Assimilation of atmospheric wind vectors retrieved via Optical flow algorithm and a thermal all-sky imager

Ground-based remote sensing of wind is currently dominated by radar profilers and wind lidars, which deliver profiles of excellent quality and high update rates. Unfortunately, the relative high costs of these devices have so far strongly limited their geographical coverage. On the other hand, infrared all-sky imagers are more affordable instruments, that can provide valuable information at day and night time, not only about cloud cover, but also about wind via computer vision techniques. In this work we investigate for the first time, whether this kind of derived wind observations can be used for data assimilation. A Reuniwatt’s thermal-infrared all-sky imager “Sky InSight”©, installed at the Lindenberg Meteorological Observatory – Richard-Assmann-Observatory (MOL-RAO) in Germany, a ceilometer in the same location and the computer vision algorithm “Optical Flow” (OF) were used to retrieve atmospheric wind vectors at cloud base height: subsequent brightness temperature photographs delivered by our imager were geometry-corrected and afterwards analysed by the OF-procedure, obtaining a set of atmospheric wind vectors in the surroundings of the camera. These vectors were finally rescaled and averaged to generate one overall wind observation, valid at the cloud base height retrieved by the ceilometer, for the time period when the photographs were taken. Afterwards, these derived wind observations were assimilated into the German regional weather prediction system, which uses the limited area version of the ICON (ICOsahedral Nonhydrostatic) model and the Local Ensemble Transform Kalman Filter (LETKF). In this work we evaluate the quality of these observations as well as their data assimilation impact for a set of monitoring experiments.