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)

1. Cosmic rays (CR) as element of space weather 1.1. Influence of CR on the Earth’s 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

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 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

1. AUTOMATICALLY DETERMINATION OF THE FEP EVENT START BY NEUTRON MONITOR DATA THE PROBABILITY OF FALSE ALARMS THE PROBABILITY OF MISSED TRIGGERS

2. DETERMINATION OF ENERGY SPECTRUM OUT OF MAGNETOSPHERE BY THE METHOD OF COUPLING FUNCTIONS

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

3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION

3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION

3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION

3. DETERMINATION OF TIME OF EJECTION, SOURCE FUNCTION AND PARAMETERS OF PROPAGATION

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

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 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.

The relation between malfunctions of satellites at different orbits and space weather factors , Malfunction data on different satellites, including “Kosmos” series (circular orbit at 800 km altitude and 74º inclination) in the period 1971-1994 were analyzed in the search of possible influence of different space environmental parameters. The special database was created, which combined, beyond the malfunction information, various characteristics of space weather: geomagnetic activity indices (Ap, AE and Dst), fluxes and fluences of electrons and protons at different energy, high energy cosmic ray variations, solar wind characteristics and other solar, interplanetary and geophysical data. All satellites were divided on several groups by their essential orbital characteristics (altitude, inclination). In the present study we consider an influence of a range of cosmic ray variations on the chosen groups of spacecraft. In particular, the following cosmic ray variations were involved to the analysis: 1) enhancements of the solar protons of different energy; 2) variations of high energy electrons in the Earth magnetosphere; 3) cosmic ray activity indices, obtained from neutron monitor measurements. It was found, that relation of the satellite malfunctions to the cosmic ray variations is different for various orbital groups, and it should be taken into account when the forecasting models for the malfunction frequency are being elaborated. This study was supported by the European Commission (INTAS project No 00810)

Red, Green and Blue Groups Самая многочисленная группа – GEO (135 спутников). На втором месте (68 спутников) группа (low altitude – high inclination). Это “Kosmos” like satellites и мы можем называть эту группу - “Kosmos” group. «Космосы» составляют здесь явное большинство и находятся в центре распределения этой группы. В группе (high altitude – high inclination), в основном, MEO satellites. Главное отличие от GEO не высота, а наклон орбиты. К сожалению, в этой группе только 14 спутников, но они сообщили о > 1000 malfunctions. Так что возможности для статистического анализа есть. Чтобы понять особую важность и специфичность этой группы достаточно взглянуть на октябрьскую (1989 г.) картинку. Группа (low altitude – low inclination) также важна. Здесь, в основном, пилотируемые аппараты с особой ценой сбоя. Но эта группа так мала (5 спутников), что мы вряд ли сможем получить здесь хорошо обоснованные результаты.

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. Look on two examples. First one – the wellknown period – October 89. All our topic is originated from this period with exclusevily bad space weather. We see here 3 big proton events, very strong magnetic storm. In upper part the variations of groundlevel cosmic rays. These proton events were groundlevel enancements. And these arrows are momets of satellite malfunctions. We have 3 clusters of malfunctions and they coincide with maximal proton fluxes.

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. Here the majority of the satellite malfunctions coincides with period of magnetic storm and enhancement of high-energy electron flux. The malfunctions are absent entirely in the high altitude - high inclination group, which played the main role in preceding example. Only a few malfunctions were in GEO group and huge majority – in “blue” group (low altitude - high inclination). We see entirely other subset of satellites comparing with first example.

High- and low altitude anomalies Actually in all mass we have no …. No at all No correlation between high and low malfunction’s frequencies

Seasonal dependence Anomaly’s frequency (all orbits) with statistical errors 27-day averaged frequencies and corresponding half year wave Now some words about seasonal dependence.

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. Now some words about seasonal dependence.

Seasonal dependence (different orbits) 27-day averaged frequencies and corresponding half year wave for different satellite groups The biggest season variation in GEO group. The red group is not demonstrated spring-autumn preference

Time distribution of anomalies

Space Weather Indices Solar activity Solar wind Geomagnetic activity Solar protons Electrons Ground Level Cosmic Rays ~30 indices in total We calculated mean daily frequencies for all groups and insert them in daily table. The main part of this table are

27-day running averaged Sunspot Numbers and Solar Radio Flux 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

Daily Ap-index and minimal (for this day) Dst-index Geomagnetic activity Daily Ap-index and minimal (for this day) Dst-index Different geomagnetic indices 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);

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 Sometimes we have the special crucial moments and periods in Space Weather, SSC for example. We see clear effect. The bigger storm – the bigger effect. Мы видим, что после прихода к Земле ударной волны частота спутниковых сбоев возрастает и остаётся повышенной около недели. Чем сильнее магнитная буря, тем больше это возрастание. Если для всех SSCs возрастание составляет ~20 %, то для магнитных бурь с максимальным Ap-индексом >80 нТл – оно уже близко к двухкратному. Казалось бы, это свидетельствует о связи сбоев с геомагнитными возмущениями. Однако две особенности приведенных картинок говорят о том, что всё не так просто. 1) Средняя частота сбоев pa растёт после SSC, но не в тот же день. На самом деле, средняя величина pa в дни с SSC равна 0.009±0.001, а в дни без SSC 0.014±0.001, т. е. в 1.5 раза выше. 2) Большинство магнитных бурь не продолжаются так долго.

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 But is not so simple. M

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 Other kind of special period. Here 0-day is proton event onset day. 0-день – это день начала протонного возрастания у Земли. Здесь усреднялись только возрастания, в которых среднечасовой поток протонов с энергией >10 МэВ превышал 300 pfu Мы видим возрастание частоты сбоев в нулевой и первый дни, которое на больших высотах больше, чем на малых. Правда, это не единственная особенность в поведении частоты. Сложность полученной картины может объясняться несколькими обстоятельствами: 1) недостаточной статистикой; 2) магнитными бурями, которые часто следуют за протонными возрастаниями; 3) тем, что протонные возрастания часто наблюдаются сериями.

Proton events and anomalies Other kind of special period. Here 0-day is proton event onset day. The biggest effect is in 0- and 1-days and in red group. The smaller effect in green goup and nothing on low altitudes. Различия между группами очевидно. На малых высотах связь с протонными событиями почти незаметна, а самые большие возрастания частоты аномалий наблюдаются в группе high altitude – high inclination. К сожалению, в этой группе не так много спутников. Во время многих из выделенных протонных возрастаний в этой группе было только 3-6 спутников. Mean satellite anomaly frequencies in 0- and 1-days of proton enhancements in dependence on the maximal > 10 MeV flux

Proton events and anomalies Usually we have 10% probability of malfunction, in special proton days this probability rises to 100 %. Вероятность каких-либо аномалий для спутников high altitude – high inclination группы в зависимости от величины максимального потока протонов >10 and > 60 MeV (IMP-8). В среднем спутники группы high altitude – high inclination сообщали о каких-либо сбоях 1 раз в 10 дней. В дни протонных возрастаний вероятность таких сообщений больше. При увеличении величины протонного возрастания опасность спутниковых сбоев быстро возрастает и для самых больших возрастаний становится почти неизбежной. Если брать не все возрастания, а только те, при которых в группе high altitude – high inclination было >6 спутников, то все вероятности существенно увеличиваются – для > 10 MeV протонов практически вдвое. Для этих дней коэффициент корреляции между флюэнсом протонов >10 MeV и частотой (phih) возрастает до 0.83 (p10max>9 pfu, nhih>6). Probability of any anomaly (high altitude – high inclination group) in dependence on the maximal proton > 10 and >60 MeV flux

Proton and electron hazards on the different orbits Red group is obviously the proton group. Blue group is electron one and green is mixed. Mean proton and electron fluencies on the anomaly day

Anomalies and different indices (precursors) We found interesting feature in behavior of some parameters. Here is Ap-index. We see enhanced geomagnetic activity not only in zero-day but some days before Mean behavior of Ap-index in anomaly periods (GEO satellites)

Anomalies and different indices (precursors) We found interesting feature in behavior of some parameters. Here is Ap-index. We see enhanced geomagnetic activity not only in zero-day but some days before Mean behavior of >2 MeV electron fluence in anomaly periods (GEO satellites)

Anomalies and different indices (precursors) We found interesting feature in behavior of some parameters. Here is Ap-index. We see enhanced geomagnetic activity not only in zero-day but some days before Mean behavior of solar wind speed in anomaly periods (GEO satellites)

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 The parameters used to simulate anomaly frequencies for different orbits are listed here. Green, blue and red group. Main role is for electrons in green and blue group, especially – green. In red group protons are much more important than other indices. high alt.-high incl. p>100 MeV, p60d Eak, Bznsum, SSN365

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 We tried to build the models for anomaly frequencies. Example of frequency model (GEO):

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