数值天气预报资料同化的发展与展望

Overview and prospect of data assimilation in numerical weather prediction

  • 摘要: 资料同化是结合数值天气预报与多源大气观测资料,以获得最能代表大气状态的数值天气预报所需初值的方法。随着数值模式和观测系统的迅速发展,资料同化已发展为可考虑大气不同时、空尺度不确定性特征、不同种类观测误差特性,具有动力约束、满足模式平衡性的先进理论和方法。也有越来越多的包括地面、飞机和卫星等多手段大气观测资料得以同化使用,包括极轨、静止气象卫星测量的辐射亮温,雷达探测的径向风和反射率因子信息,全球导航卫星系统无线电掩星探测资料等。为进一步改进高影响天气事件的数值预报,目标观测技术持续发展,指导获得最大预报正影响的观测区域。基于资料同化理论和方法的发展,中国的资料同化业务系统也得到了长足进步,建立了先进的资料同化业务系统,5 d全球天气预报水平在过去10年提升了约15%。在回顾过去百年资料同化发展历程的基础上,讨论了未来资料同化方法和业务系统框架的发展、新型观测资料的使用以及同化与人工智能的结合等研究方向。

     

    Abstract: For numerical weather prediction (NWP), data assimilation (DA) combines short-term forecasts and various atmospheric observations to achieve optimal initial conditions, based on which subsequent forecasts are launched. With the rapid advancements in numerical models and observing systems, DA has been significantly evolved. Modern methods now can account for uncertainties of state variables across various spatiotemporal scales, incorporate multiscale observation error statistics, and enforce dynamical constrains and model balances. Meanwhile, observations from various platforms, such as ground-based, aircraft, and satellite, have been assimilated. These include data from polar-orbiting and geostationary satellites, radar-derived radial winds and reflectivity, Global Navigation Satellite System (GNSS) radio occultations, etc. To further utilize the advanced observing systems and DA techniques for high-impact weather predictions, target observation strategies have been developed to identify areas where additional observations can yield the greatest predict improvements. Based on the advancements of DA theories and methods, China's operational systems have made significant progress, establishing advanced operational DA systems. Over the past decade, the forecast skill of 5 d global weather prediction has improved by approximately 15%. The article reviews a century of development in DA, and discusses future directions, including the advanced DA methods, operational frameworks, integration of novel observations, and the synergy between DA and artificial intelligence.

     

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