▲ 作者:Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R. Andersson, Andrew El-Kadi, Dominic Masters, et al.
▲ 链接:
https://www.nature.com/articles/s41586-024-08252-9
▲ 摘要:
天气预报基本上是不确定的,因此预测可能出现的天气情况范围(从警告公众危险天气到规划可再生能源使用)对重要决策至关重要。传统天气预报基于数值天气预报(NWP),它依赖于基于物理的大气模拟。
基于机器学习(ML)的天气预报(MLWP)的最新进展已产生了基于ML的模型,其预测误差比单一NWP模拟更小。然而,这些进展主要集中在单一的、确定性的预测上,不能代表不确定性和估计风险。总体而言,MLWP仍然不如最先进的NWP集合预测准确可靠。
研究组介绍了GenCast(一种概率天气模型),其能力和速度优于世界上最先进的中期天气预报系统ENS(欧洲中期天气预报中心的集合预报)。GenCast是一种经过数十年再分析数据训练的ML天气预报方法。
GenCast在8分钟内生成了一个12小时步长、0.25°经纬度分辨率、超过80个地表和大气变量的随机15天全球预报集合。在研究组评估的1320个目标中,其中97.2%的目标GenCast的能力优于ENS,且能够更好地预测极端天气、热带气旋路径和风力发电。
该工作有助于打开业务天气预报的新篇章,助力更准确、更有效地做出与天气有关的关键决策。
▲ Abstract:
Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP), which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.