A novel approach for the estimation of precipitation from geostationary satellites and radar.

Floods occur frequently around the world and belong to the most dangerous meteorological hazards, causing destruction of infrastructure and loss of human life. Therefore, the estimation and nearcast of precipitation is of uttermost importance to launch warnings early enough.

Although radars are a reliable source for precipitation estimation, their coverage is not sufficient for comprehensive monitoring and near real-time observation of precipitation in many regions, e.g. radar data are largely unavailable over the ocean, hence largely blind regarding thunderstorms coming from the sea.  Also over complex terrain and in remote areas radar information is only sparely available. Further, saturation effects hamper the accurate prediction of rain rates of large thunderstorms and finally rivers or catchment areas do not end close to national borders, but national radar networks do in contrast to satellite data.

Could satellite based precipitation data fill the gaps ? Up to now satellite based precipitation was not accurate enough to supplement radar data and to improve the spatial coverage and accuracy of precipitation rates around the world. Therefore, a novel method based on artificial intelligence has been developed to achieve an accuracy close to that of radar based rain rates.   

In this method a precipitation rate is derived every ten minutes from the satellite-based effective cloud albedo by regression with rain gauge data (ombrometer). This approach provides a first near real time Quantitative Precipitation Estimation (QPE) to calculate satellite-based precipitation rates with a large geographical coverage. However, this approach shows shortcomings in regional differentiation as the regression is applied over all existing cloud types.  In a second step, a method is therefore applied in order to learn from cloud structures (gradients, curvatures) and in this manner to take into account regional differences in the relationship between the retrieved cloud information and the precipitation rates. The method automatically transfers the learned relationship to regions without rain gauge data, a process that the authors refer to as spatial transfer learning. Thus, a meaningful QPE is also possible in areas without any rain gauge data (ombrometer), e.g. over the ocean and countries without radar data.  Of course the method can also be applied to radar data and could improve the QPE from radar as well. Hence, the method will be also applied to radar.

The presentation will provide an overview about the developed method and the validation results with a focus on satellite based rain rates. However, the application to radar will be briefly discussed as well. The presentation will close with a brief outlook.