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Renate Forkel

Researcher at Karlsruhe Institute of Technology

Publications -  85
Citations -  3982

Renate Forkel is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Aerosol & Air quality index. The author has an hindex of 36, co-authored 85 publications receiving 3489 citations. Previous affiliations of Renate Forkel include Fraunhofer Society & Ludwig Maximilian University of Munich.

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Online coupled regional meteorology chemistry models in Europe: current status and prospects

TL;DR: A comprehensive review of the current research status of online coupled meteorology and atmospheric chemistry modelling within Europe and highlights selected scientific issues and emerging challenges that require proper consideration to improve the reliability and usability of these models for the three scientific communities.
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Application of a multiscale, coupled MM5/chemistry model to the complex terrain of the VOTALP valley campaign

TL;DR: In this paper, a coupled complex meteorology/chemistry model has been used to simulate the flow field and the concentration fields of atmospheric pollutants in Alpine valleys during the VOTALP (Vertical Ozone Transports in the ALPs) Valley Campaign in southern Switzerland.
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Evaluation of operational on-line-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: Ozone

TL;DR: The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models on common emissions and boundary conditions as mentioned in this paper.
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Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII

TL;DR: In this paper, the authors investigated the optimal ensemble size and quality of the members based on a clustering methodology to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill.