Precipitation Retrieval Using IR Channels Brightness Temperature from SEVIRI

This study is performed to retrieve precipitation amount using Spinning Enhanced Visible and InfraRed Imager (SEVIRI) from Meteosat Second Generation (MSG). According to the relationship between the infrared channels brightness temperature and the microphysical and optical properties of the clouds (such as cloud top temperature, cloud top height, optical thickness, particle size, and particle phase), and the influence of changes in these properties on the determination of precipitation amount and intensity, the relationship between brightness temperature and precipitation have been studied for two stations in Hormozgan province. The performance of an artificial neural network and also several regression models to estimate precipitation has been evaluated. The results showed that the exponential Gaussian process regression model with performing principal component analysis by RMSE of 0.44, POD of 0.96 and the HSS of 0.67 for precipitation threshold 0.1 mm for the less than 10 mm precipitation data set have the best performance. The artificial neural network also presented a RMSE of 1.27 which indicates weaker performance in comparison with the regression model but showed good performance in distinguishing precipitation conditions from non-precipitation conditions (POD of 0.85 and HSS of 0.48). By comparing the correlation between precipitation and brightness temperature of infrared channels (average 0.36), and the correlation between observed and retreived precipitation (0.91 in the regression model and 0.43 in the neural network), it can be concluded that the precipitation products which extracted in this study, show a good correlation between observed and retrieved precipitation.