基于多尺度背景场误差协方差的区域数值预报方法及系统
By constructing a multi-scale background field error covariance model, the limitations of the single-scale model in traditional methods are overcome, resulting in more accurate data assimilation and forecasting results, and improving the overall accuracy and adaptability of regional numerical forecasting.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 内蒙古自治区气象台(内蒙古自治区环境气象预报中心)
- Filing Date
- 2025-03-24
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional regional numerical weather prediction methods rely on a single-scale background field error covariance model, which is difficult to accurately describe the characteristics of multi-scale atmospheric motion, resulting in insufficient utilization of observational information and affecting the accuracy and reliability of forecasts.
A multi-scale background field error covariance model is constructed. By multi-scale decomposition and combination of error covariance matrices, the data assimilation process is optimized, observation information at different spatial scales is integrated, and the optimal initial field is generated for forecasting.
It improves the ability to simulate multi-scale atmospheric motions, enhances the accuracy and adaptability of forecast results, and provides richer forecast products, especially under complex terrain and variable weather conditions.
Smart Images
Figure CN120195773B_ABST