Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6759-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6759-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
06 Sep 2022
Methods for assessment of models |
| 06 Sep 2022
| 06 Sep 2022
Intercomparison of four algorithms for detecting tropical cyclones using ERA5
Stella Bourdin
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ-Université Paris-Saclay, Gif-sur-Yvette, France
Sébastien Fromang
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ-Université Paris-Saclay, Gif-sur-Yvette, France
William Dulac
Centre National de Recherches Météorologiques, Université de Toulouse, Météo France, CNRS, Toulouse, France
Julien Cattiaux
Centre National de Recherches Météorologiques, Université de Toulouse, Météo France, CNRS, Toulouse, France
Fabrice Chauvin
Centre National de Recherches Météorologiques, Université de Toulouse, Météo France, CNRS, Toulouse, France
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- Fidelity of global tropical cyclone activity in a new reanalysis dataset (CRA40) J. Li et al. 10.1002/met.70009
- CyTRACK: An open-source and user-friendly python toolbox for detecting and tracking cyclones A. Pérez-Alarcón et al. 10.1016/j.envsoft.2024.106027
- Diagnosing Hurricane Barry Track Errors and Evaluating Physics Scalability in the UFS Short-Range Weather Application N. Lybarger et al. 10.3390/atmos14091457
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- An Approach to Link Climate Model Tropical Cyclogenesis Bias to Large‐Scale Wind Circulation Modes X. Feng et al. 10.1029/2023GL103838
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- How well do high-resolution Global Climate Models (GCMs) simulate tropical cyclones in the Bay of Bengal? S. Akhter et al. 10.1007/s00382-023-06745-3
- An Adaptive Learning Approach for Tropical Cyclone Intensity Correction R. Chen et al. 10.3390/rs15225341
- Tropical Cyclone Genesis Potential Indices in a New High‐Resolution Climate Models Ensemble: Limitations and Way Forward L. Cavicchia et al. 10.1029/2023GL103001
- Tropical cyclones in global high-resolution simulations using the IPSL model S. Bourdin et al. 10.1007/s00382-024-07138-w
- An Ensemble Machine Learning Approach for Tropical Cyclone Localization and Tracking From ERA5 Reanalysis Data G. Accarino et al. 10.1029/2023EA003106
- Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1 A. Mahesh et al. 10.5194/gmd-17-3533-2024
- Comparable impacts of IPO and AMO on the decadal change of northern hemisphere tropical cyclone frequency J. Cao et al. 10.1007/s00382-024-07523-5
- Evaluation of WRF model configurations for dynamic downscaling of tropical cyclones activity over the North Atlantic basin for Lagrangian moisture tracking analysis in future climate A. Pérez-Alarcón et al. 10.1016/j.atmosres.2024.107498
- Assessing South Indian Ocean tropical cyclone characteristics in HighResMIP simulations P. Pall et al. 10.1002/joc.8609
- Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset S. Gardoll & O. Boucher 10.5194/gmd-15-7051-2022
- Increased amplitude of atmospheric rivers and associated extreme precipitation in ultra-high-resolution greenhouse warming simulations A. Nellikkattil et al. 10.1038/s43247-023-00963-7
- Recent advances in seasonal and multi-annual tropical cyclone forecasting Y. Takaya et al. 10.1016/j.tcrr.2023.09.003
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- Can low-resolution CMIP6 ScenarioMIP models provide insight into future European post-tropical-cyclone risk? E. Sainsbury et al. 10.5194/wcd-3-1359-2022
- Contrasting Responses of Atlantic and Pacific Tropical Cyclone Activity to Atlantic Multidecadal Variability H. Huang et al. 10.1029/2023GL102959
- Intercomparison of four algorithms for detecting tropical cyclones using ERA5 S. Bourdin et al. 10.5194/gmd-15-6759-2022
Latest update: 30 Jan 2025
Short summary
When studying tropical cyclones in a large dataset, one needs objective and automatic procedures to detect their specific pattern. Applying four different such algorithms to a reconstruction of the climate, we show that the choice of the algorithm is crucial to the climatology obtained. Mainly, the algorithms differ in their sensitivity to weak storms so that they provide different frequencies and durations. We review the different options to consider for the choice of the tracking methodology.
When studying tropical cyclones in a large dataset, one needs objective and automatic procedures...
