Smarter Ensembles for solar wind forecasts

太阳风是太阳上层大气中不断流出的带电粒子和磁场。太阳风条件的变化导致空间天气，这可能对电网和电信网络等技术基础设施以及在空间和高空飞行中的人类健康产生不利影响。空间天气预报需要准确预测近地空间的太阳风状况。英国气象局为此使用数值太阳风模型。为了评估预报的不确定性，使用许多初始条件稍有不同的模式运行的“集合”。这个项目将试图回答三个重要的研究问题：1。这些集合如何很好地捕捉预报的不确定性？我们如何更好地定义起始合奏？预测不确定性的主要太阳风来源是什么？这将通过将英国气象局预报模型的输出与美国宇航局和欧洲航天局航天器的数据进行比较，并通过使用雷丁的计算效率高的太阳风模型来实现。培训机会：学生将有机会参加英国和美国的相关暑期学校。该学生还将访问英国气象局，使用他们的太阳风预报系统。

The solar wind is a continuous outflow of charged particles and magnetic field from the Sun's upper atmosphere. Variability in the solar wind conditions leads to space weather, which can adversely affect technological infrastructures, such as power grids and telecommunications networks, as well as the health of humans in space and on high-altitude flights.Space-weather forecasting requires accurate prediction of the solar wind conditions in near-Earth space. The Met Office uses numerical solar wind models for this purpose. In order to assess forecast uncertainty, an "ensemble" of many model runs with slightly different initial conditions is used. This project will attempt to answer three important research questions: 1. How well do these ensembles capture forecast uncertainty?2. How can we better define the starting ensembles?3. What are the primary solar wind sources of forecast uncertainty?This will be achieved through comparison of the Met Office forecast model output with of data from NASA and ESA spacecraft, and through use of Reading's computationally efficient solar wind model.Training opportunities:The student will be given the opportunity to attend relevant summer schools, both in the UK and the US. The student will also visit the UK Met Office to work with their solar wind forecasting system.