1 development of a statistical dynamic radiation belt model [email protected] center for...
TRANSCRIPT
1
Development of a Statistical Dynamic Development of a Statistical Dynamic Radiation Belt ModelRadiation Belt Model
Center for Space Radiations (CSR)
UCL, Louvain-La-Neuve, Belgium
S. Benck, L. Mazzino, V. Pierrard, S. Benck, L. Mazzino, V. Pierrard, M. Cyamukungu, J. CabreraM. Cyamukungu, J. Cabrera
ESWW5, Brussels, Belgium, 17-21 November 2008
2
McIlwain in 1966 identifies 5 « Processes acting upon outer zone electrons »
Process 1: Rapid non adiabatic accelerationProcess 2: Persistent decayProcess 3: Radial DiffusionProcess 4: Adiabatic AccelerationProcess 5: Rapid Loss
(From McIlwain, 1996 AGU)
Measured radiation dose (black) compared to the static model prediction (red) based on flux averages (see Glossy brochure of the SREM from Contraves-PSI)
Steady state GS pobabilities Flux variations ConclusionDecay timesIntroduction
3
st = Steady state flux measured
n = The expected flux variation following a storm (average)
res = Diff. between. flux before storm and steady state flux
T = Decay time of flux for a given position and energy
= Time elapsed from the storm min. Dst_prev (or drop-
out min) to 0 (Maximum flux)
N = Number of bins in Dst range
S = Solar par. that indicates phase within the solar cycle
Type = Type of storm: CME, CIR, Mix
F(Dst_prev) = Flux var. induced by prev. storm of min Dstprev
Dst prev = The min. value reached by Dst in the prev. storm
.
.
.
)_(0 prevDSTresst F 1
/01 )( T
stst e
2/
12 )( Tstst e
nTstnstn e
/
1 )(
)(0
)(),,,(
N
k
DststypeDstnDstn kprevk FP L. Mazzino, et al (2008)
T
flux = steady state background + geomagnetic activity dependent value
Steady state GS pobabilities Flux variations ConclusionDecay timesIntroduction
4
Introduction GS pobabilities Flux variations ConclusionDecay times
~100 days
st
nTstnstn e
/
1 )(
Steady state
5
st as a function of L (B>0.3 G)
st as a function of longitude, latitude and altitude
Introduction GS pobabilities Flux variations ConclusionDecay timesSteady state
6
Black dots correspond to Dst minimum of the GS. The number of storms is outlined for each of the solar cycles (orange). Solar maximum and minimum activity are delineated (green and blue lines respectively, dates indicated), corresponding to solar cycles 19 (incomplete), 20, 21, 22 and 23. Sunspot number is plotted on the black curve with superimposed smoothed curve in red.
Introduction Steady state Flux variations ConclusionDecay times
)(0
)(),,,(
N
k
DststypeDstnDstn kprevk FP
GS probabilities
50 years of Dst and sunspot number data, including ~1200 storms have been analyzed
7
Probability of having a GS of a given Dstk after a previous GS of any magnitude, for the declining phase .
Probability of two successive GS with a given time interval, for the declining phase of the different solar cycles Poisson distribution
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
Nice agreement with: Tsubouchi and Omura, Long-term occurrence probabilities of intense geomagnetic storm events, Space Weather, 2007
8(Picture: Courtesy of CNES)
http://smsc.cnes.fr/DEMETER/(Picture: Courtesy of CONAE)
http://www.conae.gov.ar/sac-c/
DEMETER/IDP SAC-C/ICARE
Electron fluxes data: Two LEO Satellites, Ee= 200 keV – 1.2 MeV
Orbit at 710 km 98.23 deg. Incl.
Orbit at 702 km 98.2 deg. Incl.
Introduction Steady state GS pobabilities ConclusionDecay times
nTstnstn e
/
1 )(
)(0
)(),,,(
N
k
DststypeDstnDstn kprevk FP
F and as a function of GS type
Flux variations
9
Flux enhancement (F) and Time interval between storm and flux max ()
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
10
TYPE 1 (mainly CME)
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
TYPE 2 (mainly CIR)
Kataoka and Miyoshi, “Flux enhancement of radiation belt electrons during geomagnetic storms driven by coronal mass ejections and corotating interaction regions.” Space weather, 2006
Storm type definition
short Bz/t, ’’peak’’, t ~3-4h long Bz/t, ’’inconsistent’’, t >7h
11
The time interval between magnetic storm and flux maximum () seems to be linear for the classified isolated storms, but random for all other storms. Need more parameters
TYPE 1(yellow), TYPE 2 (red)
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
12
Resultant flux enhancement F as a function of storm severity, corresponding to isolated TYPE 1 (yellow), TYPE 2 (red), and mixed non isolated storms (blue)
Introduction RABEM Model Dat Dat and parameters and parameters Results SummaryIntroduction Steady state GS pobabilities Flux variations ConclusionDecay times
13
L-parameter
slop
e
TYPE 1sl
ope
L-parameter
TYPE 2
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
At low L the flux enhancement increases steeper with Dstmin (slope > 0) for lower energies
At high L the flux enhancement decreases steeper with Dstmin (slope < 0) for lower energies.
For all L values, the flux enhancement increases steeper with Dstmin (slope > 0) for lower energies
For all energies the slope decreases with L
14
Decay time constant (loss timescales) of electron fluxes (T)
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
Condition of measurement: The time resolution is 12 h The maximum flux after storm must
occur 3 days before the defined end of the storm
DEMETER/IDP – SACC/ICARE comparison
15
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
A pattern that is often observed during individual storms: At low L, the decay time decreases with increasing energy, while at high L this pattern is inversed.
Meredith et al, “Energetic outer zone electron loss timescales during low geomagnetic activity.” JGR (2006)
3<L<5, T(Ehigh) > T (Elow), for <15°
Decay time of electron fluxes (T) as a function of position and energy
Lyons et al, “Pitch-angle diffusion of radiation belt electrons within the plasmasphere.” JGR (1972) T=min at around L =3Re (theory)
16
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
Meredith et al, “Evidence for acceleration of outer zone electrons to relativistic energies by whistler mode chorus.” Annales Geophysicae (2002)
(Benck et al, Study of correlations between waves and particle fluxes measured on board the DEMETER satellite, Advances in Space research (2008)
Cases where the electron flux increases continuously
(wave activity) ?
17
Identified Parameters
Steady statest
Storm occurence and related probabilities
Dstprev, Dstk (1224 storms!)
t (time interval between two storms)
Solar Cycle parameter (SSN)
Flux variations during storm time
Type of storm (presently 2 types)
(elapsed time between storm max (Dstmin) and maximum flux)
Maximum Flux and Flux enhancement F
T (Decay time)
Solar parameters data: Courtesy of GSFC Space Physics Data Facility http://omniweb.gsfc.nasa.gov/index.html
SAMPEX DATA: Courtesy of SAMPEX Data Center http://www.srl.caltech.edu/sampex/DataCenter/ SAC-C Data: Courtesy of CNES/DCT/AQ/EC Section, ONERA/DESP and CONAE Sunspot Number Data: Courtesy of Solar Influences Data Analysis Center – SIDC, Belgium
Introduction Steady state GS pobabilities Flux variations Decay times Conclusion
L. Mazzino et al, Development of a statistical dynamic radiation belt model: Analysis of storm time particle flux variations, ESA Ionizing Radiation Detection and Data Exploitation Workshop proceedings, 2008
18
19
Example of geomagnetic storm
Storm Sudden commencement
Main phase
Strength of storm:Minimum Dst reached
Recovery phase
Introduction RABEM Model Data and parameters Results Summary
20
steady state background i.e. mapping of quiet time fluxes
Statistical dynamic radiation belt model
Geomagnetic storm (GS) prediction (Dst<-50 nT) - Occurrence probability
Flux variation associated to GS, as a function of energy, position and type of storm
Flux decay time as a function of energy, position, ...
+
Steady state GS pobabilities Flux variations ConclusionDecay timesIntroduction
L. Mazzino et al, Development of a statistical dynamic radiation belt model: Analysis of storm time particle flux variations, ESA Ionizing Radiation Detection and Data Exploitation Workshop proceedings, 2008
21
Dst data (black) with filtered data (red): The second graph shows the filter detail, and the fourth shows a closed up of the event, with actual amplitude of the storm in green.
Butterworth filter:z = cutoff frequency n
z
filter *211
(Dst Data: Courtesy of World Data Center for Geomagnetism, Kyoto)
Dst
Introduction RABEM Model Data and parameters Results Summary Additional
22
Correlation between number of storms per month for different phases in a solar cycle with the Sunspot Maximum corresponding to that cycle.
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
Sunspot Number Maxima
(smoothed)
Solar cycle #20: 109
Solar Cycle #21: 159
Solar Cycle #22: 157
Solar Cycle #23: 121
23
Histogram of Dstk vs. Dstprev
number of bins = 100
Introducton RABEM Model Data and parameters Results Summary
Probability of having a storm with intensity Dstk considering that the previous one was of intensity Dstprev
All Dstmin given in absolute value
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
24
Introduction RABEM Model Data and parameters Result Summary
For few events the time interval between storms is greater than 100 days, and the time interval between those storms can be used to find the steady state.
All Dstmin given in absolute value
Histogram of Dstk vs. time interval, number of
bins = 100
Nice agreement with: Tsubouchi and Omura, Long-term occurrence probabilities of intense geomagnetic storm events, Space Weather, 2007
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
Probability of having a storm with intensity Dstk considering a given time interval elapsed since the previous storm
25
Sunspot number maximum is a good parameter to represent solar cycle activity vs. total number of storms.
The total number of storms per month in a cycle correlates directly to the severity of the solar cycle: For solar cycles with higher SSN maxima, SC 21 and SC 22,the total number of storms is higher than for SC 20 and SC 23 with lower maxima
Solar Parameter (S): Sun Spot Number
Sunspot Number Maxima
(smoothed)
Solar cycle #20: 109
Solar Cycle #21: 159
Solar Cycle #22: 157
Solar Cycle #23: 121
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
26
Histogram of smoothed Sunspot number vs. Time interval between storms (number of bins = 25 time resolution = 10 days)
The distribution of time interval between storms for all 1204 storms in the last 50 years seems to be Poisson-distributed.
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
Probability of having a certain time interval between storms considering the sunspot number
2727
Difference of time interval distribution function depending on phase and severity of solar cycle activity
Introduction RABEM Model Data and parameters Results Summary Additional
28
DEMETER Fluxes
Introduction RABEM Model Data and parameters Results Summary Additional
Geomagnetic storm: particle flux enhancement
29
(SAC-C Data: Courtesy of CNES/DCT/AQ/EC Section, ONERA/DESP and CONAE)
Introduction RABEM Model Data and parameters Results Summary Additional
30
FLUX ENHANCEMENT DUE TO GEOMAGNETIC STOMS
SAC-C
Introduction RABEM Model Data Data and parameters and parameters Results Summary
31
Resultant flux enhancement difference as a function of storm severity, corresponding to
isolated CME’s (yellow), CIR’s (red), and mixed non isolated storms (blue)
Resultant maximum flux as a function of storm severity, corresponding to isolated CME’s
(yellow), CIR’s (red), and mixed non isolated storms (blue)
Introduction RABEM Model Data and parameters Results Summary Additional
Results: Fluxes
32
Introduction RABEM Model Data and parameters Results Summary Additional
(SAC-C Data: Courtesy of CNES/DCT/AQ/EC Section, ONERA/DESP and CONAE)
TYPE 1(yellow), TYPE 2 (red)
3333
Introduction RABEM Model Data and parameters Results Summary Additional
TYPE 2
34
Introduction Steady state GS pobabilities Flux variations ConclusionDecay times
Decay time of electron fluxes (T) independent of Dst
35
• In a dipole:
We need a reference invariable with time
Hess (1968)
McIlwain (1961-1966)
• Magnetic Coordinates:
Illustration from: http://en.wikipedia.org/wiki/L-shell
36
CIR’s: Corotating Interaction Regions
Hundhausen, 1972Akasofu and Hakamada, 1983
MHD simulation of (1) high speed streams which cause the development of CIR structure and (2) the propagation of
transient shocks which also modify the CIR structure (bottom two panels particularly)
Schematic illustration of a fast streaminteracting with a slow stream
37
CME’s: Coronal Mass Ejection
Space Weather Laboratory, George Madison University
Schematic of a coronal mass ejection in the form of a magnetic cloud with a shock.
Cravens, 1997