Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-6035-2022
https://doi.org/10.5194/amt-15-6035-2022
Research article
 | 
21 Oct 2022
Research article |  | 21 Oct 2022

DeepPrecip: a deep neural network for precipitation retrievals

Fraser King, George Duffy, Lisa Milani, Christopher G. Fletcher, Claire Pettersen, and Kerstin Ebell

Related authors

Marine Carbohydrates and Other Sea Spray Aerosol Constituents Across Altitudes in the Lower Troposphere of Ny-Ålesund, Svalbard
Sebastian Zeppenfeld, Jonas Schaefer, Christian Pilz, Kerstin Ebell, Moritz Zeising, Frank Stratmann, Holger Siebert, Birgit Wehner, Matthias Wietz, Astrid Bracher, and Manuela van Pinxteren
EGUsphere, https://doi.org/10.5194/egusphere-2025-4336,https://doi.org/10.5194/egusphere-2025-4336, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Impact of weather systems on observed precipitation at Ny-Ålesund (Svalbard)
Kerstin Ebell, Christian Buhren, Rosa Gierens, Giovanni Chellini, Melanie Lauer, Andreas Walbröl, Sandro Dahlke, Pavel Krobot, and Mario Mech
Atmos. Chem. Phys., 25, 7315–7342, https://doi.org/10.5194/acp-25-7315-2025,https://doi.org/10.5194/acp-25-7315-2025, 2025
Short summary
Hygroscopic aerosols amplify longwave downward radiation in the Arctic
Denghui Ji, Mathias Palm, Matthias Buschmann, Kerstin Ebell, Marion Maturilli, Xiaoyu Sun, and Justus Notholt
Atmos. Chem. Phys., 25, 3889–3904, https://doi.org/10.5194/acp-25-3889-2025,https://doi.org/10.5194/acp-25-3889-2025, 2025
Short summary
Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024,https://doi.org/10.5194/amt-17-6223-2024, 2024
Short summary
Contrasting extremely warm and long-lasting cold air anomalies in the North Atlantic sector of the Arctic during the HALO-(𝒜 𝒞)3 campaign
Andreas Walbröl, Janosch Michaelis, Sebastian Becker, Henning Dorff, Kerstin Ebell, Irina Gorodetskaya, Bernd Heinold, Benjamin Kirbus, Melanie Lauer, Nina Maherndl, Marion Maturilli, Johanna Mayer, Hanno Müller, Roel A. J. Neggers, Fiona M. Paulus, Johannes Röttenbacher, Janna E. Rückert, Imke Schirmacher, Nils Slättberg, André Ehrlich, Manfred Wendisch, and Susanne Crewell
Atmos. Chem. Phys., 24, 8007–8029, https://doi.org/10.5194/acp-24-8007-2024,https://doi.org/10.5194/acp-24-8007-2024, 2024
Short summary

Cited articles

Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., and Inman, D. J.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks, J. Sound Vib., 388, 154–170, https://doi.org/10.1016/j.jsv.2016.10.043, 2017. a
Adhikari, A., Ehsani, M. R., Song, Y., and Behrangi, A.: Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods, Earth and Space Science, 7, e2020EA001357, https://doi.org/10.1029/2020EA001357, 2020. a
Bennartz, R., Fell, F., Pettersen, C., Shupe, M. D., and Schuettemeyer, D.: Spatial and temporal variability of snowfall over Greenland from CloudSat observations, Atmos. Chem. Phys., 19, 8101–8121, https://doi.org/10.5194/acp-19-8101-2019, 2019. a
Boudala, F. S., Gultepe, I., and Milbrandt, J. A.: The Performance of Commonly Used Surface-Based Instruments for Measuring Visibility, Cloud Ceiling, and Humidity at Cold Lake, Alberta, Remote Sens., 13, 5058, https://doi.org/10.3390/rs13245058, 2021. a
Buttle, J. M., Allen, D. M., Caissie, D., Davison, B., Hayashi, M., Peters, D. L., Pomeroy, J. W., Simonovic, S., St-Hilaire, A., and Whitfield, P. H.: Flood processes in Canada: Regional and special aspects, Can. Water Resour. J., 41, 7–30, https://doi.org/10.1080/07011784.2015.1131629, 2016. a
Download
Short summary
Under warmer global temperatures, precipitation patterns are expected to shift substantially, with critical impact on the global water-energy budget. In this work, we develop a deep learning model for predicting snow and rain accumulation based on surface radar observations of the lower atmosphere. Our model demonstrates improved skill over traditional methods and provides new insights into the regions of the atmosphere that provide the most significant contributions to high model accuracy.
Share