Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 21
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.
https://doi.org/10.5194/gmd-16-6247-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 21
Methods for assessment of models
 | 
02 Nov 2023
Methods for assessment of models |  | 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, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li

Supplement

https://doi.org/10.5194/gmd-16-6247-2023-supplement

Data sets

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): Second release of my code Shaohui Zhou https://doi.org/10.5281/zenodo.8108889

Model code and software

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): Second release of my code Shaohui Zhou https://doi.org/10.5281/zenodo.8108889

Download
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.
Read more