Forecast of Actionable Radiation Belt Scenarios

在空间天气预报建模中，大多忽略了一切都是由太阳驱动的事实，这基本上是不可预测的。将观测到的太阳动力学模型应用于地球是有问题的，它依赖于我们无法约束其边界条件的模型。我们仅限于L1的数据，给出了一个小时的提前期和控制参数（例如Kp）的神经网络类型预测，这些参数决定了我们最佳模型的物理特性。Nowcasts更好：先进的数据同化技术与基于物理的模式在再现真实的辐射带（RB）环境方面显示出很高的保真度。由于缺乏地球观测卫星以外的高质量实时数据，这种临近预报的实际使用受到限制。FARBES项目则不同：它将其雄心限制在对卫星运营商有用的简单、可实现的预测目标上，同时避免了过去项目的陷阱。我们认为，虽然可能不可能准确预测空间天气事件的中断，但一旦事件开始，我们就有工具来预测随后的行为，并在事件期间更新我们的预测。虽然我们可能无法全面详细预测随后的动态行为，但我们可以为卫星运营商提供一些关键事件特征的可操作预测：到达最恶劣环境的时间B。最严重的通量达到C。这些特征被航天器运营商代表在ESWW 16上认为是最有用的[http://www.stce.be/ esww13/contributions/public/S5-O1/S5-O1-03-PitchfordDave/FORECASTINGTHEPERFECTSTORM.ppt]。我们通过使用基于物理学的模型克服了数据同化临近预报的局限性，该模型仅由简单、经济实惠和可靠的地基实时输入驱动，我们通过使用基于地球物理学的RB动力学预测方法克服了我们无法准确预测磁层驱动因素的缺陷，该方法从临近预报开始，并在事件期间通过实时模型输入的持续可用性不断改进。

Forecast in Space Weather modeling mostly ignore the fact everything is driven by the sun, that is basically unpredictable. Propagating observed solar dynamics to Earth is questionable, it depends on models whose boundary conditions we are incapable of constraining. We are limited to data at L1, giving a one hour lead time and neural net type forecasts of controlling parameters ( e.g. Kp) that govern the physics of our best models. Nowcasts are better: advanced data assimilation techniques with physics based models show great fidelity in reproducing the real radiation belt (RB) environment. Operational use of such Nowcasts is limited by lack of high quality real-time data beyond GEOS. The FARBES project is different: it limits its ambition to simple, achievable prediction goals that are of utility to satellite operators, while avoiding the pitfalls of past projects. We hold that while it may be impossible to accurately predict the break of a space weather event, once an event has started we have the tools to predict subsequent behavior and to update our predictions during the event. While we may not be able to globally predict in detail the subsequent dynamic behavior, we can provide actionable forecasts for satellite operators on a few key event characteristics: a. Time to most severe environment b. Most severe Flux reached c. Time to the end of event These characteristics were deemed most useful by spacecraft operator representatives at ESWW16 [http://www.stce.be/ esww13/contributions/public/S5-O1/S5-O1-03-PitchfordDave/FORECASTINGTHEPERFECTSTORM.ppt]. We overcome the data assimilation nowcast limitations by using physics based models driven by simple, affordable and reliable ground-based real-time inputs only, we overcome our inability to accurately forecast magnetospheric drivers by using a scenario-driven forecast approach for RB dynamics starting with nowcast and is constantly refined during an event by the ongoing availability of real-time model inputs.Workday Project Setup Complete