Presentation on theme: "Improving Salammbô model and coupling it with IMPTAM model V. Maget 1 D. Boscher 1, A. Sicard-Piet 1, N. Ganushkina 2 1. ONERA, Toulouse, FRANCE 2. FMI,"— Presentation transcript:
Improving Salammbô model and coupling it with IMPTAM model V. Maget 1 D. Boscher 1, A. Sicard-Piet 1, N. Ganushkina 2 1. ONERA, Toulouse, FRANCE 2. FMI, Helsinki, FINLAND SPACECAST Final Outreach Meeting, BAS, Cambridge, 07 th February, 2014
Bases to better understand Radiation belts modelling Particles of interest today: Electrons: from keV up to a few MeV Plots of interest: L* vs Time representation Making a satellite fly in the model Origin: Sun (through plasmasheet) Effects: TiD, surface and deep charging GEO GPS POES15 >100keV RBSP-A 0.57-1.12 MeV Earth’s radiation belts bases: 3 quasi-periodic movements due to magnetic field trapping Definition of an adequate coordinate system L*, Aeq, MLT, Energy What a satellite really observes ?
Purpose: the global optimization problem The model is a complex balance between all active physical processes Work done during the SPACECAST project: Improving the most significant bricks of physics ahead of Salammbô model Determining the best combination of them by comparing to real data (GEO, Van Allen Probes, …) Poor physics below about 100 keV due to E-field influence: plug with IMPTAM model Boundary condition Radial diffusion w-p interactions GPS GEO
Optimizing the science bricks combination (1/3) High activity 1.5 MeV Low activity 1.5 MeV Improved bricks during the SPACECAST project: Enhanced radial diffusion model based on data Boundary conditions (THEMIS and NOAA-POES data statistical analysis) Wave-particle interactions (primarily Chorus waves and plasma densities influence) Drop-outs modelling (focus on in the following) Influence of boundary conditions and radial diffusion modelling: Flux in MeV -1 cm -2 s -1 sr -1 L* 10 9 10 7 10 5 10 3 10
Optimizing the science bricks combination (2/3) Cold plasma densities influence wave – particle interaction: Only few cold plasma models exist The density shapes the interaction Worst cases can be defined Flux energy spectra may be very influenced by this density Low density High density
Optimizing the science bricks combination (3/3) Initial state and wave-particle interactions modelling influence: From 27 th February, 2013 to 28 th March, 2013 Two sets of wave-particle interactions (all type of waves) RBSP-A 0.57-1.12 MeV RBSP-A 0.05-0.06 MeV Salammbô 0.57-1.12 MeV Salammbô 0.05-0.06 MeV Kp index
Modelling magnetopause shadowing effect (1/4) What is magnetopause shadowing effect?
Modelling magnetopause shadowing effect (2/4) October 1990 magnetic storm Comparison with CRRES data 330 keV 1.17 MeV CRRES NO DROPOUTS DROPOUTS KP INDEX
Modelling magnetopause shadowing effect (3/4) 16 th – 30 th September 2007 drop-outs GOES 10 GOES 12 > 600 keV > 2 MeV > 600 keV > 2 MeV Observation Simulation without drop-outs modelling Simulation with drop-outs modelling Integrated flux in cm -2 s -1 sr -1 Color code
Modelling magnetopause shadowing effect (4/4) Inclusion in the upcoming release
Improving low energy rendering: IMPTAM plug Work in progress… Salammbô 3D physics is poor below about 100 keV thus IMPTAM outputs are considered as always better ! The coupling is based on a data assimilation pattern: each time IMPTAM outputs are available they are ingested in Salammbô Encouraging first results Kp index 50 – 75 keV 170 – 250 keV Observation from LANL_97A Salammbô alone Salammbô + IMPTAM Color code
Conclusions Bricks of physics have been improved (radial diffusion, boundary condition, wave-particle interaction) Their combination improves SALAMMBO precision (factor of 2 to 10) Still a challenge to select the perfect combination valid for any magnetosphere configurations (depend on energies, magnetic activities, initialisation, plasma densities …) Drop-outs modeled in Salammbô model: improve the results (will be included in the upcoming release) IMPTAM model improves SALAMMBO outputs below 100 keV Each step made has been compared to in-flight measurements
Acknowledgements The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 262468, and is also supported in part by the UK Natural Environment Research Council