3D Wind Downscaling With Terrain Drag and Roughness Correction
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Solution Overview
Problem
Existing weather prediction models, such as the LDAPS model, struggle to generate high-resolution three-dimensional wind numerical data accurately, particularly in complex topographic areas, leading to excessive simulation of wind speed in mountainous regions.
Innovation Solution
A downscaling process is applied to wind numerical data using detailed topographic information, involving coordinate system conversion, roughness length adjustment, height correction, and subgrid-scale terrain drag effect correction to generate high-resolution wind data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wind numerical data is generated using operational LDAPS model, then prediction coverage and computational efficiency are improved, but resolution accuracy and topographic representation deteriorate
Solution Approach 1:
The patent divides the wind field calculation into two segments: coarse-grid operational LDAPS model for overall prediction coverage, and fine-grid post-processing for local accuracy enhancement. This segmentation allows the system to maintain both broad coverage and localized precision by applying different resolution levels to different spatial scales.
Solution Approach 2:
The patent transitions from two-dimensional horizontal resolution to three-dimensional vertical resolution by introducing multiple height levels (surface, mid-level, upper-level) in the post-processed data. This dimensional enhancement allows the model to capture vertical wind structure variations that were not resolved in the original operational data, thereby improving resolution accuracy without sacrificing coverage.
2Productivity
If wind numerical data is generated using operational LDAPS model, then computational efficiency is improved, but accuracy in complex topographic areas deteriorates
Solution Approach 1:
The patent applies local quality enhancement by selectively improving only the wind data in complex topographic areas through post-processing, while maintaining the original operational data in flat or simple terrain regions. The post-processing algorithm identifies and treats only those regions requiring enhancement, thereby maintaining computational efficiency while improving reliability where needed.
Solution Approach 2:
The patent introduces detailed topographic information as an intermediary element that mediates between the operational LDAPS model output and the final high-resolution wind data. This intermediary topographic data enables the post-processing to adjust wind fields to account for terrain effects, thereby improving accuracy in complex topographic areas without requiring complete redesign of the operational model.
3Area of stationary object
If surface wind speed is simulated in mountainous regions, then spatial coverage is improved, but simulation accuracy deteriorates due to excessive wind speed
Solution Approach 1:
The patent changes key parameters in the wind field calculation by introducing height-based correction factors and roughness length adjustments specific to mountainous regions. These parameter modifications allow the model to account for terrain-induced wind acceleration and deceleration effects, thereby improving simulation accuracy while maintaining broad spatial coverage across diverse terrains.
Data Source
AI summary
Provided is a method of calculating high-resolution three-dimensional wind numerical information considering detailed topographic information, the method performing, when the wind numerical data of the operational LDAPS model is calculated (S100), a downscaling process based on the detailed topographic information using the wind numerical data as an input data, wherein the downscaling process performs a preprocessing process of converting a coordinate system and interpolating horizontal and vertical numerical data (S200), performs a process of adjusting roughness lengths (S300), correcting heights (S400), and correcting a subgrid-scale terrain drag effect (S500) on the basis of the detailed topographic information, and finally generating data that has gone through each of the processes as high-resolution wind numerical data (S600).