Gneiting Tilmann, Raftery Adrian E
Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195, USA.
Science. 2005 Oct 14;310(5746):248-9. doi: 10.1126/science.1115255.
Traditional weather forecasting has been built on a foundation of deterministic modeling--start with initial conditions, put them into a supercomputer model, and end up with a prediction about future weather. But as Gneiting and Raftery discuss in their Perspective, a new approach--ensemble forecasting--was introduced in the early 1990s. In this method, up to 100 different computer runs, each with slightly different starting conditions or model assumptions, are combined into a weather forecast. In concert with statistical techniques, ensembles can provide accurate statements about the uncertainty in daily and seasonal forecasting. The challenge now is to improve the modeling, statistical analysis, and visualization technologies for disseminating the ensemble results.
传统的天气预报建立在确定性建模的基础之上——从初始条件开始,将其输入超级计算机模型,最终得出对未来天气的预测。但正如格奈廷和拉夫蒂在他们的《观点》文章中所讨论的,一种新方法——集合预报——在20世纪90年代初被引入。在这种方法中,多达100次不同的计算机运行,每次运行的起始条件或模型假设略有不同,被组合成一个天气预报。与统计技术相结合,集合预报可以提供关于每日和季节性预报不确定性的准确表述。现在面临的挑战是改进用于传播集合预报结果的建模、统计分析和可视化技术。