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  • Project title


    Ground-based remote sensing of the atmosphere for improving the characterization of microphysical cloud properties and for improving the load prediction of renewable energies. Project lead: Prof. M. Wendisch, Jun-Prof H. Kalesse, Dr. M. Schäfer, Dr. A. Ehrlich. Young scientists: M. Lochmann, W. Schimmel, A. Emmanouilidis


    Beneficiary


    University of Leipzig

    Leipzig Institute for Meteorology (LIM)


    Project summary


    Ground-based remote sensing of the atmosphere serves both, the basic research of clouds and precipitation, and in the operational service of weather forecasting as an important building block for the power prognosis of renewable energies. Within this project there are two main objectives: Firstly, it will be analysed how artificial neural networks (ANN) can be optimized for power forecasting of Photovoltaics (PV) and wind turbines, if additional data from weather stations and ground-based remote sensing measurements are implemented. On the other hand, in two further subprojects for cloud observations, retrieval algorithms will be developed to improve and extend the determination of cloud properties. The focus will be on instrument synergies for the derivation of cloud droplet concentrations and the development of application-specific ANN for the characterization of the distribution of liquid water in mixed-phase clouds. The project is divided into three work packages: AP1: Testing the influence of additional measurement data from weather stations and ground-based remote sensing on the power prognosis of PV and wind power plants using artificial neural networks (ANN) AP2: Development of a cloud radar and lidar based ANN for detection of liquid water in clouds AP3: Development of a cloud droplet concentration retrieval based on synergistic remote sensing observations




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    Last update 26 July 2019 by A. Foth

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