Reply on RC1

Abstract

Observations from geostationary satellites offer the unique ability to provide spatially continuous coverage at continental scales with high spatial and temporal resolution. Because of this, they are commonly used to complement ground-based measurements of precipitation, whose coverage is often more limited. We present a novel, neural-network-based, near real-time precipitation retrieval for Brazil based on visible and infrared (VIS/IR) observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite 16. The retrieval, which employs a convolutional neural network to perform Bayesian retrievals of precipitation, was developed with the aims of (1) leveraging the full potential of latest-generation geostationary observations and (2) providing probabilistic precipitation estimates with well-calibrated uncertainties. The retrieval is trained using co-locations with combined radar and radiometer retrievals from the Global Precipitation Measurement (GPM) Core Observatory. Its accuracy is assessed using one month of gauge measurements and compared to the precipitation retrieval that is currently in operational use at the Brazilian Institute for Space Research as well as two state-of-the-art global precipitation products: The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System and the Integrated Multi-Satellite Retrievals for GPM (IMERG). Even in its most basic configuration, the accuracy of the proposed retrieval is similar to that of IMERG, which merges retrievals from VIS/IR and microwave observations with gauge measurements. In its most advanced configuration, the retrieval reduces the mean absolute error for hourly accumulations by 22 % compared the currently operational retrieval, by 50 % for the MSE and increases the correlation by 400 %. Compared to IMERG, the improvements correspond to 15 %, 15 % and 39 %, respectively. Furthermore, we show that the probabilistic retrieval is well calibrated against gauge measurements when differences in a priori distributions are accounted for. In addition to potential improvements in near real time precipitation estimation over Brazil, our findings highlight the potential of specialized data driven retrievals that are made possible through advances in geostationary sensor technology, the availability of high-quality reference measurements from the GPM mission and modern machine learning techniques. Furthermore, our results show the potential of probabilistic precipitation retrievals to better characterize the observed precipitation and provide more trustworthy retrieval results.