Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3839-2024
https://doi.org/10.5194/gmd-17-3839-2024
Model description paper
 | 
14 May 2024
Model description paper |  | 14 May 2024

DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties

Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen

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Cited articles

Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., and Nahavandi, S.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges, Inform. Fusion, 76, 243–297, https://doi.org/10.1016/j.inffus.2021.05.008, 2021. a
Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine Learning for Precipitation Nowcasting from Radar Images, arXiv [preprint], https://doi.org/10.48550/arXiv.1912.12132, 2019. a
Alexander, C., Dowell, D. C., Hu, M., Olson, J., Smirnova, T., Ladwig, T., Weygandt, S., Kenyon, J. S., James, E., Lin, H., Grell, G., Ge, G., Alcott, T., Benjamin, S., Brown, J. M., Toy, M. D., Ahmadov, R., Back, A., Duda, J. D., Smith, M. B., Hamilton, J. A., Jamison, B. D., Jankov, I., and Turner, D. D.: Rapid Refresh (RAP) and High Resolution Rapid Refresh (HRRR) Model Development, 100th Annual AMS Meeting, Boston Convention and Exhibition Center 415 Summer St. Boston, MA, https://rapidrefresh.noaa.gov/pdf/Alexander_AMS_NWP_2020.pdf (last access: 2 May 2024), 2020. a
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. a, b, c
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a, b
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Short summary
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.