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,

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)

mailto:[email protected]

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

,,

,,,


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


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


The behavior of RK for R 10 GV with time


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


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)


Mean behavior of >2 MeV electron fluence in anomaly periods (GEO satellites)


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

Thank You

Documents

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,