Articles | Volume 16, issue 21
https://doi.org/10.5194/gmd-16-6247-2023
© 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-16-6247-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
02 Nov 2023
Methods for assessment of models |
| 02 Nov 2023
| 02 Nov 2023
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)
Shaohui Zhou
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China
Shanghai Key Laboratory of Ocean-Land-Atmosphere Boundary Dynamics and Climate Change, Fudan University, Shanghai, 200438, China
Zexia Duan
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Xingya Xi
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Yubin Li
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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Cited
15 citations as recorded by crossref.
- Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications A. Durap https://doi.org/10.31127/tuje.1624354
- A double-stage method for improving long-term numerical wind speed prediction via chaos-noise decoupling Z. Duan et al. https://doi.org/10.1080/15435075.2026.2698762
- Improving multi-modal wind speed prediction of short and medium term with a bi-clustered machine learning method Y. Zhang et al. https://doi.org/10.5194/amt-19-5071-2026
- Mum Çubuğu Grafik Gösterimi, Minimum Artıklık Maksimum İlgililik Algoritması ve XGBoost Modeline Dayalı Rüzgâr Hızı Tahmini S. Karasu https://doi.org/10.24012/dumf.1496080
- 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
- A Hybrid Wind Speed Forecasting Framework Based on Downscaled Multi-Model Forecasts and Machine Learning for Day-Ahead Wind Power Applications D. Oh et al. https://doi.org/10.3390/en19122928
- Improving the forecast of wind speed and significant wave height using neural networks and gradient boosting trees M. Ré Henriques et al. https://doi.org/10.1016/j.oceaneng.2025.120925
- An integrated wind speed prediction and model selection system for multi-scenarios incorporating improved fuzzy comprehensive evaluation W. Zhang et al. https://doi.org/10.1007/s00477-026-03273-4
- Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data S. Zhou et al. https://doi.org/10.3390/rs17132155
- Comparative evaluation of ECMWF and GFS for operational day-ahead wind speed forecasting X. Xia et al. https://doi.org/10.1016/j.renene.2026.125263
- Systematic evaluation of transformer-based time series forecasting models for post-processing WRF-simulated wind speed and predicting short-term power output X. Xia et al. https://doi.org/10.1016/j.apenergy.2025.127070
- A forecasting method for corrected numerical weather prediction precipitation based on modal decomposition and coupling of multiple intelligent algorithms C. Meng et al. https://doi.org/10.1007/s00703-024-01030-2
- Assessment of the ZJWARMS Forecast Model’s Adaptability and AI-Based Bias Correction over Complex Terrain Q. Zhang et al. https://doi.org/10.3390/atmos16101151
- MTRCL: A spatio-temporal multi-source data fusion method for enhancing short-term wind speed forecast capability of numerical weather prediction X. Ju et al. https://doi.org/10.1016/j.energy.2025.139728
- Short-Term Wind-Forecast Calibration for Energy Management Using Numerical Modeling and In Situ Measurements A. Pérez et al. https://doi.org/10.3390/en18236342
15 citations as recorded by crossref.
- Machine learning-based wind speed prediction using random forest: a cross-validated analysis for renewable energy applications A. Durap https://doi.org/10.31127/tuje.1624354
- A double-stage method for improving long-term numerical wind speed prediction via chaos-noise decoupling Z. Duan et al. https://doi.org/10.1080/15435075.2026.2698762
- Improving multi-modal wind speed prediction of short and medium term with a bi-clustered machine learning method Y. Zhang et al. https://doi.org/10.5194/amt-19-5071-2026
- Mum Çubuğu Grafik Gösterimi, Minimum Artıklık Maksimum İlgililik Algoritması ve XGBoost Modeline Dayalı Rüzgâr Hızı Tahmini S. Karasu https://doi.org/10.24012/dumf.1496080
- 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
- A Hybrid Wind Speed Forecasting Framework Based on Downscaled Multi-Model Forecasts and Machine Learning for Day-Ahead Wind Power Applications D. Oh et al. https://doi.org/10.3390/en19122928
- Improving the forecast of wind speed and significant wave height using neural networks and gradient boosting trees M. Ré Henriques et al. https://doi.org/10.1016/j.oceaneng.2025.120925
- An integrated wind speed prediction and model selection system for multi-scenarios incorporating improved fuzzy comprehensive evaluation W. Zhang et al. https://doi.org/10.1007/s00477-026-03273-4
- Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data S. Zhou et al. https://doi.org/10.3390/rs17132155
- Comparative evaluation of ECMWF and GFS for operational day-ahead wind speed forecasting X. Xia et al. https://doi.org/10.1016/j.renene.2026.125263
- Systematic evaluation of transformer-based time series forecasting models for post-processing WRF-simulated wind speed and predicting short-term power output X. Xia et al. https://doi.org/10.1016/j.apenergy.2025.127070
- A forecasting method for corrected numerical weather prediction precipitation based on modal decomposition and coupling of multiple intelligent algorithms C. Meng et al. https://doi.org/10.1007/s00703-024-01030-2
- Assessment of the ZJWARMS Forecast Model’s Adaptability and AI-Based Bias Correction over Complex Terrain Q. Zhang et al. https://doi.org/10.3390/atmos16101151
- MTRCL: A spatio-temporal multi-source data fusion method for enhancing short-term wind speed forecast capability of numerical weather prediction X. Ju et al. https://doi.org/10.1016/j.energy.2025.139728
- Short-Term Wind-Forecast Calibration for Energy Management Using Numerical Modeling and In Situ Measurements A. Pérez et al. https://doi.org/10.3390/en18236342
Saved (final revised paper)
Latest update: 09 Sep 2026
Short summary
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indices for 13 months remain relatively stable: the forecasting accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction...
