Articles | Volume 15, issue 23
Research article
01 Dec 2022
Research article |  | 01 Dec 2022

An improved near-real-time precipitation retrieval for Brazil

Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, and Alan J. P. Calheiros

Related authors

GPROF V7 and beyond: Assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean
Simon Pfreundschuh, Clément Guilloteau, Paula J. Brown, Christian D. Kummerow, and Patrick Eriksson
EGUsphere,,, 2023
Short summary
Ice water path retrievals from Meteosat-9 using quantile regression neural networks
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717,,, 2022
Short summary
GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm
Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad​​​​​​​
Atmos. Meas. Tech., 15, 5033–5060,,, 2022
Short summary
Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems
Simon Pfreundschuh, Stuart Fox, Patrick Eriksson, David Duncan, Stefan A. Buehler, Manfred Brath, Richard Cotton, and Florian Ewald
Atmos. Meas. Tech., 15, 677–699,,, 2022
Short summary
Can machine learning correct microwave humidity radiances for the influence of clouds?
Inderpreet Kaur, Patrick Eriksson, Simon Pfreundschuh, and David Ian Duncan
Atmos. Meas. Tech., 14, 2957–2979,,, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Broadband radiative quantities for the EarthCARE mission: the ACM-COM and ACM-RT products
Jason N. S. Cole, Howard W. Barker, Zhipeng Qu, Najda Villefranque, and Mark W. Shephard
Atmos. Meas. Tech., 16, 4271–4288,,, 2023
Short summary
Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)
Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079,,, 2023
Short summary
Retrieval of snow layer and melt pond properties on Arctic sea ice from airborne imaging spectrometer observations
Sophie Rosenburg, Charlotte Lange, Evelyn Jäkel, Michael Schäfer, André Ehrlich, and Manfred Wendisch
Atmos. Meas. Tech., 16, 3915–3930,,, 2023
Short summary
Using optimal estimation to retrieve winds from velocity-azimuth display (VAD) scans by a Doppler lidar
Sunil Baidar, Timothy J. Wagner, David D. Turner, and W. Alan Brewer
Atmos. Meas. Tech., 16, 3715–3726,,, 2023
Short summary
Angular sampling of a monochromatic, wide-field-of-view camera to augment next-generation Earth radiation budget satellite observations
Jake J. Gristey, K. Sebastian Schmidt, Hong Chen, Daniel R. Feldman, Bruce C. Kindel, Joshua Mauss, Mathew van den Heever, Maria Z. Hakuba, and Peter Pilewskie
Atmos. Meas. Tech., 16, 3609–3630,,, 2023
Short summary

Cited articles

Adler, R. F. and Negri, A. J.: A satellite infrared technique to estimate tropical convective and stratiform rainfall, J. Appl. Meteorol. Clim., 27, 30–51, 1988. a
Arkin, P. A. and Meisner, B. N.: The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982–84, Mon. Weather Rev., 115, 51–74, 1987. a
Chollet, F.: Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 1251–1258, Honolulu, HI, USA, 21–26 July 2017,, 2017. a
de Siqueira, R. A. and Vila, D.: Hybrid methodology for precipitation estimation using Hydro-Estimator over Brazil, Int. J. Remote Sens., 40, 4244–4263,, 2019. a, b, c, d, e
Fohla De S. Paulo​​​​​​​: Chuva causa morte, derruba casas e deixa famílias desalojadas em Duque de Caxias, no RJ, (last access: 31 January 2022), 2020. a, b
Short summary
We used methods from the field of artificial intelligence to train an algorithm to estimate rain from satellite observations. In contrast to other methods, our algorithm not only estimates rain, but also the uncertainty of the estimate. 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 measurements of rain more accurate and reliable.