Transcript Pitch angle evolution of energetic electrons at

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Wave particle
Interactions in the
Inner Magnetosphere
R. Friedel
ISR-1, Los Alamos National Laboratory, USA
Plus many community contributions…
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GEM workshop, Snowmass, June 2013 – Student selected Tutorial
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Contents
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Motivation
“Once upon a time in the radiation belts”
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Inner radiation Belt WP modeling approaches
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Brief History
Current Status
Dynamics
Classes of Models
Diffusion coefficient calculations
Limits of pure diffusion codes
Role of Proxies
– Background electron density proxy
– LEO Wave proxy
– Substorm injection proxy
•
Summary/Conclusion
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Motivation
•
This talk looks at wave particle interactions from the point of view of how
they are modeled in inner radiation belt MeV electron models, looking at
current limitations.
•
I am NOT reviewing here the detailed micro-physics of wave particle
interactions – that would be presumptuous in the presence of experts in
the field (some present here) – Danny Summers, Jay Albert, the three
Richards (Thorne, Horne, Denton), Yoshiharu Omura, Jacob Bortnik, plus
many others!
•
I ask the question: What do we need to do better to increase the fidelity of
inner radiation belt wave particle interaction modeling? With our current
approaches, are we on the right path? Are we getting lost in detailed
physics that while “interesting”, may not play an important part in global
inner radiation belt modeling?
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Brief History (1)
From Friedel et al. 2002 review
Initially observed as dropout
followed by a delayed increase
of relativistic electrons at
geosynchronous orbit during
recovery phase of storm.
Up to 3 orders of magnitude
increase of ~2 MeV electrons
(blue line)
Initially a zoo of proposed
mechanisms (See review,
Friedel et.al, 2002): external
source, recirculation, internal
source, MeV electrons from
Jupiter…
For a more recent review see Shprits et al 2008a, b; JGR
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Brief History (2)
Results form Reeves et al. 2003
Difficulty in understanding dynamics of system: Wide range of
responses for similar geomagnetic storms – Increase / Decrease / Shift
of peak / No change - are all possible responses
Many processes operate simultaneously that cannot be separated
observationally
Response thought to be result of a delicate balance of loss, transport
and internal energization processes.
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Quick Question:
Why can’t current models reproduce observed range of dynamics?
We have a range of quite sophisticated modelling approaches for the inner
radiation belts, that include transport, acceleration, losses. What’s missing?
I would hold that our current models DO include the major physical processes,
but that we are driving these models with broad statistical inputs (DLL, wave
statistics driving DEE and Dαα, simple density models, badly constrained
boundary conditions)
Simply: Average inputs in -> average behaviour out
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Current Status (1) – Internal/External Source
Results from Yue Chen et al. (Nature Physics, 2007)
Strongly suggestive of internal source
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Current Status (1) – Internal/External Source
Results from Geoff Reeves et al. (Science, submitted, 2013)
μ = 3433 MeV/G
K =0.11 Re G1/2
Final proof of
internal source?
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Current Status (2) – The wave picture
(or: no talk on waves is complete without this graphic from Danny Summers)
Plasmasheet:
Source of seed population
(convection&impulsive injection)
Magnetopause:
Possible loss mechanism,
shadowing + outward diffusion
Waves:
Drifting electrons encounter
several possible wave regions
Hiss (loss) inside
plasmasphere/plumes,
Chorus (energization) outside
plasmasphere, and EMIC (strong
loss) at edge of plasmasphere /
plumes. New: magnetosonic
waves near equator
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Current Status (3) - Characteristics of Fast Waves
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Current Status (3) - Characteristics of Slow Waves
ULF waves from MHD simulations
Bz relative to a dipole field in LFM (left); and in a coupled LFM-RCM simulation,
from Pembroke et al. (2012).
Also numerous studies on ULF observations from spacecraft (GOES, CRRES,
etc) – used to calculate DLL, drift resonance interactions
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Radiation Belt Dynamics (1)
The intensity and the
structure of the relativistic
electron belts is controlled
by a balance of:
 acceleration
 transport
 & losses
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Radiation Belt Dynamics (2)
Earthward Radial Diffusion produces
betatron acceleration as electrons
move to regions of higher B.
Perpendicular energy gain enhances
the flux of 90° pitch angles.
Magnetic moment, µ, is conserved
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Radiation Belt Dynamics (3)
Substorm Injections will
produce similar effects to
diffusion and are critical
for moving particles from
open to closed drift
orbits.
-> source particles for
energization processes
-> free energy for waves
-> NOT included in
current modeling efforts!
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Radiation Belt Dynamics (4)
Whistler mode Hiss
inside the plasmasphere
produces electron loss
through precipitation.
Plasmapause position
seems to control the
inner edge of the outer
electron belt.
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Radiation Belt Dynamics (5)
EMIC Waves are
produced when hot ring
current ions stream
through the dense
plasmasphere/
plasmaspheiric plumes
EMIC waves can
produce strong MeV
pitch angle scattering
leading to electron
precipitation and
isotropization.
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Radiation Belt Dynamics (6)
VLF Chorus is
produced by injected
hot electrons.
Doppler-shifted
cyclotron resonance
can produce both pitch
angle diffusion (losses)
and energy diffusion
(acceleration).
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Current Status (4) – Chorus = internal source?
Evidence from Meredith et al. 2003
CRRES data: October
9th 1990 Storm
Recovery phase associated
with:
– prolonged substorm
activity.
– enhanced levels of
whistler mode chorus.
– gradual acceleration of
electrons to relativistic
energies.
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Current Status (4) – ULF drift resonance = internal
source? Evidence from Rostoker et al. [1998]
ULF wave power observed by a ground magnetometer plotted together
with energetic electron fluxes observed at geosynchronous orbit.
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Current Status (4) – Main Phase Losses?
From Green et al., Ukhorisky et al., Chen et al, Shprits et al., Turner et al …
Magnetopause:
Chen computes last closed drift shell from T01s model and shows that this
boundary is often near GEO, down to L*=4.5 for major storms. This plus
outward diffusion due to negative gradients (Chen, Shprits) can lead to
significant losses (Shprits, Turner).
Waves:
EMIC for strong diffusion losses proposed (Summers, Horne, Thorne). Recent
data (Friedel, Chen) point to this NOT to be a major loss process (but it
probably happens, data from NOAA [Søraas]). Whistler/lightning induced losses
play a role (Rogers) as do ground based transmitters (Abel &Thorne, Starks).
Microburst / Precipitation bands
Observations from Sampex (O’Brien). Co-located with Chorus region. With
some assumptions, could explain all relativistic electron losses on their own.
Also observations from balloons (Milan). Alex Crew estimates 10/20% for main
phase/recovery phase max loss contribution (at this meeting).
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Current Status (4) – Observation of fast losses
From Morley et al, 2010 – new RBSP observations at this meeting!
Loss of MeV electrons
down to
L=4 within 1-2 hours
Superposed epoch
(Morley 20111) study
shows these dropouts
are a consistent
signature of High Speed
Solar wind Interactions
with the
magnetosphere.
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Inner Radiation belt modeling Approaches (1)
Modeling the effect of wave particle interactions on trapped electrons
Main classes of models:
1.Diffusion models based on Fokker-Planck Equation.


Uses diffusion coefficients to model the effects of waves on radial, pitch angle,
energy and cross diffusion
Simple lifetimes to model pitch angle diffusion loss
2.RAM-type drift physics codes


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Uses DLL in static fields or calculates drifts in self consistent magnetic and
electric fields
Simple lifetimes to model pitch angle diffusion loss
Uses DEE and Dαα + cross terms) with statistic wave amplitudes or with
calculated growth rates -> wave amplitudes
3.MHD codes with particle tracers
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Radial diffusion from self-consistent fields
Traced particles use DEE and Dαα with statistic wave amplitudes
4.Hybrid codes

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Can treat self-consistent EMIC / whistler growth & interaction
Limited coupling to global codes
5.PIC codes

Once these do the global magnetosphere we may all be able to go home…
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Inner Radiation belt modeling Approaches (2)
What limits current wave particle interaction modeling the most?
For ULF wave / magnetic+electric field fluctuation driven radial diffusion
global, coupled MHD codes (e.g. LFM + RCM or variants of coupled codes
in the SWMF are maturing and may be able to soon replace statistic DLL
formulations.
For the faster wave modes (EMIC, Chorus, Hiss, Magnetosonic) we may
need to rely on diffusion coefficients for some time yet. Required inputs:
Background plasma density, ion composition, background magnetic field,
wave fields.
For bounce/drift averaged quantities, these need to be known globally.
-> Many approximations, many degrees of freedom.
Additional limitations are all the approximations of quasi-linear theory.
Strong non-linear effects are not yet taken into account - these may be able
to be included using additional advection terms (Albert).
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Inner Radiation belt modeling Approaches (3)
Diffusion coefficient calculation (Glauert et al, Summers et al, Albert etc)
Diffusion coefficient calculations based on quasi-linear theory are
computationally expensive and the community has spent a lot of effort to
perform these calculations with varying degrees of approximations:
For background environmental
conditions:
For the waves:
-
-
-
Dipole magnetic field
Some dynamic field
models
Simple background
density models
Simple ion composition
models
-
-
-
First order resonances
only
Parallel propagation of
waves only
Assumed k-distribution of
waves (guided by data)
Assumed frequency
distribution of waves
(guided by data)
Fixed K-distribution along
field lines
No feedback of particles
on waves, no damping
Currently parameterized
by wave power only
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For global wave power
distribution:
-
We never have global insitu wave data
Simple statistics based
on geomagnetic activity
indices
Assumes instantaneous
MLT distribution =
statistical MLT
distribution
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Inner Radiation belt modeling Approaches (3)
Wave Models – Model grid and distribution in one bin
L-shell: [3, 12] in step of .2
Local Time: [0, 24]hr in step of 1 hr
Mag. Latitude Ranges: [0, 10], [10, 25],
[25, 35] and >35 deg
AE ranges: <100nT, [100, 300)nT and
>300nT
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Inner Radiation belt modeling Approaches (4)
Using diffusion coefficients - challenges
Some basic questions to ponder:
•Which of the listed approximations is the “tall pole”?
•Particles drift through wave regions repeatedly and integrate the effects of
waves over time. How spatially or temporally detailed do we need to make
our diffusion coefficients?
•Will eliminating the current approximations in calculating diffusion
coefficients lead to computationally prohibitive complexity?
•Have we reached or will we soon reach the limits of the diffusive approach
for modelling the fast wave/particle interaction?
•Is our modelling of fast wave particle interactions the “tall pole” in the
overall modelling of the inner radiation belt? Compared to, e.g
•
•
•
Specification of boundary conditions
Specification of global magnetic fields
Effects of un-modelled fast transport such as substorm injections
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Limitation of pure diffusion codes (1)
Pitch angle diffusion in realistic fields – drift shell splitting
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Specifying needed inputs for wave-particle
interaction modeling through proxies
• Space Physics abounds in the use of proxies, e.g. Dst
for the ring current, AE for the electrojet currents, ABI
(auroral boundary index from DMSP) for auroral activity,
etc…
• Advantages: Cheap, often based on simple
instrumentation, ground based or based on
programmatic missions, can be global and available
24/7, long term availability. Can form a reliable
operational input to radiation belt models.
• Disadvantages: Often coarse (integrative), may respond
to multiple physical processes, mapping to high altitude
magnetosphere often problematic.
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Background electron density
Relativistic electron lifetimes from
HEO (Joseph Fennell, Aerospace
Corporation).
Modeled electron lifetimes
from Hiss (Chris Jeffery,
LANL)
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PLASMON: Proxies for electron density driving
assimilative plasmasphere models
Lead by Janos
Lichtenberger, Eötvös
University, Budapest
Uses ground based
data from whistlers,
field line resonances
with in –situ data from
LANL MPA, Themis
and RBSP with a data
assimilative
plasmasphere model
lead by Anders
Jorgensen, NM Tech
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PLASMON: Automated analyses of whistlers – virtual
(whistler) trace transformation [Lichtenberger, JGR, 2009]
Whistler Nose frequency
related to equatorial electron
density and density profile
along field line.
log10 neq=A + B⋅L
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PLASMON: Automated analyses field line resonances –
cross phase method, FLRINV [Berube et al. 2003]
Method yields
mass density
along field line
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PLASMON: Automated analysis of field line resonances –
cross phase method, FLRINV [Berube et al. 2003]
Example of automated detection of resonance frequency –
example of continuous detection over ~12 hours from
European EMMA chain of magnetometer stations
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PLASMON: data assimilative plasmasphere
modeling
Data assimilation
result with minimal
ground-based data
set.
Observations (red),
assimilation result
(black), and
a reference model
(blue).
Uses densities inferred from whistlers and field line resonances with a data
assimilative DGCPM plasmasphere model [Anders Jorgensen, NM Tech].
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LEO particle precipitation proxy for high altitude
wave distribution and intensity (Y. Chen, LANL)
Comparing CRRES wave statistics with NOAA 30 KeV
precipitation statistics – deriving model relationship
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LEO particle precipitation proxy for high altitude
wave distribution and intensity
Using the statistical wave proxy for near-global, 12hr
resolution wave maps during a geomagnetic storm
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LEO particle precipitation proxy for high altitude
wave distribution and intensity
Using the statistical wave proxy for real-time wave
prediction at RBSP
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GPS operational dosimeters as a proxy for
substorm injection distribution (todo)
GPS ns08
07/1983 – 02/1984
BDD-I
GPS ns10
10/1984 – 11/1992
BDD-I
GPS ns18
01/1990 – 12/1995
BDD-II
GPS ns24
11/1991 – 11/2000
BDD-II
GPS ns28
05/1992 – 09/1996
BDD-II
GPS ns39
07/1993 – 10/2005
BDD-II
GPS ns33
04/1996 – 01/2007
BDD-II
GPS ns41
12/2000 – today
BDD-IIR
GPS ns54
12/2002 – today
CXD
GPS ns56
02/2003 – today
CXD
GPS ns60
07/2004 – today
CXD
GPS ns61
11/2004 – today
CXD
GPS ns59
12/2004 – today
CXD
GPS ns53
10/2005 – today
CXD
GPS ns58
12/2006 – today
CXD
GPS ns55
10/2007 - today
CXD
GPS ns57
12/2007 – today
CXD
GPS ns48
02/2008 – today
BDD-IIR
GPS ns50
08/2009 – today
BDD-IIR
100/200 keV – 10 MeV electrons
5/9 MeV – 60 MeV protons
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GPS high temporal and spatial resolution data
November 2003 High Speed Stream event (3hr, 0.10L)
GEO
GEO
GPS
0.15 MeV
GPS
GPS
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1.25
1.25 MeV
MeV
GPS
GPS
3 MeV
3 MeV
DST
DST
SW Speed
SW Speed
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Summary / Conclusion
1. Coupled MHD codes as a way to “do” radial diffusion is
maturing.
2. For “fast” wave particle interactions the use if diffusion codes
for the global problem is likely to be around for some time
3. Main limitation today seem not to be in the modeling of the
physics of wave particle interactions but in the specification
of required inputs.
4. We need to look to other data sources and other methods to
specify these inputs (e.g n, BW) in order to increasethe
fidelity of modeling.
5. Ground based / programmatic satellite inputs will be needed
for long term operational modeling efforts.
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