Application of deep convolutional neural networks for precipitation estimation through both top-down and bottom-up approaches

Abstract

Reliable and accurate precipitation estimations are a crucial hydrological parameter for various applications, including managing water resources, drought monitoring and natural hazard prediction. The two main approaches for estimating precipitation from satellite data are the top-down and bottom-up. The top-down approach uses data from Geostationary and Low Earth Orbiting satellites to infer precipitation from atmosphere and cloud information, while the bottom-up approach estimates precipitation using soil moisture observations, e.g.  the SM2RAIN algorithm. The main difference between these approaches is that the top-down approach is a more direct method of measuring precipitation that estimates it instantaneously, which may lead to underestimation, while the bottom-up approach measures accumulated rainfall with more reliable precipitation estimation between two consecutive SM measurements. In this study, we develop the deep convolutional neural networks (CNN) algorithm to combine the top-down and bottom-up approaches for estimating precipitation using the satellite level 1 products including the satellite backscatter information from the Advanced SCATterometer (ASCAT), infrared (IR) and water vapor (WV) channels from geostationary satellites. This algorithm is assessed at 0.1° spatial and daily temporal resolution over Italy for the period of 2019-2021. The results show that the developed model improves the accuracy of precipitation estimation. Additionally, it indicates that there is a significant potential for global precipitation estimation using this model.