Classifying direct normal irradiance 1‑minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations

Autor: Schroedter-Homscheidt, Marion, Kosmale, M., Saint-Drenan, Y.‑M.
Přispěvatelé: Deutsches Zentrum für Luft- und Raumfahrt [Oberpfaffenhofen-Wessling] (DLR), Finnish Meteorological Institute (FMI), Centre Observation, Impacts, Énergie (O.I.E.), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Meteorologische Zeitschrift, Vol 29, Iss 2, Pp 131-145 (2020)
Meteorologische Zeitschrift
Meteorologische Zeitschrift, Berlin: A. Asher & Co., 2020, 29 (2), pp.131-145. ⟨10.1127/metz/2020/0998⟩
ISSN: 0941-2948
0369-1845
DOI: 10.1127/metz/2020/0998⟩
Popis: t Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. Eight variability classes were previously defined for the 1 min resolved direct normal irradiance (DNI) variability inside an hour. In this study spatial structural parameters derived fromsatellite-based cloud observations are used as classifiers in order to detect the associated direct normal irradiance (DNI) variability class in a supervised classification scheme. A neighbourhood of 3×3 to 29×29 satellite pixels is evaluated to derive classifiers describing the actual cloud field better than just using a single satellite pixel at the location of the irradiance observation. These classifiers include cloud fraction in a window around the location of interest, number of cloud/cloud free changes in a binary cloud mask in this window, number of clouds, and a fractal box dimension of the cloud mask within the window. Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well – the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.g. to quantify short-term variability impacts on solar power production. This approach is based on satellite-based cloud observations only and does not require any ground observations of the location of interest.
Databáze: OpenAIRE
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