ucla-lanl reanalysis project yuri shprits 1 collaborators: binbin ni 1, dmitri kondrashov 1, yue...
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UCLA-LANL Reanalysis Project
www.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
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
Dominant acceleration and loss mechanisms of relativistic electrons in the outer radiation belt
Losses
1) Plasmaspheric Hiss ( whistler mode waves) loss time on the scale of 5-10 days
2) Chorus waves outside plasmapause provide fast losses on the scale of a day
3) EMIC waves mostly in plumes on the dusk side very fast localized
4) Combined effect of losses to magnetopause and outward radial diffusion
Sources
1) Inward radial diffusion
2) Local acceleration due to chorus waves
Kp
ind
ex
Lif
etim
e, d
ays
Ph
ase
Sp
ace
Den
sity
Phase Space Density
Time, days Time, days
L-value Time, days
L-v
alu
e
Monotonic profiles of PSD obtained with a radial diffusion model.
Comparison of the radial diffusion model and observations, starting on 08/18/1990.
L
Radial Diffusion Model
3
4
5
6
7
-1
-0.5
0
0.5
L
Hourly averaged CRRES observations
=700 MeV G-1 K=0.11 G0.5 RE
3
4
5
6
7
-1
-0.5
0
0.5
0 10 20 30 40 500
2
4
6
8
Time, days
Kp
Make a prediction of the state of the system and error
covariance matrix, using model dynamics
Kalman Filter
fkk
fk ww 11
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
fkk
fk ww 11 i
kfk
ak www
ik
fk
ak www
Comparison of the model with data assimilation with Daily-averaged CRRES observations.
L
Model with data assimilation
3
4
5
6
7
-1
-0.5
0
0.5
0 10 20 30 40 500
2
4
6
8
Time, days
Kp
L
Daily averaged CRRES observations
=700 MeV G-1 K=0.11 G0.5 RE
3
4
5
6
7
-1
-0.5
0
0.5
Inacuracies associated with a choice of magnetic field model for various satellites
Ni et al.,| 2009b
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.
Data assimilation with synthetic data produced with a radial diffusion model with Kp
L
Radial Diffusion Model with = 1/Kp
4567
-1-0.500.5
LSparce data, Radial Diffusion Model with = 1/Kp
=700 MeV G-1 K=0.11 G0.5 RE
4567
-1-0.500.5
L
Radial Diffusion Model with = 5/Kp
4567
-1-0.500.5
L
Radial Diffusion Model with = 5/Kp, data = 1/Kp
4567
-1-0.500.5
0 10 20 30 40 500
5
Time, days
Kp
204060
Data assimilation with synthetic data produced with a radial diffusion model with Kp
L
Radial Diffusion Model with = 5/Kp
4567
-1-0.500.5
LSparce data, Radial Diffusion Model with = 5/Kp
=700 MeV G-1 K=0.11 G0.5 RE
4567
-1-0.500.5
L
Radial Diffusion Model with = 1/Kp
4567
-1-0.500.5
L
Radial Diffusion Model with = 1/Kp, data = 5/Kp
4567
-1-0.500.5
0 10 20 30 40 500
5
Time, days
Kp
204060