AI 中文总结
本文提出DOP+机器学习方法，结合气象场条件约束改进直接观测预测，实现GOES-East全圆盘云演化的高精度10分钟分辨率临近预报，性能优于多种基准方法，为全球对流尺度云临近预报奠基。
AI 中文摘要
云对航空、太阳能、遥感和风暴预测均有影响，但仍是最难预报的大气特征之一，尤其是对流尺度的云。由于云由跨广泛时空尺度的过程塑造，数值天气预报、外推方法及现有机器学习（ML）方法均受限于精度、区域范围和时间分辨率的组合问题。本文提出DOP+——一种针对云的机器学习方法，通过在直接观测预测（DOP）的基础上加入气象场条件约束，来预报GOES-East全圆盘红外亮温。该方法覆盖热带、中纬度和海洋区域，范围约10^8平方公里，约占地球表面的五分之一。DOP+以10分钟分辨率预报云演化，在0-6小时的预见期内，其技巧评分（fraction skill score）和平均绝对误差技巧评分均优于持续性预报、天气尺度数值天气预报及纯DOP基线模型，且在2-3小时内仍能保留对流结构。DOP+因此实现了精度、时间分辨率和空间覆盖的最优组合，为对流时间尺度下的全球云临近预报奠定了基础。
英文摘要
Clouds affect aviation, solar energy, remote sensing, and storm prediction, yet they remain among the hardest atmospheric features to forecast, particularly at convective scales. Because clouds are shaped by processes spanning a wide range of space and time scales, numerical weather prediction, extrapolation methods, and existing machine learning (ML) approaches are each limited by some combination of accuracy, domain size, and temporal resolution. We present DOP+, an ML approach for clouds that forecasts GOES-East full-disk infrared brightness temperatures by extending direct observation prediction (DOP) with conditioning on meteorological fields. The domain covers tropical, midlatitude, and marine regimes across $\sim 10^8 ~ km^2$, roughly a fifth of Earth's surface. DOP+ forecasts cloud evolution at 10-minute resolution and outperforms persistence, synoptic-scale NWP, and a pure DOP baseline across 0-6 h lead times in fractions skill score and mean absolute error skill score. Convective structure is retained out to 2-3 h. DOP+ thus achieves a state-of-the-art combination of accuracy, temporal resolution, and spatial coverage. Our work lays the foundation for fully global cloud nowcasting at convective timescales.
