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
Viewed
Total article views: 3,223 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Apr 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,430 | 736 | 57 | 3,223 | 50 | 53 |
- HTML: 2,430
- PDF: 736
- XML: 57
- Total: 3,223
- BibTeX: 50
- EndNote: 53
Total article views: 2,450 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Sep 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,889 | 524 | 37 | 2,450 | 41 | 46 |
- HTML: 1,889
- PDF: 524
- XML: 37
- Total: 2,450
- BibTeX: 41
- EndNote: 46
Total article views: 773 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Apr 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 541 | 212 | 20 | 773 | 9 | 7 |
- HTML: 541
- PDF: 212
- XML: 20
- Total: 773
- BibTeX: 9
- EndNote: 7
Viewed (geographical distribution)
Total article views: 3,223 (including HTML, PDF, and XML)
Thereof 2,996 with geography defined
and 227 with unknown origin.
Total article views: 2,450 (including HTML, PDF, and XML)
Thereof 2,292 with geography defined
and 158 with unknown origin.
Total article views: 773 (including HTML, PDF, and XML)
Thereof 704 with geography defined
and 69 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
21 citations as recorded by crossref.
- Use of threshold parameter variation for tropical cyclone tracking B. Enz et al. 10.5194/gmd-16-5093-2023
- The Relationship Between African Easterly Waves and Tropical Cyclones in Historical and Future Climates in the HighResMIP‐PRIMAVERA Simulations E. Bercos‐Hickey et al. 10.1029/2022JD037471
- 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
- Tropical Cyclone Detection from the Thermal Infrared Sensor IASI Data Using the Deep Learning Model YOLOv3 L. Lam et al. 10.3390/atmos14020215
- An Approach to Link Climate Model Tropical Cyclogenesis Bias to Large‐Scale Wind Circulation Modes X. Feng et al. 10.1029/2023GL103838
- Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker W. Dulac et al. 10.1007/s00382-023-06902-8
- 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
- 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
- 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
- A composite approach to produce reference datasets for extratropical cyclone tracks: application to Mediterranean cyclones E. Flaounas et al. 10.5194/wcd-4-639-2023
- 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
17 citations as recorded by crossref.
- Use of threshold parameter variation for tropical cyclone tracking B. Enz et al. 10.5194/gmd-16-5093-2023
- The Relationship Between African Easterly Waves and Tropical Cyclones in Historical and Future Climates in the HighResMIP‐PRIMAVERA Simulations E. Bercos‐Hickey et al. 10.1029/2022JD037471
- 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
- Tropical Cyclone Detection from the Thermal Infrared Sensor IASI Data Using the Deep Learning Model YOLOv3 L. Lam et al. 10.3390/atmos14020215
- An Approach to Link Climate Model Tropical Cyclogenesis Bias to Large‐Scale Wind Circulation Modes X. Feng et al. 10.1029/2023GL103838
- Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker W. Dulac et al. 10.1007/s00382-023-06902-8
- 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
- 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
- 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
4 citations as recorded by crossref.
- A composite approach to produce reference datasets for extratropical cyclone tracks: application to Mediterranean cyclones E. Flaounas et al. 10.5194/wcd-4-639-2023
- 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: 17 Jul 2024
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...
