Periodic Reporting for period 1 - FARBES (Forecast of Actionable Radiation Belt Scenarios)
Période du rapport: 2023-01-01 au 2023-12-31
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 whos boundary conditions we are incapable of constraining. We are limited to data collected by satellites at L1 Lagrangian Point, 1.5 million km from the Earth, 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 Geosynchronouos orbit.
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
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 Geosynchronouos orbit.
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
1st year:
1. We have created a complex wave propagation model, consisting of three submodels:
a) magnetospheric propgation
b) transionospheric propgation
c) subionospheric propgation
The submodels have been separately verified. The full model was used to calculate the in-situ VLF power for selected events. The verification of the model is going on.
2. We have developed a code that can read data files from EMMA and ENIGMA magnetometers, perform FFT to obtain power spectral densities for hourly periods, transform those into electric field power spectral densities at the equator in space, and derive hourly values of the electric field radial diffusion coefficient. We have also added a functionality for including an assessed contribution of the magnetic field radial diffusion coefficient, using a statistical approximation that links the two, however imperfectly; and performed initial tests on historical dates of quiet and active geomagnetic conditions.
3. We have developed a method for the definition, automatic detection and analysis of the events In the Radiation Belts using Ca index.
4. We have implemented the Analog Ensemble method to provide scenario-based forecasts for FARBES.
1. We have created a complex wave propagation model, consisting of three submodels:
a) magnetospheric propgation
b) transionospheric propgation
c) subionospheric propgation
The submodels have been separately verified. The full model was used to calculate the in-situ VLF power for selected events. The verification of the model is going on.
2. We have developed a code that can read data files from EMMA and ENIGMA magnetometers, perform FFT to obtain power spectral densities for hourly periods, transform those into electric field power spectral densities at the equator in space, and derive hourly values of the electric field radial diffusion coefficient. We have also added a functionality for including an assessed contribution of the magnetic field radial diffusion coefficient, using a statistical approximation that links the two, however imperfectly; and performed initial tests on historical dates of quiet and active geomagnetic conditions.
3. We have developed a method for the definition, automatic detection and analysis of the events In the Radiation Belts using Ca index.
4. We have implemented the Analog Ensemble method to provide scenario-based forecasts for FARBES.