06 Apr 2022
06 Apr 2022

An improved near real-time precipitation retrieval for Brazil

Simon Pfreundschuh1, Ingrid Ingemarsson1, Patrick Eriksson1, Daniel Alejandro Vila2, and Alan James P. Calheiros3 Simon Pfreundschuh et al.
  • 1Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
  • 2Regional office for the Americas, World Meteorological Organization, Asunción, Paraguay
  • 3Coordination of Applied Research and Technological Development, National Institute for Space Research (INPE), São José dos Campos, Brazil

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.

Simon Pfreundschuh et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-78', Anonymous Referee #1, 07 May 2022
    • AC1: 'Reply on RC1', Simon Pfreundschuh, 19 Aug 2022
    • AC2: 'Reply on RC1', Simon Pfreundschuh, 19 Aug 2022
  • RC2: 'Comment on egusphere-2022-78', Anonymous Referee #2, 17 Jun 2022
    • AC3: 'Reply on RC2', Simon Pfreundschuh, 19 Aug 2022

Simon Pfreundschuh et al.

Simon Pfreundschuh et al.


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Short summary
We use methods from the field of artificial intelligence to train an algorithm to predict rain from satellite observations. In contrast to other methods, our algorithm not only predicts the rain but also the uncertainty of the prediction. Using independent measurements from rain gauges, we show that our method performs better than currently available methods and that the provided uncertainty estimates are reliable. Our method makes satellite-based estimates of rain more accurate and reliable.