Integrated satellite precipitation: combining products, infrared brightness temperature and electrical activity by convolutional neural networks

Abstract

Precipitation monitoring is extremely crucial for agricultural activities, since it is a fundamental component of the hydrological balance that has a great impact on yields. In-situ observations through rain gauges are scarce, so it is complemented with precipitation estimates from remote sensors (i.e. satellites and meteorological radars) that increase the spatial and temporal coverage. In this work we propose to use a convolutional neural network model with a UNet type architecture, based on data provided by the GOES-16 satellite. In particular, the combined use of infrared brightness temperature (which provides information on cloud top temperature) and electrical activity (which provides information on convection intensity) will be evaluated. Model training is performed using precipitation data estimated by the GPM satellite-borne weather radar.