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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@physics.technion.ac.il / Fax: 972-4-6964952/Tel: 972-4-6964932)lid@physics.technion.ac.il

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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 change 1.2. 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 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

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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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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 (> 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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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.

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High- and low altitude anomalies No correlation between high and low malfunctions frequencies

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Seasonal dependence Anomalys frequency (all orbits) with statistical errors 27-day averaged frequencies and corresponding half 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 corresponding half 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 F 10.7 – daily Sunspot Numbers and radio fluxes; SSN 27, SSN 365 – 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 (>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 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 Ap>50 nT c – storms with Ap>80 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 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 anomalies Mean satellite anomaly frequencies in 0- and 1-days of proton enhancements in dependence on the maximal > 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 > 10 and >60 MeV flux

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Proton and electron hazards on 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 >2 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. e>2 MeV Apd, AEd, sf p60d, p100 Vsw Bzd, da10 low alt.-high incl. e>2 MeV CRA Apd, AEd, sf Vsw, Bzd high alt.-high incl. p>100 MeV, p60d Eak, Bznsum, SSN365

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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

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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

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