Modelling convective cell occurrence in proximity to cold fronts using extreme gradient boosting
Abstract
Machine learning is emerging as a valuable tool for convection-related applications such as post-processing numerical weather prediction output, improving understanding of convective storm climatology and potentially improving existing convective parameterization schemes. In a rapidly developing field, it is vital to assess the strengths and limitations of machine learning approaches across different applications. Here, a probabilistic model is developed using a convective cell dataset as ground truth and predictors primarily from ERA5. The model's ability to reproduce the convective cell climatology at different regions relative to cold fronts (i.e. post-frontal and pre-frontal) is assessed during the warm-season in Germany. The optimal number of features (predictors) is selected using a feature elimination strategy. Overall, the optimised model exhibits high skill in reproducing the spatial and temporal cell frequency at different regions relative to the front. While the highest cell frequency is correctly identified near the surface front, the model underestimates the actual cell count in this region. Feature importance analysis shows that the model depends most heavily on CAPE to make its predictions. Additionally, the time of day predictor is key for accurately capturing the diurnal cycle of convective cells on both sides of the cold front. The study highlights both the advantages and the limitations of data-driven models, offering valuable insights for future data-driven climate and weather prediction models.
Versions
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BibTeX
@misc{pacey2026modelling,
title = {{Modelling convective cell occurrence in proximity to cold fronts using extreme gradient boosting}},
author = {George Pacey and Stephan Pfahl and Lisa Schielicke},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2606.26699},
eprint = {2606.26699},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.26699},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Modelling convective cell occurrence in proximity to cold fronts using extreme gradient boosting
AU - George Pacey
AU - Stephan Pfahl
AU - Lisa Schielicke
PY - 2026
DA - 2026/06/25
PB - arXiv
DO - 10.48550/arxiv.2606.26699
UR - https://arxiv.org/abs/2606.26699
AB - Machine learning is emerging as a valuable tool for convection-related applications such as post-processing numerical weather prediction output, improving understanding of convective storm climatology and potentially improving existing convective parameterization schemes. In a rapidly developing field, it is vital to assess the strengths and limitations of machine learning approaches across different applications. Here, a probabilistic model is developed using a convective cell dataset as ground truth and predictors primarily from ERA5. The model's ability to reproduce the convective cell climatology at different regions relative to cold fronts (i.e. post-frontal and pre-frontal) is assessed during the warm-season in Germany. The optimal number of features (predictors) is selected using a feature elimination strategy. Overall, the optimised model exhibits high skill in reproducing the spatial and temporal cell frequency at different regions relative to the front. While the highest cell frequency is correctly identified near the surface front, the model underestimates the actual cell count in this region. Feature importance analysis shows that the model depends most heavily on CAPE to make its predictions. Additionally, the time of day predictor is key for accurately capturing the diurnal cycle of convective cells on both sides of the cold front. The study highlights both the advantages and the limitations of data-driven models, offering valuable insights for future data-driven climate and weather prediction models.
ER -