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  • 1
    Publication Date: 2018-09-01
    Print ISSN: 0960-1481
    Electronic ISSN: 1879-0682
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Elsevier
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  • 2
    Publication Date: 2017-09-29
    Description: Cirrus clouds play an important role in climate as they tend to warm the Earth–atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m−2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.
    Print ISSN: 1867-1381
    Electronic ISSN: 1867-8548
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
    Publication Date: 2018-07-19
    Description: Usually, neural networks trained on historical feed-in time series of wind turbines deterministically predict power output over the next hours to days. Here, the training goal is to minimise a scalar cost function, often the root mean square error (RMSE) between network output and target values. Yet similar to the analog ensemble (AnEn) method, the training algorithm can also be adapted to analyse the uncertainty of the power output from the spread of possible targets found in the historical data for a certain meteorological situation. In this study, the uncertainty estimate is achieved by discretising the continuous time series of power targets into several bins (classes). For each forecast horizon, a neural network then predicts the probability of power output falling into each of the bins, resulting in an empirical probability distribution. Similiar to the AnEn method, the proposed method avoids the use of costly numerical weather prediction (NWP) ensemble runs, although a selection of several deterministic NWP forecasts as input is helpful. Using state-of-the-art deep learning technology, we applied our method to a large region and a single wind farm. MAE scores of the 50-percentile were on par with or better than comparable deterministic forecasts. The corresponding Continuous Ranked Probability Score (CRPS) was even lower. Future work will investigate the overdispersiveness sometimes observed, and extend the method to solar power forecasts.
    Print ISSN: 1680-7340
    Electronic ISSN: 1680-7359
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
    Publication Date: 2017-04-05
    Description: Cirrus clouds play an important role in climate as they tend to warm the Earth-Atmosphere system. Nevertheless they remain one of the largest uncertainties in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0 respectively, that are retrieved by CALIOP. Among the cirrus free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8, and of 100 % or less for cirrus clouds with an optical thickness down to 0.07, with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m−2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS does also retrieve an opacity flag, which tells whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.
    Electronic ISSN: 1867-8610
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 5
    Publication Date: 2022-02-18
    Description: Die zunehmende Abkehr vom ursprünglichen EEG-Vergütungssystem mit einer festen Einspeisevergütung hin zu einer mehr und mehr marktorientierten Ausrichtung führt zu der Frage, ob die Umstrukturierung des EEG am Ende zu einer neuen Phase der Energiewende führt, der Neo-EEG-Phase. Im vorliegenden Artikel werden die Veränderungen und Entwicklungsphasen des EEG mit besonderem Blick auf die Akteure des Stromsystems analysiert. Im Kontext der Energiewende können die zu beobachtenden und teils deutlich einschneidenden Veränderungen für alle Akteure des Systems durchaus als "Akteurswende" verstanden werden.
    Keywords: ddc:600
    Repository Name: Wuppertal Institut für Klima, Umwelt, Energie
    Language: German
    Type: conferenceobject , doc-type:conferenceObject
    Format: application/pdf
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