Local surface layer wind field fusion and nowcasting method
By combining the wind shear index of the meteorological tower with Barnes stepwise correction and a convolutional neural network model, the problem of integrating WRF-LES large eddy simulation with actual wind fields was solved, achieving efficient forecasting of local near-surface wind fields, which is applicable to wind field forecasting for wind power and low-altitude economics.
Patent Information
- Application Number
- CN202511573298.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-30
- Publication Date
- 2026-02-03
AI Technical Summary
In existing technologies, there are few studies on the integration of WRF-LES large eddy simulation with actual wind fields, resulting in insufficient local near-surface wind field forecasts. In particular, the utilization rate of wind field data in fields such as wind power and low-altitude flight is not high, and the application of convolutional neural networks in wind field and large eddy forecasting is limited.
By using the wind shear index calculated by the wind measurement tower to fill the gaps in the wind profile above the automatic weather station, and combining the WRF-LES large eddy simulation and automatic weather station data to perform Barnes stepwise correction wind field fusion, a convolutional neural network model is established to perform wind field data fusion and nowcasting.
It achieves efficient fusion and nowcasting of local near-surface wind fields, improving the accuracy of wind field forecasts in fields such as wind power and low-altitude economy, and uses model parameters in WRF-LES real-time forecasts for correction.