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 conditio...
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Información proyecto FARBES
Duración del proyecto: 35 meses
Fecha Inicio: 2023-01-01
Fecha Fin: 2025-12-31
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Descripción del proyecto
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 environmentb. Most severe Flux reachedc. Time to the end of eventThese 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