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

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

doi:https://doi.org/10.5194/gmd-18-1851-2025

Articles | Volume 18, issue 5

https://doi.org/10.5194/gmd-18-1851-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-1851-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 5

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

Supplement

https://doi.org/10.5194/gmd-18-1851-2025-supplement

Data sets

Data for the manuscript the manuscript "Cell tracking-based frame-work for assessing nowcasting model skill in reproducing growth and decay of convective rainfall" by Ritvanen et al. Jenna Ritvanen et al. https://doi.org/10.57707/fmi-b2share.627e6133c2594dc3945d14fe0ef9c922

Results for the manuscript "Cell tracking-based framework for assessing now-casting model skill in reproducing growth and decay of convective rainfall" by Ritvanen et al. Jenna Ritvanen et al. https://doi.org/10.57707/fmi-b2share.e1897cfb9a9d4466bb9d7235882bc511

Model code and software

Cell tracking -based verification framework for nowcasts Jenna Ritvanen https://doi.org/10.5281/zenodo.14227567

pysteps: T-DaTing algorithm with splits & merges Daniele Nerini et al. https://doi.org/10.5281/zenodo.11242613

fmidev/lagrangian-convolutional-neural-network: L-CNN model with Swiss data Jenna Ritvanen https://doi.org/10.5281/zenodo.11242483

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.