数据同化在空间天气学中的应用

由太阳活动引起的耀斑和日冕物质抛射等短时间尺度变化的空间天气事件会影响并危害地球磁层、电离层、中高层大气、卫星运行安全以及人类健康，因此对这些空间天气事件的预测显得尤为重要。数据同化在稀疏观测和异步采集的情况下能够增加模型的预测能力，对模型变量进行自洽分析。在数值预报中引入数据同化方法，能够提高预测可信度。本文从数据同化方法的角度出发，主要分析了数据同化目前在大气、电离层、磁层、太阳及其他行星科学研究中的应用，并初步讨论了数据同化未来在空间天气方面的应用。 

Application of Data Assimilation in Space Weather

Space weather events caused by solar activities such as flares and Coronal Mass Ejections (CMEs) can affect the magnetosphere of the Earth, the middle and upper atmosphere, ionosphere, the safety of satellite operation and human health directly or indirectly. Therefore, the prediction of space weather events is particularly important. In the case of sparse observation and asynchronous sampling, data assimilation can increase the prediction ability of the model, self-consistent analysis of model variables can be carried out, and the introduction of data assimilation method in numerical prediction can improve the reliability of the prediction. This paper mainly introduces the application of data assimilation in the atmosphere, ionosphere, magnetosphere, the Sun and other planets from the perspective of data assimilation methods. The potential applications of data assimilation in space weather in the future are also discussed.