Why have space weather forecasts not improved for over a decade?

太阳风是物质从太阳源源不断地流出，充满整个太阳系。近地空间太阳风条件的快速变化对空间和地面技术产生了影响，最明显的是电网。它还对高空飞行和太空中的人类构成辐射威胁，例如未来的月球和火星载人任务。出于这些原因，这种“空间天气”在英国国家风险登记册上占有显著位置；准确的空间天气预报是至关重要的目标。为了减轻空间天气造成的代价高昂、有时甚至是危险的影响，有必要为即将到来的事件提供足够的预警时间。由于太阳风总是远离太阳，预测更远的未来实际上意味着从更接近太阳的信息开始预测。目前，气象局(和其他国际预报机构)使用对太阳表面和低层大气的远程观测，并结合计算机模型，以预测未来2-4天的空间天气。不幸的是，这些最先进的方法预测恶劣空间天气开始的时间的准确性不足以用于许多应用，而且十多年来一直没有提高。然而，有一些对太阳风的常规测量目前并未被模型使用。使用雷丁大学在过去4-5年开发的方法，我们已经证明，通过使用一种名为数据同化(DA)的技术将这些观测数据纳入模型中，可以显著改进太阳风预测。我们将使用我们新的太阳风DA方法来确定哪些因素对空间天气预报的准确性影响最大，因此，准确预报有望达到的基本限制是什么。我们还将调整我们的太阳风DA方法，以与英国气象局的预报模型合作，为未来的空间天气预报提供重大改进。这将产生广泛的社会效益，因为卫星运行和电网都会受到空间天气的影响。

The solar wind is a continual outflow of material from the Sun, which fills the solar system. Rapid changes in the solar wind conditions in near-Earth space have consequences for technologies in space and on the ground, most notably the power grid. It also poses a radiation threat to humans on high altitude flights and in space, such as future crewed missions to the moon and Mars. For these reasons, such "space weather" features prominently on the UK National Risk register; accurate space-weather forecasting a goal of critical importance.In order to mitigate against the costly, and sometimes dangerous, impacts of space weather, it is necessary to provide adequate warning time of an incoming event. As the solar wind always flows away from the Sun, forecasting further into the future effectively means starting the forecasts with information from closer to the Sun. Currently, the Met Office (and other international forecast agencies) uses remote observations of the Sun's surface and lower atmosphere in conjunction with computer models in order to forecast space weather 2-4 days into the future. Unfortunately, the accuracy with which these state-of-the-art methods can forecast the time of onset of the severe space weather is both insufficient to be useful for many applications, and has not improved for more than a decade. However, there are measurements of the solar wind being routinely made that are not currently being used by the models. Using methods developed at the University of Reading over the last 4-5 years, we have shown that solar wind forecasts can be significantly improved by ingesting these observations into the models using a technique called data assimilation (DA). We will employ our new solar wind DA methods to determine what factors most influence space-weather forecast accuracy and, consequently, what are the fundamental limits to how accurate forecasting can hope to reach. We will also adapt our solar wind DA methods to work with the Met Office's forecast models, providing a significant improvement in future space-weather forecasting. This will have wide societal benefits, as both satellite operation and the power grids can be impacted by space weather.