L.; Gao, X.; Li, Z.; Jia, D.; Jiang, J. Nowcasting of surface solar irradiance using FengYun-4 satellite observations over China. Remote Sens.
**2019**, 11, 1984. [Google Scholar] [CrossRef][Green Version] - Fleet, D.J.; Weiss, Y. Optical Flow Estimation. In Handbook of Mathematical Models in Computer Vision; Paragios, N., Chen, Y., Faugeras, O.D., Eds.; Springer: Berlin/Heidelberg, Germany, 2006; pp. 237–257. ISBN 978-0-387-26371-7. [Google Scholar] [CrossRef]
- Wood-Bradley, P.; Zapata, J.; Pye, J. Cloud tracking with optical flow for short-term solar forecasting. In Proceedings of the 50th Conference of the Australian Solar Energy Society, Melbourne, Australia, 2–7 November 2012. [Google Scholar]
- Lucas, B.D.; Kanade, T. An Iterative Image Registration Technique with an Application to Stereo Vision. In Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI ‘81), Vancouver, BC, Canada, 24–28 August 1981; pp. 674–679. [Google Scholar]
- Cros, S.; Sébastien, N.; Liandrat, O.; Schmutz, N. Cloud pattern prediction from geostationary meteorological satellite images for solar energy forecasting. In Proceedings of the SPIE Remote Sensing, Amsterdam, The Netherlands, 22–25 September 2014. [Google Scholar] [CrossRef]
- Nonnenmacher, L.; Coimbra, C.F.M. Streamline-based method for intra-day solar forecasting through remote sensing. Sol. Energy
**2014**, 108, 447–459. [Google Scholar] [CrossRef] - Sun, D.; Roth, S.; Black, M.J. Secrets of optical flow estimation and their principles. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13–18 June 2010; pp. 2432–2439. [Google Scholar] [CrossRef][Green Version]
- Sun, D.; Roth, S.; Black, M.J. A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis.
**2014**, 106, 115–137. [Google Scholar] [CrossRef][Green Version] - Chow, C.W.; Belongie, S.; Kleissl, J. Cloud motion and stability estimation for intra-hour solar forecasting. Sol. Energy
**2015**, 115, 645–655. [Google Scholar] [CrossRef] - Peng, Z.; Yu, D.; Huang, D.; Heiser, J.; Kalb, P. A hybrid approach to estimate the complex motions of clouds in sky images. Sol. Energy
**2016**, 138, 10–25. [Google Scholar] [CrossRef] - Du, J.; Min, Q.; Zhang, P.; Guo, J.; Yang, J.; Yin, B. Short-term solar irradiance forecasts using sky images and radiative transfer model. Energies
**2018**, 11, 1107. [Google Scholar] [CrossRef][Green Version] - Urbich, I.; Bendix, J.; Müller, R. A novel approach for the short-term forecast of the effective cloud albedo. Remote. Sens.
**2018**, 10, 955. [Google Scholar] [CrossRef][Green Version] - Teerakawanich, N.; Leelaruji, T.; Pichetjamroen, A. Short term prediction of sun coverage using optical flow with GoogLeNet. Energy Rep.
**2020**, 6, 526–531. [Google Scholar] [CrossRef] - Li, Y.; Chen, X.; Yang, M. Optical flow based solar irradiance forecasting in satellite images. In Proceedings of the 2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019, Irkutsk, Russia, 4–9 August 2019; pp. 442–447. [Google Scholar] [CrossRef]
- Farnebäck, G. Two-Frame Motion Estimation Based on Polynomial Expansion. Lect. Notes Comput. Sci.
**2003**, 2749, 363–370. [Google Scholar] [CrossRef][Green Version] - Pérez, J.S.; Meinhardt-Llopis, E.; Facciolo, G. TV-L1 Optical Flow Estimation. Image Process. Line
**2013**, 3, 137–150. [Google Scholar] [CrossRef][Green Version] - Urquhart, B.; Ghonima, M.; Nguyen, D. (Andu); Kurtz, B.; Chow, C.W.; Kleissl, J. Sky-Imaging Systems for Short-Term Forecasting. In Solar Energy Forecasting and Resource Assessment; Kleissl, J., Ed.; Elsevier Academic Press: Boston, MA, USA, 2013; pp. 195–232. [Google Scholar] [CrossRef]
- MétéoFrance. Algorithm Theoretical Basis Document for Cloud Products (CMa-PGE01 v3.2, CT-PGE02 v2.2 & CTTH-PGE03 v2.2); Technical Report SAF/NWC/CDOP/MFL/SCI/ATBD/01; MétéoFrance: Paris, France, 2016. [Google Scholar]
- Kosmopoulos, P.G.; Kazadzis, S.; Taylor, M.; Raptis, P.I.; Keramitsoglou, I.; Kiranoudis, C.; Bais, A.F. Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements. Atmos. Meas. Technol.
**2018**, 11, 907–924. [Google Scholar] [CrossRef][Green Version] - Pfeifroth, U.; Kothe, S.; Trentmann, J. Validation report: Meteosat solar surface radiation and effective cloud albedo climate data record (Sarah 2), EUMETSAT SAF CM Validation report with reference number SAF/CM/DWD/VAL/ METEOSAT/HEL, 2.1. EUMETSAT Satell. Appl. Facil. Clim. Monit
**2016**. [Google Scholar] [CrossRef] - Gueymard, C.A. The sun’s total and spectral irradiance for solar energy applications and solar radiation models. Sol. Energy
**2004**, 76, 423–453. [Google Scholar] [CrossRef] - Zach, C.; Poth, T.; Bischof, A. A duality based approach for real-time TVL1 optical flow. In Pattern Recognition; Lecture Notes in Computer Science; Hamprecht, F.A., Schnorr, C., Jahne, B., Eds.; Springer: Berlin/Heidelberg, Germany, 2007; Volume 4713, Chapter 22; pp. 214–223. [Google Scholar] [CrossRef]
- OpenCV Object Tracking and Optical Flow Algorithms Including Farnebäck and TVL1. Available online: https://docs.opencv.org/3.4/dc/d6b/group__video__track.html#ga5d10ebbd59fe09c5f650289ec0ece5af (accessed on 3 December 2020).
- Horn, B.K.; Schunck, B.G. Determining optical flow. Artif. Intell.
**1981**, 17, 185–203. [Google Scholar] [CrossRef][Green Version] - Chambolle, A. An Algorithm for Total Variation Minimization and Applications. J. Math Imaging Vis.
**2004**, 20, 89–97. [Google Scholar] [CrossRef] - Mayer, B.; Kylling, A. Technical note: The libRadtran software package for radiative transfer calculations–description and examples of use. Atmos. Chem. Phys.
**2005**, 5, 1855–1877. [Google Scholar] [CrossRef][Green Version] - Solar Energy Applications Web Service. Available online: http://solea.gr/solar-energy-management/ (accessed on 3 December 2020).
- Urbich, I.; Bendix, J.; Muller, R. The seamless solar radiation (SESORA) forecast for solar surface irradiance–Method and validation. Remote Sens.
**2019**, 11, 2576. [Google Scholar] [CrossRef][Green Version] - Coimbra, C.; Pedro, H. HAIMOS Ensemble Forecasts for Intra-Day and Day-Ahead GHI, DNI and Ramps. University of California San Diego, Solar Forecasting 2, 2019, DE-EE0008216. Available online: http://coimbra-server3.dynamic.ucsd.edu/doesf2/data/8216-University_of_California_San_Diego_DataReport.pdf (accessed on 3 December 2020).
- Taylor, K.E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res.
**2001**, 106, 7183–7192. [Google Scholar] [CrossRef] - Vallance, L.; Charbonnier, B.; Paul, N.; Dubost, S.; Blanc, P. Towards a standardized procedure to assess solar forecast accuracy: A new ramp and time alignment metric. Solar Energy
**2017**, 150, 408–422. [Google Scholar] [CrossRef] - Verbois, H.; Blanc, P.; Huva, R.; Saint-Drenan, Y.M.; Rusydi, A.; Thiery, A. Beyond quadratic error: Case-study of a multiple criteria approach to the performance assessment of numerical forecasts of solar irradiance in the tropics. Renew. Sustain. Energy Rev.
**2020**, 117, 109471. [Google Scholar] [CrossRef]

**Figure 1.**The cloud modification factor (CMF) for 14, 22 and 30 April 2020 at 12:15 UTC, as calculated by the fast radiative transfer models (FRTM) using as input the Meteosat second generation (MSG) cloud optical thickness (COT).

**Figure 3.**The sensitivity parameters optimization for the CMV COT approach as a function of the normalized score of forecasted pixels for 15 min-ahead time horizon. (

**a**) Downsampling rate, (

**b**) Pyramid layers, (

**c**) Number of search iterations, (

**d**) Window size, (

**e**) Size of pixel neighborhood, (

**f**) Value of Gaussian filter.

**Figure 5.**The real (

**a**) and the FRB forecasted (

**b**) COT map for 30 April 2020 at 12:15 UTC, as well as the corresponding real (

**c**) and FRB forecasted (

**d**) CMF maps for 3 h-ahead (12 time-step forecasts).

**Figure 6.**Contour plot of the FRB forecasted COT mean absolute error (MAE) percentage as a function of COT classes for 3 h ahead at 15 min intervals.

**Figure 7.**The CMF error during the 3 case study days for the PRS, FRB and TVL short-term forecasting approaches as a function of RMSE and MBE (

**a**), and a zoomed plot for the two CMV methods (

**b**) as to highlight the similarities and discrepancies of the same statistics.

**Figure 8.**The cloudy sky to clear sky TRN forecasting error of CMF in terms of RMSE during the 3 case study days for the PRS, COT, and CMF short-term forecasting approaches for 15 min, 1, 2 and 3 h forecast horizon.

**Figure 9.**Absolute difference between the forecast and real CMF for 15 min ahead as a function of the CMF change (

**a**). The RMSE in terms of CMF for the same forecast time horizon for various CMF classes (

**b**).

**Figure 10.**Density scatter plots of 30 April 2020 covering Europe and North Africa (i.e., 1.5 million pixels) for the PRS (upper line) and FRB forecasted COT (lower line) short-term forecast approaches, for the 15 min, 1, 2 and 3 h ahead time horizons.

**Figure 11.**Classification of PRS and forecasted COT in terms of MAE ± 1σ for the 15 min forecast time horizon.

**Figure 12.**Difference between the CMV and the real CMF during 14 April 2020 at 15 min, 1, 2 and 3 h ((

**a**–

**d**), respectively) ahead time horizons, as well as the difference between the PRS and the real CMF for the same time horizons (

**e**–

**h**).

**Figure 13.**Taylor diagram for the overall CMF error for PRS and CMV approaches under all and TRN conditions.

**Table 1.**The cloud motion vector characteristics and statistics of the selected days in terms of average cloud speed (ACS) and cloud direction change (CDC) under all-sky conditions, clear to cloudy and cloudy to clear sky condition transition (TRNs) and cloudy persistence during the studied 3 h time horizon, i.e., from 9:15 to 12:15 UTC.

Cloud Motion Vector Analysis | 14/04/2020 | 22/04/2020 | 30/04/2020 |
---|---|---|---|

Total number of data comparison | 1.45 million/image × 12 forecasts × 3 days = 52.2 million | ||

Cloud coverage (million pixels) | 9.0 (52%) | 5.6 (32%) | 9.9 (57%) |

ACS (km/h) | 37 | 11 | 23 |

CDC (degrees) | 3 | 13 | 7 |

Total number of clear to cloudy TRN | 2.8 (16%) | 1.7 (9.7%) | 2.5 (14.5%) |

ACS (km/h) | 49 | 17 | 36 |

CDC (degrees) | 6 | 21 | 13 |

Total number of cloudy to clear TRN | 1.3 (7.6%) | 1.1 (6.2%) | 1.0 (5.5%) |

ACS (km/h) | 42 | 14 | 27 |

CDC (degrees) | 2 | 15 | 7 |

Total number of cloudy to cloudy | 5.3 (30.3%) | 3.0 (17.2%) | 6.8 (39%) |

ACS (km/h) | 20 | 2 | 6 |

CDC (degrees) | 1 | 3 | 1 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Kosmopoulos, P.; Kouroutsidis, D.; Papachristopoulou, K.; Raptis, P.I.; Masoom, A.; Saint-Drenan, Y.-M.; Blanc, P.; Kontoes, C.; Kazadzis, S.
Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation. *Energies* **2020**, *13*, 6555.
https://doi.org/10.3390/en13246555

**AMA Style**

Kosmopoulos P, Kouroutsidis D, Papachristopoulou K, Raptis PI, Masoom A, Saint-Drenan Y-M, Blanc P, Kontoes C, Kazadzis S.
Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation. *Energies*. 2020; 13(24):6555.
https://doi.org/10.3390/en13246555

**Chicago/Turabian Style**

Kosmopoulos, Panagiotis, Dimitris Kouroutsidis, Kyriakoula Papachristopoulou, Panagiotis Ioannis Raptis, Akriti Masoom, Yves-Marie Saint-Drenan, Philippe Blanc, Charalampos Kontoes, and Stelios Kazadzis.
2020. "Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation" *Energies* 13, no. 24: 6555.
https://doi.org/10.3390/en13246555