Predictive Capability of WRF Cycling 3DVAR: LiDAR Assimilation at FINO1
Journal article, Peer reviewed
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Original versionJournal of Physics: Conference Series (JPCS). 2021, 2018, 012006. 10.1088/1742-6596/2018/1/012006
In this study, we assess tentatively the predictive capability of the Weather Research and Forecasting (WRF) model and its Three-Dimensional Variational (3DVAR) system. We study the impact of LiDAR data assimilation on predictions of wind speed and direction. The simulation domain covers the German wind energy research platform FINO1 in the Southern North Sea, at which the LiDAR data were recorded. The results demonstrate the significance of applying the LiDAR Data Assimilation (DA) with a short assimilation interval (1-hour) to improve the wind prediction and the temporal and spatial representation of low level jet events, compared with an experiment without DA. Furthermore, we briefly examine the impacts of data assimilation cycle length on predictive performance of the DA system, and spatial variability of wind speed over the study area at a height of 250 m.