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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 mid-latitudes 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 is 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 Reunion Island, a tropical island in the South-West Indian Ocean. The multi-layer 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 mid troposphere. Ground-based GHI observations at 12 sites on Reunion Island are used to quantify the impact of CWP DA. Over a total of 44 days during austral summer time, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 hours. Root Mean Squared Error and Mean Absolute Error are reduced by 4% and 3% respectively. [ABSTRACT FROM AUTHOR]