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Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts

German Aerospace Center (DLR), Institute of Solar Research, Ctra de Senes s/n km 4, 04200 Tabernas, Spain
DLR, Institut für Vernetzte Energiesysteme, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Laboratory of Atmospheric Physics, Department of Physics, University of Patras, 26500 Patras, Greece
CIEMAT Energy Department–Renewable Energy Division, Av. Complutense 40, 28040 Madrid, Spain
MINES ParisTech, 06904 Sophia Antipolis CEDEX, France
NETRA, NTPC Ltd., E3 Echotech Ii, Udhyog Vihar Greater Noida, Gautam Budh Nagar. Pin 201306, Up, India
CSP Services. Paseo de Almería 73, 2, 04001 Almería, Spain
DLR, Institute of Solar Research, Linder Höhe, 51147 Cologne, Germany
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1059;
Received: 26 March 2019 / Revised: 23 April 2019 / Accepted: 29 April 2019 / Published: 5 May 2019
(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)
PDF [7059 KB, uploaded 6 May 2019]


The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts. View Full-Text
Keywords: Nowcasting; uncertainty; all sky imager; cloud camera; solar irradiance; irradiance map; DNI; DNI variability classification; cloud modeling Nowcasting; uncertainty; all sky imager; cloud camera; solar irradiance; irradiance map; DNI; DNI variability classification; cloud modeling

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Nouri, B.; Wilbert, S.; Kuhn, P.; Hanrieder, N.; Schroedter-Homscheidt, M.; Kazantzidis, A.; Zarzalejo, L.; Blanc, P.; Kumar, S.; Goswami, N.; Shankar, R.; Affolter, R.; Pitz-Paal, R. Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts. Remote Sens. 2019, 11, 1059.

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