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

Data sets

Data for the manuscript "DEUCE v1.0: A neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties" by Harnist et al. (2023) Bent Harnist et al. https://doi.org/10.23728/fmi-b2share.3efcfc9080fe4871bd756c45373e7c11

Model code and software

fmidev/deuce-nowcasting: Initial release of the source code for the manuscript Bent Harnist https://doi.org/10.5281/zenodo.7961954

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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.