Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1851-2025
https://doi.org/10.5194/gmd-18-1851-2025
Methods for assessment of models
 | 
18 Mar 2025
Methods for assessment of models |  | 18 Mar 2025

Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall

Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini

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Cited articles

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
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Beucher, S. and Lantuejoul, C.: Use of Watersheds in Contour Detection, in: International Workshop on Image Processing: Real-time Edge and Motion Detection/Estimation, Rennes, France, https://people.cmm.minesparis.psl.eu/users/beucher/publi/watershed.pdf (last access: 12 March 2025), 1979. a
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Bouguet, J.-Y.: Pyramidal Implementation of the Affine Lucas Kanade Feature Tracker Description of the Algorithm, Intel corporation, https://robots.stanford.edu/cs223b04/algo_affine_tracking.pdf (last access: 12 March 2025), 2001. a, b
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Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
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