Cosmic Rays and Space Weather

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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 (lid_at_physics.technion.ac.il / Fax
  972-4-6964952/Tel [phone])

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1. Cosmic rays (CR) as element of space weather

• 1.1. Influence of CR on the Earths 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

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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

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• 3. CR, space weather, and satellite anomalies
• 4. CR, space weather, and people health

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ISRAEL CR SPACE WEATHER CENTERData 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

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Monitoring and Forecast of Solar Flare Particle
Events Using Cosmic-Ray Neutron Monitor and
Satellite 1-min Data
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FORECAST STEPS
1. 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
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1. AUTOMATICALLY DETERMINATION OF THE FEP EVENT
START BY NEUTRON MONITOR DATA
THE PROBABILITY OF FALSE ALARMS
THE PROBABILITY OF MISSED TRIGGERS
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2. DETERMINATION OF ENERGY SPECTRUM OUT OF
MAGNETOSPHERE BY THE METHOD OF COUPLING FUNCTIONS
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2. DETERMINATION OF ENERGY SPECTRUM OUT OF
MAGNETOSPHERE BY THE METHOD OF COUPLING FUNCTIONS
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3. DETERMINATION OF TIME OF EJECTION, SOURCE
FUNCTION AND PARAMETERS OF PROPAGATION
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3. DETERMINATION OF TIME OF EJECTION, SOURCE
FUNCTION AND PARAMETERS OF PROPAGATION
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3. DETERMINATION OF TIME OF EJECTION, SOURCE
FUNCTION AND PARAMETERS OF PROPAGATION
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3. DETERMINATION OF TIME OF EJECTION, SOURCE
FUNCTION AND PARAMETERS OF PROPAGATION
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3. DETERMINATION OF TIME OF EJECTION, SOURCE
FUNCTION AND PARAMETERS OF PROPAGATION
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4.1 FORECASTING OF EXPECTED FEP FLUXES AND
COMPARISON WITH OBSERVATIONS (2-nd CASE K(R, r)
DEPENDS FROM DISTANCE TO THE SUN)
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5.1 COMBINED FORECASTING ON THE BASIS OF NM DATA
AND BEGINNING OF SATELLITE DATA
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5B. COMBINED FORECASTING ON THE BASIS OF NM DATA
AND BEGINNING OF SATELLITE DATA COMPARISON WITH
GOES OBSERVATIONS
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CONCLUSION FOR SEP
BY ONE-MINUTE NEUTRON MONITOR DATA AND
ONE-MINUTE AVAILABLE FROM INTERNET COSMIC RAY
SATELLITE DATA FOR 20-30 MIN DATA IT IS POSSIBLE
TO DETERMINE THE TIME OF EJECTION, SOURCE
FUNCTION, AND DIFFUSION COEFFICIENT IN DEPENDENCE
FROM ENERGY AND DISTANCE 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.
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The relation between malfunctions of satellites
at different orbits and space weather factors

• ,

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Red, Green and Blue Groups
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Period with big number of satellite malfunctions

• Upper panel cosmic ray activity near the Earth
  variations of 10 GV cosmic ray density solar
  proton (gt 10 MeV and gt60 MeV) fluxes.
• Lower panel geomagnetic activity Kp- and
  Dst-indices.
• Vertical arrows on the upper panel correspond to
  the malfunction moments.

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Period with big number of satellite malfunctions

• Upper panel cosmic ray activity near the Earth
  variations of 10 GV cosmic ray density electron
  (gt 2 MeV) fluxes hourly data.
• Vertical arrows correspond to the malfunction
  moments. Lower row all malfunctions.
• Lower panel geomagnetic activity Kp- and
  Dst-indices.

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High- and low altitude anomalies
No correlation between high and
lowmalfunctions frequencies
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Seasonal dependence
Anomalys frequency (all orbits)with
statistical errors
27-day averaged frequencies and
correspondinghalf year wave
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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.
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Seasonal dependence (different orbits)
27-day averaged frequencies and
correspondinghalf year wave for different
satellite groups
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Time distribution of anomalies
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Space Weather Indices

• Solar activity
• Solar wind
• Geomagnetic activity
• Solar protons
• Electrons
• Ground Level Cosmic Rays
• 30 indices in total

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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
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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
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Energetic protons and electrons
Daily proton and electron fluencies
p10, p100 daily proton (gt10, gt100 MeV)
fluencies (GOES) p10d, p60d daily proton
(gt10, gt60 MeV) fluxes (IMP) p10max, p60max
maximal hourly proton (gt10, gt60 MeV) fluxes
(IMP) e2 daily electron (gt2 MeV) fluence
(GOES) e2d, e2max daily and maximal
electron (gt2 MeV) flu? (GOES)
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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
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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)
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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 Apgt50 nT
• c storms with Apgt80 nT

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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

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Proton events and anomalies

• Averaged behavior of pgt10, pgt100 MeV and
  satellite malfunction frequency during proton
  event periods.
• The enhancement with gt300 pfu were used

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Proton events and anomalies
Mean satellite anomaly frequencies in 0- and
1-days of proton enhancementsin dependence on
the maximal gt 10 MeV flux
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Proton events and anomalies
Probability of any anomaly (high altitude high
inclination group) in dependence on the maximal
proton gt 10 and gt60 MeV flux
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Proton and electron hazardson the different
orbits
Mean proton and electron fluencies on the anomaly
day
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Anomalies and different indices(precursors)
Mean behavior of Ap-index in anomaly periods
(GEO satellites)
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Anomalies and different indices(precursors)
Mean behavior of gt2 MeV electron fluence in
anomaly periods (GEO satellites)
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Anomalies and different indices(precursors)
Mean behavior of solar wind speed in anomaly
periods (GEO satellites)
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Models of the anomaly frequency

• high alt.- low incl.
• egt2 MeV
• Apd, AEd, sf
• p60d, p100 Vsw
• Bzd, da10

low alt.-high incl. egt2 MeV CRA Apd, AEd, sf Vsw,
Bzd
high alt.-high incl. pgt100 MeV, p60d Eak,
Bznsum, SSN365
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Models of the anomaly frequency

• 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

Example of frequency model (GEO)
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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)

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THE END

• Thank You