once upon a time in the electron radiation belts

U N C L A S S I F I E D


Operated by the Los Alamos National Security, LLC for the DOE/NNSA

Once Upon a Time

in the

Electron Radiation Belts

R. Friedel, G. Reeves, J. Koller, Y Chen, S. Zaharia, V. Jordanova

ISR-1, Los Alamos National Laboratory, USA

Paul O’Brien (presenting)

The Aerospace Corporation, Chantilly, USA


U N C L A S S I F I E D Slide 2


REPW 2007, Rarotonga, August 2007

Contents

• Rationale

• Brief History, Current Status– “Once upon a time in the electron radiation belts”

• Limitations of modeling– Wave particle interactions– Pitch Angle Diffusion in realistic fields– Radial Diffusion– Loss processes

• Let’s not get depressed – the path forward– Data Assimilation prospects– The DREAM Code coupling Plan

• Summary/Conclusion





Rationale (1)

• The energetic electron radiation belt is of increasing scientific and operational interest.– Complex natural system.– Affects a large amount of commercial and military space assets.– Space Situational Awareness.– Propagating environment for high altitude nuclear explosions.

• A large body of recent theoretical work (incomplete list).

– Radial diffusion revisited (Chan [Rice], Elkington, [LASP]).– Detailed work on wave-particle interactions (R. Thorne [UCLA],

R. Horne [BAS], D. Summers [St. Johns], J. Albert [AFRL]…).





Rationale (2)

• Increasingly detailed modeling work (incomplete list).

– Diffusve models (Boscher&Bourdarie [ONERA], Shprits [UCLA], Koller [LANL], …).

– In ring current simulations [Miyoshi&Jordanova [LANL], Fok [GSFC], ….).

– Coupled Energy/Pitch Angle diffusion (Albert [AFRL], Shprits [UCLA], Koller [LANL], …).

• Increasingly higher fidelity data work.– Data inter-calibration and use of phase space coordinates @

constant adiabatic invariants (Chen [LANL], Bourdarie [Onera], Green [LASP], …).

– Relatively many data sources: LANL GEO/GPS, GOES, Polar, Cluster, HEO, Sampex, NOAA-POES, ….





Brief History (1)From Friedel et al. 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 mechansims (See review, Friedel et.al, 2002): external source, recirculation, internal source, MeV electrons from Jupiter…





Brief History (2)Results form Reeves et al.

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 seperated observationally

Response thought to be result of a delicate balance of loss, transport and internal energization processes.





Current Status (1) – Internal/External SourceResults from Yue Chen et al.





Current Status (2) – The wave picture(or: no talk is complete without this graphic [Summers et al])

Plasmasheet: Source of seed population (convection&impulsive injection)

Magnetopause:Possible loss mechanism for intersecting distorted drift paths + outward diffusion

Waves:Drifting electrons encounter several possible wave regionsHiss (loss) inside plasmasphere/plumes,Chorus (energization) outside plasmasphere, and EMIC (strong loss) at edge of plasmasphere / plumes.





Current Status (3) – Chorus = internal source? Evidence from Meredith et al.

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.





Current Status (4) – Losses? From Green et al., Ukhorisky et al., Chen et al, Shprits et al., …

Magnetopause:Green concluded it not to be a major loss source. Work by Ukhorisky shows distorted drift paths near dusk during storms that can intersect magnetopause. Chen computes last closed drift shell from T01s model and shows that this boundary is often near GEO, down to L*=4.5. This plus outward diffusion due to negative gradients (Chen) can lead to significant losses (Shprits). Waves:EMIC for strong diffusion losses proposed (Summers). Recent data (Friedel) and pitch angle diffusion simulations (Shprits) support this mechanism. Whistler/lightning induced losses play a role (Rogers).

Microburst / Precipitation bandsObservations from Sampex (O’Brien). Co-located with Chorus region. With some assumptions, could explain all relativistic electron losses on their own.





Limitations of modeling (1)Wave particle interactions – HISS, CHORUS, EMIC

Theoretical work (Horne/Thorne/Albert…) is mainly based on a quasi-linear approximation. To estimate pitch angle and energy diffusion coefficients, a range of input parameters are needed:

Background plasma density, ion composition, magnetic field strength, wave strength & frequency / k distribution. For bounce/drift averaged quantities, these need to be known globally. -> Many approximations, many degree’s of freedom.

PLUS – all the approximations of quasi-linear theory. Non-linear effects are not taken into account at all, however it is known that non-linear effects can produce macroscopic changes in pitch angle in one interaction.

Q: Which of these parameters is most important? -> Little gain in detailed modelling if unconstrained parameters are biggest unknown.

-> Pick most important parameter -> estimated by Data Assimilation.





Limitations of modeling (2) Pitch angle diffusion in realistic fields – drift shell splitting





Limitations of modeling (2)Pitch angle diffusion in realistic fields – drift shell splitting

Current diffusion modeling is performed in a drift averaged sense with all wave processes acting over an assumed fraction of the drift orbit.

REAL particle distributions have a geometric background variation of the pitch angle shape due to the asymmetric magnetic field.

The REAL pitch angle distribution at any point is always a convolution of both radial and pitch angle gradients.

In the magnetosphere, pitch angle diffusion acts on the LOCAL slope of the pitch angle distribution. Redistribution by diffusion depends on diffusion coefficients AND local gradients.

Q: How large an error is made by ignoring these effects?





Limitations of modeling (3)Radial diffusion

This is the oldest and best established part of the electron radiation belt dynamics. A large part of the dynamics (~80%) can be recovered using diffusion alone.

However, radial diffusion coefficients have been derived based on simple assumptions in the past (dipole fields, fluctuations of electric and magnetic field) [Schultz&Lanzerotti)

Other derivations are based on data (which mixes radial, pitch angle and energy diffusion).

Depending on the study different parameterizations of radial diffusion have been proposed – both as a function of L and activity (Kp).

Q: Which one is the best? How can one tell? Is a broad Kp dependence sufficient? -> estimate activity dependence with Data Assimilation?





Limitations of modeling (4)Loss processes

This is still the part of electron radiation belt dynamics that is least understood.

Many mechanisms have been proposed and with the appropriate assumptions, may individually be blamed for all the losses.

In reality, a combination of all the mechanisms may act together, with varying combinations depending on energy, activity level and location.

Observationally these mechanisms are difficult to constrain.

Q: Is there a strategy that can differentiate between these processes? And if not, can a simple parameterization of losses in terms of energy and location dependent lifetimes be sufficient? -> estimate these lifetimes with Data Assimilation?





The Path Forward (1)Data Assimilation prospects

Once the best reduced set of controlling parameters has been found, they can be added to the model state and can be estimated using an Ensemble Kalman Filter.

Results from an identical twin test case show how this works in principle:

In Red is the actually used variation of the radial diffusion parameter – Blue the recovered variation when treated as a free parameter and estimated using an Ensemble Kalman filter.





The Path Forward (2)LANL DREAM effort - Four integrated lines of modeling

Radiation BeltDiffusion Model

New AssimilationTechniques

Storm-TimeMagnetic Field Model

Phase Space DensityMatching

Self ConsistentRing Current Model

MagnetosphericGlobal Dynamics

DynamicRadiation EnvironmentAssimilationModel

Satellite ObservationsIntercalibration, etc.

EmpiricalSpecification

New StatisticalEmpirical Models





The path forward (3): The full DREAM coupling plan





Summary / Conclusion (1)• Fascinating problem: Modeling Relativistic electrons, although only

passive “riders” on magnetospheric activity, requires a full self-consistent treatment of plasmasphere, ring current, magnetic and electric fields, wave generation and wave-particle interaction.

• The times of data only and modeling only work are over.

• To improve our modeling capability in representing a real electron radiation belt, further detailed modeling may be less effective compared to better specification of some of the controlling parameters.

• A reduced parametric specification may actually be better than modeling the full physics with wrong assumptions.





Summary / Conclusion (2)• The upcoming RBSP mission is designed to provide the best data

possible for the electron radiation belt problem. Still:– Only two point measurements– Limited and varying time resolution at each L.

• Between now and RBSP launch, what can be done to fill the remaining gaps in needed inputs for successful modeling?

• Focus on ground based data – a much under-utilized resource– Whistler data is plentiful and on-going and can give equatorial plasma density and

plasmapause position.– ULF wave data from ground magnetometers can infer mass loading of field lines.

• DMSP data has also been used to infer plasmapause position (Anderson).

once upon a time in the electron radiation belts

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