Forecast improvements from solar wind data assimilation

电网和电信网络等技术基础设施容易受到空间天气的影响。对于空间天气预报来说，更多的准备时间需要准确地表示近地空间的太阳风条件。目前，太阳风预报模型都是自由运行的，没有超出初始条件的观测约束。数据同化(DA)是将模式和观测数据合并以确保对现实的最佳估计的过程。数值天气预报通过观测网络的扩展和DA的应用，在准确预报时间方面取得了巨大的进步。第一个太阳风DA实验使用简单的二维模型来重建太阳风速。这种方法显示出改进预测的巨大希望，尽管仍然存在一些悬而未决的问题。已经提出了一种结合三维结构的方法，尽管还没有实现。

Technological infrastructures, such as power grids and telecommunications networks, are vulnerable to space weather. For space-weather forecasting, increased lead time requires accurate representation of the solar wind conditions in near-Earth space. At present, solar wind forecast models are "free running" without an observational constraints beyond the initial conditions. Data assimilation (DA) is the process of merging model and observational data to ensure an optimal estimate for reality. Numerical Weather Prediction has made huge strides in accurate forecast lead time through the expansion of the observational network and the application of DA. The first solar wind DA experiments have used simple 2-dimensional models to reconstruct solar wind speed. This approach shows great promise for improved forecasting, though a number of outstanding issues remain. A method for incorporating the 3-dimensional structure has been proposed, though has yet to be implemented.