用地形跟随坐标提升AI天气模型的降水预报精度。
Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts
- 将地形跟随坐标引入AI天气模型，改善降水空间分布。
- 极端降水事件和强度谱的预测准确率显著提高。
- 适合关注高精度降水模拟的研究者与气象业务人员。
人工智能天气预测（AIWP）模型常产生模糊的降水预报。本研究提出将地形跟随坐标整合进AIWP模型以解决此问题。通过将示例模型FuXi适配至1.0度网格间距数据，开展预报实验。验证结果表明，地形跟随坐标显著提升了极端事件和降水强度谱的估计效果。同时，该坐标系与全球质量与能量守恒约束具有良好协同性，明显降低了轻雨偏差。案例分析显示，地形跟随坐标能更准确地表示近地面风场，有助于AIWP模型学习降水与其他预报变量之间的关系。研究结果表明，地形跟随坐标值得在AIWP模型中考虑，以提升降水预报准确性。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) weather prediction (AIWP) models often produce ``blurry'' precipitation forecasts. This study presents a novel solution to tackle this problem -- integrating terrain-following coordinates into AIWP models. Forecast experiments are conducted to evaluate the effectiveness of terrain-following coordinates using FuXi, an example AIWP model, adapted to 1.0 degree grid spacing data. Verification results show a largely improved estimation of extreme events and precipitation intensity spectra. Terrain-following coordinates are also found to collaborate well with global mass and energy conservation constraints, with a clear reduction of drizzle bias. Case studies reveal that terrain-following coordinates can represent near-surface winds better, which helps AIWP models in learning the relationships between precipitation and other prognostic variables. The result of this study suggests that terrain-following coordinates are worth considering for AIWP models in producing more accurate precipitation forecasts.
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