20 apr 2011 mgy satellite anomalies and space weather
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20 Apr 2011 mgy Satellite Anomalies and Space Weather. Lev Dorman (1, 2) for the Team (A. Belov, I. Ben Israel, U. Dai, L. Dorman,, E. Eroshenko, N. Iucci, Z. Kaplan, O . Kryakunova , A. Levitin, M . Parisi, N . Ptitsyna, L. Pustil’nik,

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20 apr 2011 mgy satellite anomalies and space weather

20Apr 2011 mgySatellite Anomalies and Space Weather

Lev Dorman (1, 2) for the Team

(A. Belov, I. Ben Israel, U. Dai, L. Dorman,, E. Eroshenko, N. Iucci,

Z. Kaplan, O. Kryakunova , A. Levitin, M. Parisi,N. Ptitsyna, L. Pustil’nik,

A. Sternlieb, M. Tyasto, E. Vernova, G. Villoresi, V. Yanke, I. Zukerman)

1. Israel Cosmic Ray and Space Weather Center, affiliated to Tel Aviv University, Golan Research Institute, and Israel Space Agency

2. Cosmic Ray Department of IZMIRAN, Russian Academy of Sciences

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Abstract
Abstract

Results of the Project, which aims to improve the methods of safeguarding satellites in the Earth’s magnetosphere from the negative effects of the space environment, are presented. Anomaly data from the “Kosmos” series satellites in the period 1971–1999 are combined in one database, together with similar information on other spacecrafts. This database contains, beyond the anomaly information, various characteristics of the space weather: geomagnetic activity indices (Ap, AE and Dst), fluxes and fluencies of electrons and protons at different energies, high energy cosmic ray variations and other solar, interplanetary and solar wind data. A comparative analysis of the distribution of each of these parameters relative to satellite anomalies was carried out for the total number of anomalies (about 6000 events), and separately for high (5000 events) and low (about 800 events) altitude orbit satellites. No relation was found between low and high altitude satellite anomalies. Daily numbers of satellite anomalies, averaged by a superposed epoch method around sudden storm commencements and proton event onsets for high (>1500 km) and low (<1500 km) altitude orbits revealed a big difference in a behavior. Satellites were divided on several groups according to the orbital characteristics (altitude and inclination).

The relation of satellite anomalies to the environmental parameters was found to be different for various orbits that should be taken into account under developing of the anomaly frequency models.

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Satellite anomaly data
Satellite anomaly data

The main contribution was from NGDC satellite anomaly database, created by J. Allen and D. Wilkinson.

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“Kosmos” data (circular orbit at 800 km altitude and 74º inclination)

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1994 year anomalies - Walter Thomas report (Thomas, 1995).

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The satellites characteristics - from different Internet sources:

http://spacescience.nasa.gov/missions/index.htm

http://www.skyrocket.de/space/index2.htm

http://hea-www.harvard.edu/QEDT/jcm/space/jsr/jsr.html

http://www.astronautix.com/index.htm

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Satellite and anomaly number
Satellite and Anomaly Number

~300 satellites~6000 satellite anomalies

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Red green and blue groups
Red, GreenandBlueGroups

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Period with big number of satellite anomalies
Period with big number of satellite anomalies

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.

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Other example
Other example

  • 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.

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Seasonal dependence
Seasonal dependence

Anomaly’s frequency (all orbits) with statistical errors

27-day averaged frequencies and correspondinghalf year wave

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Seasonal dependence1
Seasonal dependence

Satellite anomalies 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 semiannual wave with maxima in equinoxes best fitting the frequency data.

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Seasonal dependence different orbits
Seasonal dependence (different orbits)

27-day averaged frequencies and correspondinghalf year wave for different satellite groups

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Time distribution of anomalies
Time distribution of anomalies

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Space weather indices
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
Solar activity

27-day running averaged Sunspot Numbers and Solar Radio Flux

We useSSN 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
Geomagnetic activity

Daily Ap-index and minimal (for this day) Dst-index

We useApd, Apmax– daily and maximal Ap-index;

AEd, AEmax – daily and maximal AE-index;

DSTd, DSTmin – daily and minimal Dst-index;

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Protons and electrons
Protons and electrons

Daily proton and electron fluencies

We usep10, 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);

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Solar wind
Solar Wind

Daily solar wind speed and intensity of interplanetary magnetic field

We useVsw, 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
Cosmic Ray Activity Indices +

Daily CRA-indices and sum of negative IMF z-component

We useda10, 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
SSC and anomalies

  • Averaged behavior of satellite anomalies 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

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Ssc and anomalies1
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
Proton events and anomalies

  • Averaged behavior of p>10, p>100 MeV and satellite malfunction frequency during proton event periods.

  • The enhancement with >300 pfu were used

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Proton events and anomalies1
Proton events and anomalies

Mean satellite anomaly frequencies in 0- and 1-days of proton enhancementsin dependence on the maximal > 10 MeV flux

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Proton events and anomalies2
Proton events and anomalies

Probability of any anomaly (highaltitude – highinclination group) in dependence on the maximal proton > 10 and >60 MeV flux

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Proton and electron hazards on the different orbits
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
Anomalies and different indices (precursors)

Mean behavior of Ap-index in anomaly periods (GEO satellites)

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Anomalies and different indices precursors1
Anomalies and different indices (precursors)

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

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Anomalies and different indices precursors2
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
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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Models of the anomaly frequency1
Models of the anomaly frequency

low alt.-high incl.

cc=0.2

e>2 MeV

CRA

Apd, AEd, sf

Vsw, Bzd

high alt.- low incl.

cc=0.39

  • e>2 MeV

  • Apd, AEd, sf

  • p60d, p100 Vsw

  • Bzd, da10

high alt.-high incl.cc=0.7

p>100 MeV, p60d

Eak, Bznsum, SSN365

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Sep forecast steps
SEP 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 BY SOLVING AN INVERSE PROBLEM

  • 4. FORECASTING OF EXPECTED SEP FLUXES AND COMPARISON WITH OBSERVATIONS; CORRECTION OF THE INVERSE PROBLEM SOLUTION

  • 5. COMBINED FORECASTING ON THE BASIS OF NEUTRON MONITOR AND SATELLITE DATA

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Summary
Summary

  • The relation between Space Weather parameters and frequency of satellite anomalies are different for different satellite groups (orbits)

  • The models simulated anomaly frequency in different orbits are developed and could be adjusted for forecasting (mainly energetic particles and magnetic activity)

  • The models for forecasting of energetic particle events and magnetic activity can be developed in near future on the basis of ground and satellite observations

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