Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.
<p>The motion of clouds at a given location can be detected using ground-based all-sky imagers that frequently acquire images of the sky dome. Motion flow is used for minute-scale forecasting of cloud cover and solar irradiance, for example in the case of forecasting photovoltaic power production. While visible-range sky cameras are often applied for this purpose, they neither allow to detect the altitude of clouds, nor accurately detect clouds at night time. However, thermal-infrared all-sky imagers, such as Reuniwatt&#8217;s Sky InSight, retrieve brightness temperatures with constant accuracy at day and night time. This allows for the retrieval of diverse cloud parameters such as cloud base height. Atmospheric wind vectors can be derived and geolocalised by combining cloud motion detection and cloud-base height retrieval. In this study, we evaluate the accuracy of atmospheric wind vector retrievals by the means of the Sky InSight. Radiosoundings and wind profiler observations are used as a reference.</p>
<p>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&#8217;s thermal-infrared all-sky imager &#8220;Sky InSight&#8221;&#169;, installed at the Lindenberg Meteorological Observatory &#8211; Richard-Assmann-Observatory (MOL-RAO) in Germany, a ceilometer in the same location and the computer vision algorithm &#8220;Optical Flow&#8221; (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.</p>
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