量子位·2025-11-18 05:02Core Viewpoint - The article discusses the advancements in weather forecasting technology with the introduction of Google's DeepMind WeatherNext 2, which offers real-time, hour-level predictions and significantly improves the accuracy and speed of weather forecasts [2][7]. Group 1: Technological Advancements - WeatherNext 2 operates 8 times faster than its predecessor and provides hourly resolution forecasts, allowing for detailed predictions such as "light rain from 2-3 PM" [2]. - The system can generate dozens to hundreds of possible weather evolution scenarios from the same input [4]. - Traditional supercomputers take hours to perform similar tasks, while WeatherNext 2 can complete them in under a minute using a single TPU [6]. Group 2: Importance of Detailed Forecasting - Detailed weather predictions are crucial for various industries, including energy management, urban planning, agriculture, logistics, and aviation [9][10]. - The atmospheric system is complex and chaotic, where small disturbances can significantly impact weather patterns [10]. Group 3: Functional Generative Networks (FGN) - The key to WeatherNext 2's speed and accuracy is the introduction of Functional Generative Networks (FGN), which uses slight, globally consistent random perturbations to model weather [13][15]. - FGN allows the model to generate a complete future weather field from a 32-dimensional random vector, effectively creating multiple future scenarios [15][18]. - This method has resulted in a significant reduction in prediction errors and improved the model's ability to predict extreme weather events, such as typhoon paths, with a 24-hour advance in accuracy compared to previous models [19][21]. Group 4: Performance and Stability - FGN has shown to be stable, efficient, and practical, although it may occasionally produce minor artifacts in high-frequency variables [22][23].