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Cosmic Rays and Space Weather
Lev I. Dorman (1, 2) (1) Israel Cosmic Ray and Space Weather Center and Emilio Segre’ Observatory affiliated to Tel Aviv University, Technion and Israel Space Agency, Israel,
(2) Cosmic Ray Department of IZMIRAN, Russian Academy of Science, Russia
Contact: ([email protected] / Fax: 972-4-6964952/Tel: 972-4-6964932)
1. Cosmic rays (CR) as element of space weather
• 1.1. Influence of CR on the Earth’s atmosphere and global climate change1.2. Radiation hazard from galactic CR 1.3. Radiation hazard from solar CR 1.4. Radiation hazard from energetic particle precipitation from radiation belts
2. CR as tool for space weather forecasting
• 2.1. Forecasting of the part of global climate change caused by CR intensity variations
• 2.2. Forecasting of radiation hazard for aircrafts and spacecrafts caused by variations of galactic CR intensity
• 2.3. Forecasting of the radiation hazard from solar CR events by using on-line one-min ground neutron monitors network and satellite data
• 2.4. Forecasting of great magnetic storms hazard by using on-line one hour CR intensity data from ground based world-wide network of neutron monitors and muon telescopes
• 3. CR, space weather, and satellite anomalies
• 4. CR, space weather, and people health
ISRAEL CR & SPACE WEATHER CENTER
Data Analysis
• Search of flare beginning in cosmic rays (automatic SEP detection)
• Restoration of particles impact (F(t,E))
• Prediction of magnetic storms from CR-network data
Monitoring and Forecast of Solar Flare Particle Events Using Cosmic-Ray Neutron Monitor
and Satellite 1-min Data
FORECAST STEPS1. AUTOMATICALLY DETERMINATION OF THE SEP EVENT
START BY NEUTRON MONITOR DATA
2. DETERMINATION OF ENERGY SPECTRUM OUT OF MAGNETOSPHERE BY THE METHOD OF COUPLING FUNCTIONS
3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION
4. FORECASTING OF EXPECTED SEP FLUXES AND COMPARISON WITH OBSERVATIONS
5. COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA
1. AUTOMATICALLY DETERMINATION OF THE FEP EVENT START BY NEUTRON MONITOR DATA
160
1201 60lnln
Zk
ZkAkAZZA IID
160
1201 60lnln
Zk
ZkBkBZZB IID
DZA1 2.5, DZB1 2.5,
THE PROBABILITY OF FALSE ALARMS
THE PROBABILITY OF MISSED TRIGGERS
2. DETERMINATION OF ENERGY SPECTRUM OUT OF MAGNETOSPHERE BY THE METHOD OF COUPLING
FUNCTIONS
mmm km
kcm
kmmcm RaRaRkaRRW exp1,
11 , if cRR,
and 0,RRWcm , if cRR
bRRDRD o
dRRaRRakaRFc
mmm
R
km
kkcmmmcm
expexp1, 11
,,,,
,,,,,
cmccncnccm
clccmcmcclclmn RFRRWRFRRW
RFRRWRFRRWR
cmocmcm RIRIRI
,, ckcckcck RbFRRWRRI
2. DETERMINATION OF ENERGY SPECTRUM OUT OF MAGNETOSPHERE BY THE METHOD OF COUPLING
FUNCTIONS
cmcncncm
clcmcmclc RIRFRIRF
RIRFRIRFR
,,
,,
,,,,
,,
clccmcmccl
clccmcmccl
RFRRWRFRRW
RIRRWRIRRWb
cmccncnccm
clccmcmcclclmn RIRRWRIRRW
RIRRWRIRRWR
,,
,,,
3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION
xTTtxTTtxTTt e 13312211 ,,
R TTxTTxTb
Tb
r
RK
xTTx
TT2123
122
12
112
12 ln4
R TTxTTxTb
Tb
r
RK
xTTx
TT3123
133
12
113
13 ln4
11312 TTTTx
R TTxTTxTb
Tb
R TTxTTxTb
Tb
TT
TT
132313
3
1
122312
2
1
12
13
ln
ln
3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION
R TTxTTxTb
Tb
xTTxTTr
R TTxTTxTb
Tb
xTTxTTrRK
132313
3
1
13132
1
122312
2
1
12122
1
ln
4
ln
4
3
213332
212
2212
1111
4/exp2/32/124/exp2/3
2/124/exp2/32/12
tRKrtRKRDR ttbtRKrtRK
RDR ttbtRKrtRKRDR ttbRN
o
ooo
eeo TTRK
rTTRKRNTrRn4
2exp232
1,, 21
3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION
The behavior of RK for R 10 GV with time
3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION
tRK
rrtRKrRNtrRn o
12
21
24
231
231
2exp
232,,
11, rrRKrRK 321 ,, nnn 321 ,, ttt
1
31132
1232113
132
12312 lnlnlnln32
nnttt
tttnntt
ttt
ttttt
31
213
13
11
21
212
12
12
11
21
1ln2ln23ln2ln23 nntt
ttr
nntt
ttrRK
kko
tRK
rtRKrnRN
12
2123
123
124
12
exp232
3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION
4.1 FORECASTING OF EXPECTED FEP FLUXES AND COMPARISON WITH OBSERVATIONS (2-nd CASE: K(R, r) DEPENDS FROM DISTANCE TO THE SUN)
5.1 COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA
11, rrRKrRK
111 RRcvKRK
dRdR
RKTT
rRKTTrRNdTTRF
e
e
TRo
Tcs
ce
1
21
2
24
231
231 2
exp232
RKTT
rrRKTTrRNTrRn
e
eo
12
21
24
231
231
2exp
232,,
maxln, kko EEaeoo RTTRNTRN
5B. COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA; COMPARISON WITH GOES OBSERVATIONS
CONCLUSION FOR SEP
BY ONE-MINUTE NEUTRON MONITOR DATA AND ONE-MINUTE AVAILABLE FROM INTERNET COSMIC RAY SATELLITE DATA FOR 20-30 MINDATA IT IS POSSIBLE TO DETERMINE THE TIME OF EJECTION, SOURCE FUNCTION, AND DIFFUSIONCOEFFICIENT IN DEPENDENCE FROM ENERGY ANDDISTANCE FROM THE SUN. THEN IT IS POSSIBLE TO FORECAST OF SEP FLUXES AND FLUENCY IN HIGH AND LOW ENERGY RANGES UP TO ABOUT TWO DAYS. SEPTEMBER 1989 EVENT IS USED AS A TEST CASE.
The relation between malfunctions of satellites at different orbits and
space weather factors
,
Red, Green and Blue Groups
Period with big number of satellite malfunctions
Upper panel – cosmic ray activity near the Earth: variations of 10 GV cosmic ray density; solar proton (> 10 MeV and >60 MeV) fluxes.
Lower panel – geomagnetic activity: Kp- and Dst-indices.
Vertical arrows on the upper panel correspond to the malfunction moments.
Period with big number of satellite malfunctions
• Upper panel – cosmic ray activity near the Earth: variations of 10 GV cosmic ray density; electron (> 2 MeV) fluxes – hourly data.
• Vertical arrows correspond to the malfunction moments. Lower row – all malfunctions.
• Lower panel – geomagnetic activity: Kp- and Dst-indices.
High- and low altitude anomalies
No correlation between high and lowmalfunction’s frequencies
Seasonal dependence
Anomaly’s frequency (all orbits)with statistical errors
27-day averaged frequencies and correspondinghalf year wave
Seasonal dependence
Satellite malfunction frequency and Ap-index averaged over the period 1975-1994. The curve with points is the 27-day running mean values; the grey band corresponds to the 95 % confidence interval. The sinusoidal curve is a semidiurnal wave with maxima in equinoxes best fitting the frequency data.
Seasonal dependence (different orbits)
27-day averaged frequencies and correspondinghalf year wave for different satellite groups
Time distribution of anomalies
Space Weather Indices
• Solar activity
• Solar wind
• Geomagnetic activity
• Solar protons
• Electrons
• Ground Level Cosmic Rays
~30 indices in total
Solar activity
27-day running averaged Sunspot Numbers and Solar Radio Flux
We use
SSN and F10.7 – daily Sunspot Numbers and radio fluxes;
SSN27, SSN365 – 1 year and 1 rotation running averaged SSN
Geomagnetic activity
Daily Ap-index and minimal (for this day) Dst-index
We use
Apd, Apmax – daily and maximal Ap-index;
AEd, AEmax – daily and maximal AE-index; DSTd, DSTmin – daily and minimal Dst-index;
Energetic protons and electrons
Daily proton and electron fluencies
p10, p100 – daily proton (>10, >100 MeV) fluencies (GOES);
p10d, p60d – daily proton (>10, >60 MeV) fluxes (IMP);
p10max, p60max – maximal hourly proton (>10, >60 MeV) fluxes (IMP); e2 – daily electron (>2 MeV) fluence (GOES); e2d, e2max – daily and maximal electron (>2 MeV) fluх (GOES);
Solar Wind
Daily solar wind speed and intensity of interplanetary magnetic field
Vsw, Vmax – daily and maximal solar wind speed;
Bm – daily IMF intensity; Bzd, Bzmin – daily and minimal z-component IMF (GSM); Bznsum – sum of negative z-component values;
Cosmic Ray Activity Indices
Daily CRA-indices and sum of negative IMF z-component
da10, CRA – indices of cosmic ray activity, obtained from
ground level CR observations (Belov et al., 1999); Eakd, Eakmax – estimation of daily and maximal energy, transferred
from solar wind to magnetosphere (Akasofu, 1987);
SSC and anomalies
• Averaged behavior of satellite malfunction frequency near Sudden Storm Commencements
• 634 days with SSC in total
• a – all storms• b – storms with Ap>50 nT• c – storms with Ap>80 nT
SSC and anomalies• Averaged behavior
Ap, Dst – indices of geomagnetic activity and satellite malfunction frequency near Sudden Storm Commencements
• Malfunctions start later and last longer than magnetic storms
Proton events and anomaliesAveraged behavior of p>10, p>100 MeV and satellite malfunction frequency during proton event periods.The enhancement with >300 pfu were used
Proton events and anomalies
Mean satellite anomaly frequencies in 0- and 1-days of proton enhancements
in dependence on the maximal > 10 MeV flux
Proton events and anomalies
Probability of any anomaly (high altitude – high inclination group) in dependence on the maximal proton > 10 and >60 MeV flux
Proton and electron hazardson the different orbits
Mean proton and electron fluencies on the anomaly day
Anomalies and different indices(precursors)
Mean behavior of Ap-index in anomaly periods (GEO satellites)
Anomalies and different indices(precursors)
Mean behavior of >2 MeV electron fluence in anomaly periods (GEO satellites)
Anomalies and different indices(precursors)
Mean behavior of solar wind speed in anomaly periods (GEO satellites)
Models of the anomaly frequencyhigh alt.- low incl.
e>2 MeV• Apd, AEd, sf• p60d, p100
Vsw• Bzd, da10
low alt.-high incl.
e>2 MeVCRAApd, AEd, sfVsw, Bzd
high alt.-high incl.
p>100 MeV, p60d Eak, Bznsum, SSN365
Models of the anomaly frequency
Example of frequency model (GEO):
We checked ~ 30 different Space Weather parameters and a lot of their combinations
We used the parameters for anomaly day and for several preceding days
Only simplest linear regression models were checked (exclusions for e and p indices)
Obtained models contain 3-8 different geo- heliophysical parameters
The models appear to be different for different satellite groups
da10105.1sf102)d60p(106.1)100p(101.1 4375.0435.04
Summary on satellite anomalies
• The models simulated anomaly frequency in different orbits are developed and could be adjusted for forecasting
• The relation between Space Weather parameters and frequency of satellite malfunctions are different for different satellite groups (orbits)
THE END
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