UCLA-LANL Reanalysis Project atmos.ucla/reanalisys

1. UCLA-LANL Reanalysis Projectwww.atmos.ucla.edu/reanalisys Yuri Shprits 1 Collaborators: Binbin Ni 1, Dmitri Kondrashov 1, Yue Chen 2, Josef Koller 2, Reiner Friedel 2, Geoff Reeves 2, Michael Ghil 1, Richard Thorne 1, Tsugunobu Nagai 3 1Department of Atmospheric and Oceanic Sciences, UCLA, Los Angeles, CA 2 Los Alamos National Lab, Los Alamos, NM

2. Talk Outline • Acceleration and loss processes in the Earth’s radiation belts • Combining radial diffusion model with observations by means of Kalman filtering (performing reanalysis) • Comparison between ensemble and exact Kalman filters • Comparison between reanalysis obtained with Akebono and CRRES observations • Sensitivity of the reanalysis to the assumed magnetic field model • Summary and Conclusions

3. Dominant acceleration and loss mechanisms of relativistic electrons in the outer radiation belt Losses • Plasmaspheric Hiss ( whistler mode waves) loss time on the scale of 5-10 days • Chorus waves outside plasmapause provide fast losses on the scale of a day • EMIC waves mostly in plumes on the dusk side very fast localized 4) Combined effect of losses to magnetopause and outward radial diffusion Sources • Inward radial diffusion • Local acceleration due to chorus waves

4. Lifetime, days Kp index Time, days Time, days Phase Space Density L-value Phase Space Density L-value Time, days Monotonic profiles of PSD obtained with a radial diffusion model.

5. Comparison of the radial diffusion model and observations, starting on 08/18/1990.

6. Kalman Filter Assume initial state and data and model errors Make a prediction of the state of the system and error covariance matrix, using model dynamics Compute Kalman gain and innovation vector Compute updated error covariance matrix Update state vector using innovation vector

7. Comparison of the model with data assimilation with Daily-averaged CRRES observations.

8. Comparison between UCLA Kalman filter approach and LANL ensemble Kalman filter

9. Comparison between reanalysis obtained with Akebono and CRRES observations Ni et al.,| 2009a

10. Comparison Between the Radial Diffusion Model and Reanalyses Ni et al.,| 2009a

11. Global Coherency Ni et al.,| 2009a

12. Comparison of reanalyses obtained with various magnetic field models. Ni et al.,| 2009b

13. Inacuracies associated with a choice of magnetic field model for various satellites Ni et al.,| 2009b

14. Simulations with VERB diffusion code. Phase Space Density at m=850 MeV/G ; K=0.025 G0.5 RE

15. Summary • Data assimilation allows to blend observations from various satellites with a model, minimize errors of individual measurements and produce high resolution in time and space reconstruction of the phase space density. • Comparison of reanalyzes from the polar orbiting Akebono and nearly equatorial CRRES spacecraft shows that data assimilation can be used to accurately reconstruct radiation belt phase space density. • Results of the reanalysis are insensitive to a choice of magnetic field model. • Reanalysis shows persistent peaks in phase space density which are consistent with the local acceleration processes. • Global coherency of the radiation belt PSD indicates that pitch-angle distributions reach the lowest normal mode and decay as whole on the time-scales of a day.

16. Data assimilation with synthetic data produced with a radial diffusion model with t=1/Kp

17. Data assimilation with synthetic data produced with a radial diffusion model with t=5/Kp

18. Innovation vector