Yuri Shprits 1 , Binbin Ni 1 , Yue Chen 2 , Tsugunobu Nagai 3 , and Dmitri Kondarashov 1
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I Reanalysis of the Radiation Belt Fluxes Using CRRES and Akebono Satellites. II What can we Learn From Reanalysis. Yuri Shprits 1 , Binbin Ni 1 , Yue Chen 2 , Tsugunobu Nagai 3 , and Dmitri Kondarashov 1 1 Department of Atmospheric and Oceanic Sciences, UCLA, Los Angeles, CA 2 Los Alamos National Laboratory Los Alamos, NM 3 Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Tokyo, Japan.
description
I Reanalysis of the Radiation Belt Fluxes Using CRRES and Akebono Satellites. II What can we Learn From Reanalysis. Yuri Shprits 1 , Binbin Ni 1 , Yue Chen 2 , Tsugunobu Nagai 3 , and Dmitri Kondarashov 1 1 Department of Atmospheric and Oceanic Sciences, UCLA, Los Angeles, CA - PowerPoint PPT Presentation
Transcript of Yuri Shprits 1 , Binbin Ni 1 , Yue Chen 2 , Tsugunobu Nagai 3 , and Dmitri Kondarashov 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 LaboratoryLos Alamos, NM
3Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Tokyo, Japan.
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Phase Space Density
Time, days Time, days
L-value Time, days
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Monotonic profiles of PSD obtained with a radial diffusion model.
Shprits and Thorne, 2004
Brautigam and Albert, 2000
Comparison of the radial diffusion model and CRRES observations, starting on 08/18/1990
Make a prediction of the state of the system and error
covariance matrix, using model dynamics
Kalman Filter
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Compute Kalman gain and innovation vector
Update state vector using innovation vector
Compute updated error covariance matrix
Assume initial state and
data and model errors
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Comparison of Reanalysis with near-equatorial CRRES and polar-orbiting Akebono satellites
3D UCLA code simulations which can be used for the 3D data assimilation
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 .
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iim sEOFtPCsftsf )()()(),(
),,( ELs
EOFs of SST
El Niño-3 Index comparison with PC-1
ENSO – II
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.
