A novel approach for the estimation of precipitation from geosationary satellites.

Observations from weather radars are well established for the estimation of precipitation. However these data are unavailable over the ocean and only sparely available over remote regions. Furthermore, in mountains the retrieval of precipitation is severely compromised by the scan geometry. Finally, non-meteorological noise and the saturation of the radar signal by thunderstorm clouds  also pose seroius problems to accuracy. Satellite data can help to overcome these shortcomings. However, the infrared signal provides mainly information about the cloud top temperature and thus no information about rain droplets. However, during day there is a good correlation of the cloud optical thickness and effective radii as e.g. demonstrated in the PhD of Rob Roebeling. Nevertheless in any case the satellite based precipitation needs to be calibrated. Typically, satellite instruments measuring in the micro-wave are used for this purpose. However, in this study, we use ground based data (omrbometer) for the calibration of the satellite based precipitation rate.  We investigate various machine learning methods (e.g. SVM, transformer based GANs and transfer learning) to find the best way to perform recurrent, near-real time calibration updates based on pre-trained information.  In this way, the spatial information of the satellite precipitation can be optimally combined with the high accuracy of the ground based measurements. The presentation will provide an overview about the developed methods and the validation results and the link of the work to the WMO-AINNP project.  Further a outlook will be given about the nowcasting of satellite based precipitation and its integration into the concept of seamless prediction at DWD and the combination with radar nowcasting.