Cosmic Ray Using for Monitoring and Forecasting Dangerous Solar Flare Events Lev I. Dorman (1, 2) 1. Israel Cosmic Ray & Space Weather Center and Emilio Segre’ Observatory, affiliated to Tel Aviv University, Technion, and Israel Space Agency; Qazrin, ISRAEL. 2. Cosmic Ray Department of IZMIRAN, Russian Academy of Sciences, Troitsk, Moscow region, RUSSIA ERICE, JULY 2004

Magnetic storms

Satellite and Anomaly Number ~300 satellites ~6000 satellite malfunctions

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.

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

Israel Cosmic Ray & Space Weather Center ESO (Mt. Hermon) Neutron intensity + 8 multiplicities, Pressure, AEF, Temp., Hum., Wind Magnetometer H-magnetic field MAPI Israel Multidurectional muon telescopes on Mt. Hermon and underground Earthquake’s precursors in low energy neutrons (He-3 - detectors) Internet space and CR- network

ESO data input for space weather monitoring and forecasting CR Neutrons flux (minute) CR Multiplicity (energy spectrum) Temperature, pressure, humidity, wind, atmospheric electric field Earth magnetic field (magnetic storms) CR-mesons Solar irradiation Earthquake’s neutrons

ESO data analysis for space weather monitoring and forecasting Automatic search of flare beginning in solar cosmic rays Determination of solar CR source spectrum and diffusion coefficient in space Forecasting of solar CR intensity and fluency Prediction of great geomagnetic storms by CR-network data

Forecast of Solar Flare Particle Events Using Cosmic-Ray Neutron Monitor and Satellite Data: Principles of the Algorithm and its Verification

FORECAST STEPS 1. AUTOMATICALLY DETERMINATION OF THE FEP 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 FEP 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 THE PROBABILITY OF FALSE ALARMS THE PROBABILITY OF MISSED TRIGGERS

SCHEME OF ALHORITHMS FOR “FEP ON-LINE SEARCH”

EXAMPLE OF INTERNET PRESENTATION OF REAL TIME DATA FROM ESO (ISRAEL)

2.1 DETERMINATION OF ENERGY SPECTRUM OUT OF MAGNETOSPHERE BY THE METHOD OF COUPLING FUNCTIONS

2.2 DETERMINATION OF ENERGY SPECTRUM OUT OF MAGNETOSPHERE BY THE METHOD OF COUPLING FUNCTIONS

3.1 DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION (1-st CASE: K(R) DOES NOT DEPEND FROM DISTANCE TO SUN)

3.2 DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION (1-st CASE: K(R) DOES NOT DEPEND FROM DISTANCE TO SUN)

3.3 DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION (1-st CASE: K(R) DOES NOT DEPEND FROM DISTANCE TO SUN)

3.4 DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION (2-nd CASE: K(R, r) DEPENDS FROM DISTANCE TO THE SUN)

3.5 DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION (2-nd CASE: K(R, r) DEPENDS FROM DISTANCE TO THE SUN)

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

5.2COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA

5.3 COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA

5.4 COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA

5.5 COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA

5.6 COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA

5.7 COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA

5.8 COMBINED FORECASTING ON THE BASIS OF NM DATA AND BEGINNING OF SATELLITE DATA

Forecasting of expected FEP fluency for.

CONCLUSION BY ONE-MINUTE NEUTRON MONITOR DATA AND ONE-MINUTE AVAILABLE FROM INTERNET COSMIC RAY SATELLITE DATA FOR 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 FEP FLUXES AND FLUENCY IN HIGH AND LOW ENERGY RANGES UP TO ABOUT TWO DAYS. SEPTEMBER 1989 EVENT IS USED AS A TEST CASE.

Part 1 (Ch. 1-4). CR as object of research and as research instrument Part 2 (Ch. 5-9). Influence of atmospheric processes on CR Part 3 (Ch ). Influence of CR on atmospheric processes Part 4 (Ch ). CR research applications Unsolved problems, References, Indexes

Ch. 1 CR as universal phenomenon in the Universe External and Internal CR Different types of CR Astrophysical and Geophysical aspects of CR High Energy Physics of CR Development of CR research Main properties of primary CR