发表机构
Google Research; Google DeepMind; Google（谷歌研究院; 谷歌DeepMind; 谷歌公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
WeatherNext 3通过摄入低延迟地球静止卫星数据，实现每小时预报、与物理模型相当的分辨率及多源观测利用，解决了AI气象模型的分辨率与观测利用缺陷，提升了中期预报性能。
AI 中文摘要
最先进的AI气象模型已展现出出色的中期预报能力与计算效率，但存在两个关键缺陷：其预报的空间和时间分辨率低于最优的基于物理的模型，且仅以分析数据初始化并训练。因此，它们无法直接利用观测数据，且分析数据中的任何偏差都会被预报继承。WeatherNext 3解决了这些缺陷，并建立了概率中期预报能力的新状态。首先，WeatherNext 3通过摄入低延迟的地球静止卫星数据，每小时生成一次新的预报（而非传统全球模型的每6小时一次）。其次，WeatherNext 3的时间和空间分辨率与基于物理的全球模型相当，单水平变量（包括太阳辐射和云量）的时间步长为1小时，分辨率为0.1度。第三，WeatherNext 3超越了传统分析变量，学习预测卫星衍生的降水估计值，以及热带气旋和站点观测数据。对稀疏站点数据进行建模使WeatherNext 3能够根据当地地理特征，在任何位置和时间生成2米温度和露点预报，其误差显著低于同类全球模型，即使在对未见过的站点进行评估时也是如此。总体而言，WeatherNext 3的能力使基于AI的业务气象预报超越了对数据同化、预报和后处理这三个传统独立阶段的模拟，这有助于进一步推动全球气象预报的性能和粒度前沿。
英文摘要
State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spatial and temporal resolution than the best physics-based models and they are exclusively initialized with and trained on analysis data. As a result, they cannot directly make use of observations, and any biases in the analysis are inherited by the forecast. WeatherNext 3 addresses these shortcomings and establishes a new state-of-the-art for probabilistic medium-range forecasting skill. First, WeatherNext 3 generates new forecasts every hour (rather than every 6 hours like traditional global models) by ingesting low-latency geostationary satellite data. Second, WeatherNext 3's temporal and spatial resolution are on par with physics-based global models, with hourly time steps and 0.1 degree resolution for single-level variables, including solar radiation and cloud cover. Third, WeatherNext 3 moves beyond traditional analysis variables by learning to predict satellite-derived precipitation estimates, as well as tropical cyclone and station observations. Modelling sparse station data allows WeatherNext 3 to make 2m temperature and dewpoint predictions at any location and time, conditioned on local geographical features, with substantially lower error than competing global models, even when evaluated against unseen stations. Together, WeatherNext 3's capabilities move operational AI-based weather forecasting beyond emulating the traditionally distinct stages of data assimilation, forecasting and post-processing, which helps to further push the frontier of performance and granularity for global weather prediction.
