Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-251-2023
© Author(s) 2023. 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-16-251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Jan 2023
Development and technical paper |
| 10 Jan 2023
| 10 Jan 2023
Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range
Daan R. Scheepens
CORRESPONDING AUTHOR
Research Group Data Mining and Machine Learning, Faculty of Computer Science, University of Vienna, Währingerstrasse 29, 1090 Vienna, Austria
Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Hohe Warte 38, 1190 Vienna, Austria
Kateřina Hlaváčková-Schindler
CORRESPONDING AUTHOR
Research Group Data Mining and Machine Learning, Faculty of Computer Science, University of Vienna, Währingerstrasse 29, 1090 Vienna, Austria
Claudia Plant
Research Group Data Mining and Machine Learning, Faculty of Computer Science, University of Vienna, Währingerstrasse 29, 1090 Vienna, Austria
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- Extreme-oriented loss: Powering a novel framework for improving spatiotemporal met-ocean forecasting Y. Han et al. https://doi.org/10.1016/j.eswa.2026.131115
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- Large model-driven physical neural network architecture: Coupled multi-environmental factors for vessel drift trajectory prediction F. Li et al. https://doi.org/10.1016/j.oceaneng.2025.123560
- A novel data augmentation method for fault diagnosis in spindle assembly based on key-variable selection with AdaBoost-Kriging model S. Ding et al. https://doi.org/10.1080/0951192X.2025.2563263
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- A two stage feature extraction and synchronized feature–parameter learning framework for reliable multistep wind speed forecasting Z. Yang & J. Che https://doi.org/10.1016/j.energy.2025.139349
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- Graph neural networks for hourly precipitation projections at the convection permitting scale with a novel hybrid imperfect framework V. Blasone et al. https://doi.org/10.1017/eds.2025.10022
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- Deep learning-based downscaling of ocean surface vector wind over the Taiwan Strait and its adjacent seas with Pangu-weather J. Yi et al. https://doi.org/10.1016/j.jmarsys.2026.104224
24 citations as recorded by crossref.
- Unstructured mesh-based graph neural networks for estimating the spatiotemporal distribution of a human-induced chemical in freshwater S. Kim et al. https://doi.org/10.1016/j.wroa.2025.100367
- Forecasting of Sea-Surface Wind Speed Using Deep-Learning Method Based on Multidimensional Frequency-Domain Feature Fusion J. He & Z. Deng https://doi.org/10.1007/s11802-025-5987-8
- A study on TFT-LCD manufacturing quality prediction method using SHAP feature selection and ensemble learning H. Zheng et al. https://doi.org/10.1080/0951192X.2025.2498083
- Class-aware contrastive optimization for imbalanced text classification G. Khvatskii et al. https://doi.org/10.1007/s44248-025-00064-0
- Extreme-oriented loss: Powering a novel framework for improving spatiotemporal met-ocean forecasting Y. Han et al. https://doi.org/10.1016/j.eswa.2026.131115
- Local and Long-range Convolutional LSTM Network: A novel multi-step wind speed prediction approach for modeling local and long-range spatial correlations based on ConvLSTM M. Yu et al. https://doi.org/10.1016/j.engappai.2023.107613
- Regression augmentation with data-driven segmentation S. Alahyari et al. https://doi.org/10.1016/j.neunet.2026.108603
- Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. https://doi.org/10.5194/npg-31-535-2024
- Efficient Neural Modeling of Wind Power Density for National-Scale Energy Planning: Toward Sustainable AI Applications in Industry 5.0 M. Molina-Almaraz et al. https://doi.org/10.3390/app152413000
- Large model-driven physical neural network architecture: Coupled multi-environmental factors for vessel drift trajectory prediction F. Li et al. https://doi.org/10.1016/j.oceaneng.2025.123560
- A novel data augmentation method for fault diagnosis in spindle assembly based on key-variable selection with AdaBoost-Kriging model S. Ding et al. https://doi.org/10.1080/0951192X.2025.2563263
- ASER: Adapted squared error relevance for rare cases prediction in imbalanced regression Y. Kou & G. Fu https://doi.org/10.1002/cem.3515
- Energy conservation-based on-line tuning of an analytical model for accurate estimation of multi-joint stiffness with joint modular soft actuators F. Matsunaga et al. https://doi.org/10.1017/wtc.2025.10023
- A literature review based on density forecasting and uncertainty quantification of wind power generation D. Rathod & L. Gidwani https://doi.org/10.1016/j.rser.2025.116559
- Hybrid imbalanced regression through unified data-level and algorithm-level balancing S. Shahbazi et al. https://doi.org/10.1016/j.eswa.2026.131908
- Accelerating tropical cyclone wave height estimation via machine learning and deep latent surrogates T. Du et al. https://doi.org/10.1016/j.oceaneng.2026.124560
- Improving imbalanced industrial datasets to enhance the accuracy of mechanical property prediction and process optimization for strip steel F. Li et al. https://doi.org/10.1007/s10845-023-02275-1
- Deep learning-based prediction of wind-induced lateral displacement response of suspension bridge decks for structural health monitoring Z. Wang et al. https://doi.org/10.1016/j.jweia.2024.105679
- PP-Loss: An imbalanced regression loss based on plotting position for improved precipitation nowcasting L. Xu et al. https://doi.org/10.1007/s00704-024-04984-w
- A two stage feature extraction and synchronized feature–parameter learning framework for reliable multistep wind speed forecasting Z. Yang & J. Che https://doi.org/10.1016/j.energy.2025.139349
- Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey C. Yang et al. https://doi.org/10.3390/en16145562
- Graph neural networks for hourly precipitation projections at the convection permitting scale with a novel hybrid imperfect framework V. Blasone et al. https://doi.org/10.1017/eds.2025.10022
- A comprehensive framework for data challenges and intelligent resource optimization for offshore wind energy Y. Lyu et al. https://doi.org/10.1016/j.seta.2026.104888
- Deep learning-based downscaling of ocean surface vector wind over the Taiwan Strait and its adjacent seas with Pangu-weather J. Yi et al. https://doi.org/10.1016/j.jmarsys.2026.104224
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
The production of wind energy is increasing rapidly and relies heavily on atmospheric conditions. To ensure power grid stability, accurate predictions of wind speed are needed, especially in the short range and for extreme wind speed ranges. In this work, we demonstrate the forecasting skills of a data-driven deep learning model with model adaptations to suit higher wind speed ranges. The resulting model can be applied to other data and parameters, too, to improve nowcasting predictions.
The production of wind energy is increasing rapidly and relies heavily on atmospheric...
