Yuri Shprits 1 , Binbin Ni 1 , Yue Chen 2 , Tsugunobu Nagai 3 , and Dmitri Kondarashov 1

1. I Reanalysis of the Radiation Belt Fluxes Using CRRES and Akebono Satellites.II What can we Learn From Reanalysis. Yuri Shprits1 , Binbin Ni 1, Yue Chen 2, Tsugunobu Nagai3, and Dmitri Kondarashov1 1Department of Atmospheric and Oceanic Sciences, UCLA, Los Angeles, CA 2Los Alamos National Laboratory Los Alamos, NM 3Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Tokyo, Japan.

2. 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. Shprits and Thorne, 2004 Brautigam and Albert, 2000

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

4. 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

5. Comparison of Reanalysis with near-equatorial CRRES and polar-orbiting Akebono satellites

6. 3D UCLA code simulations which can be used for the 3D data assimilation

7. PCA (Principal Component Analysis) • PCA’s operation is to reveal the internal structure of data in an unbiased way. • PCA supplies the user with a 2D picture, a shadow of this object when viewed from its most informative viewpoint. • PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. • PCA can be used to develop predictive empirical models and metric scores and forecast skills .

8. EOFs of SST

9. El Niño-3 Index comparison with PC-1

10. ENSO – II

11. Summary • Data assimilation is a powerful tool for reconstructing PSD in the radiation belt (performing reanalysis). • Reanalysis obtained with CRRES and Akebono spacecraft shows similar peaks in PSD and similar trends. • Best results are obtained when data is available at all L-shells. • Reanalysis of the data obtained from multiple spacecraft may help to inter-calibrate satellites and produce more accurate reanalysis of the radiation belt PSD (minimize observational uncertainties). • Reanalysis with a 3D model will utilize a vast array of available data and will allow for an accurate analysis of the evolution of the PSD of the radiation belt electrons. • Reanalysis may help forecast skills, imperial models and find correlations in the data which may reveal the underlying physics of acceleration and loss.