Presentation on theme: "Energetic Particles in Space: its role in Space Weather Studies. Karel Kudela IEP SAS Košice, Slovakia ECRS 2012, Moscow July 5, 2012."— Presentation transcript:
Energetic Particles in Space: its role in Space Weather Studies. Karel Kudela IEP SAS Košice, Slovakia ECRS 2012, Moscow July 5, 2012
1.Relativistic electrons. Magnetospheric transmissivity. 2.Using network(s) of NMs MTs and other instruments for alerts of space weather events. 2.1.Radiation storms Geoeffective events CR fluctuations. 3. Quasi-periodic variations observed in CR: information for space-weather related effects in vicinity of Earth. 4. Summary.
Introduction. Reviews and comprehensive analysis on relations between energetic particles in space and Space Weather research, e.g. : (Flückiger ECRS, 2004; Storini ECRS 2006 and 2010; Mavromichalaki et al, 2006; Kudela et al, 2000; 2009; Singh et al., 2010) – cosmic rays (Panasyuk, 2001; 2004) – mainly magnetospheric populations (Miroshnichenko, 2003) – radiation hazard in space (Scherer et al., 2005; Bothmer and Daglis, 2007)) – SpW effects and physics behind Models and Standards in several countries (e.g. by WG4 (Space Environment) of ISO/TC20/SC14 related to space radiation and space weather (info by Kalegaev)); ESA standards as Space Engineering - Space Environment (http://www.ecss.nl (info by Rodgers)); ISO Space environment (natural and artificial) Models of GCR; ISO Space environment (natural and artificial) Simulation guidelines for radiation exposure of non-metallic materials; ISO Space environment (natural and artificial) Model of Earth's magnetospheric magnetic field. Models are presented at e.g. Others in preparation. Here only selected recent results mentioned. Relations to health not included here.
Electrons due to their penetration ability into materials (cables, inner spacecraft system) are dangerous for satellites. Deep dielectric charging. From (Baker et al., 1998 ) Also SEU (SAA, high lat.) Nighttime injection of e to magnetosphere during storms correlate with satellite errors Relativistic electrons.
(Belov et al, 2004, Dorman et al, 2005, Iucci et al., 2006) in extensive statistical studies of satellite anomalies (220 satellites) found characteristics for quiet and dangerous days anomalies indicating clear difference also in energetic electron fluence. From (Belov et al., 2004)
Fig. 11 of (Lazutin, 2012, 2011). CF ~ 500 km S1 ~ 1000 km At L = 3.5 over BMA by SERVIS-1 (S1) and CORONAS-F (CF). Dst – lower panel Also (Hasebe et al., 2008) essential part of the RB dynamics during magnetic storms may be explained by the change of the magnetic field configuration and the adiabatic effects. Along with non-adiabatic radial diffusion it results in the radial displacement of the outer RB rather than the large losses or total disappearance of the outer RB. Input for models e
(Kovtyukh, 2012) using CORONAS-F data, during 22 strong storms: maximum of a new belt of relativistic electrons (0.6–1.5 MeV) at low altitudes (~500 km) is located on average at lesser L as compared to similar measurements near the geomagnetic equator plane. Geomagnetic field can substantially deviate from dipole configuration not only at the geomagnetic trap periphery, but at its core as well (at L ~ 2.5– 3.5), and these deviations are nonlinear. Simultaneous measurements of the fluxes of relativistic electrons at low and high altitudes can serve for estimation of the real shape of magnetic field lines at L < 4 during geomagnetic disturbances. The new belt of relativistic electrons begins its formation on the outer edge of the ring current in the very beginning of the recovery phase of storms, and that the new storm belt of relativistic electrons is a result of pumping the energy of a decaying ring current through electromagnetic waves to electrons. Also SCR boundary of penetration can be used for testing the geomagnetic field model validity (Lazutin et al., 2012).
2001, FD, Increase of relativ. electrons by >2 orders after the FD, no storm Ack.
(Reeves et al., 2011) by extensive analysis confirmed that the geosynchronous relativistic e flux ( MeV) is best correlated with the solar wind velocity measured 2 days earlier. However, the dependence is not linear, high fluxes are observed for various sw velocities (triangle distribution).
Cross-correlation of energetic e flux at low orbit (low equatorial pitch angles) vs sw speed, Kp etc using SERVIS-1 data (>0.3 MeV). Preliminary example ( L=4, 448 points in years , MeV):
(Balikhin et al.2011), stressed importance of high speed, low density solar wind for e flux. Themis A, preliminary, , L=6.6, narrow interval of B, no dep. on B. V sw and n sw one day before.
(Antonova et al., 2011, Riazantseva et al., 2012) indicate that also the auroral oval can be considered as a region of intense acceleration of energetic electrons - for the analysis of processes leading to the filling of the outer radiation belt and appearance of “killer-electrons”. At high lat’s there are regions with trapping-like structure. Needs to be included in new models of electron flux. Electrons are present there even during quiet-time periods. Relativ. electrons (0.2 – 1 MeV) - CORONAS-Photon, south.(Riazantseva et al., 2012). Multiple observations (3 subsequent orbits) of e poleward from from outer belt boundary – long duration of e enhancement. Dst > -9 nT for No e flux on ACE
1.2. Magnetospheric transmissivity. Strong changes in geomagnetic cut-offs during strong storms have been reported previously (e.g. Tyasto et al., 2009). The predicted cut-offs and asymptotic directions are different for different geomagnetic field models with external current sources (e.g. Kudela et al., 2008). Cut-offs are related to IMF, Dst and to solar wind parameters (Tyasto et al., 2011). At high latitudes the cut-offs are decreasing. The depressions is close or below the atmospheric threshold. The changes of cut-offs have to be taken into account in using the global spectrographic global survey method. Assuming both IP anisotropy and change of cut-offs leads to estimate of rather high anisotropy for one event (Sdobnov, 2011).
In addition to MAGNETOCOSMIC (U. Bern) a calculator-like tool for cut-offs, asymptotic directions with Tsyganenko’96 model has been developed (INFN Milano and IEP SAS Kosice). (web model, mail model – different in rigidity step). Position, time. Structure of allowed, forbidden trajectories, open access. Interactive paraboloid model of Magnetosphere at aboloid/index (Alexeev et al., 2001). aboloid/index Although the cut-offs at Oulu, Apatity decrease also, the effect of improved transmissivity is not seen due to changes below the atmospheric cut- offs. At Almaty and Lomnický štít it is clearly seen.
2. Using network(s) of NMs, other instruments for alerts of SpW Geoeffective events. NMs - precursors before arrival of IP shock to Earth and before the onset of FD (Dorman, 1963). Evolutions of Dst and FD are very different (e.g. Kane, 2010; Kudela and Brenkus, 2004). High v, par – info about precursory A related to IMF inhomogenity - transmitted fast to remote sites: deficit of CR observed up to distance ~0.1. par. cos( ), - cone angle of IMF(Ruffolo, 1999). Precursors to FD: proposed in the frame of PA transport near oblique, plane- parallel shock. Range in power-law index of IMF turbulence, decay length for primary energies to which NM and muon detectors (MD) are sensitive, the loss cone precursors should be observed by NM ~4 hr prior to shock arrival, by MD ~15 hr prior to shock arrival (Leerungnavarat et al, 2003).
From (Mavromichalaki et al., 2011). Many NMs used. Example of anisotropy evolution before the SSC. After reduction of isotropy part. Black, grey - decreases, increases, size of circles - amplitude of changes with respect to base values before the event. European project, Steigies, U. Kiel (http://nmdb.eu) coordinating - recently joined other NMs (US, S. African and others).http://nmdb.eu
Precursory appearance probability by GMDN before the geomagnetic storms of various intensity ( ). NP – without precursor; EV, enhanced variance; LC, loss cone precursors. From (Rockenbach et al., 2011). Superstorms <-250 nT, IS (-250, -100) nT; MS (-100, -50) nT. Limitations. Percentage of the events accompanied by the precursors prior to the Sudden Storm Commencement (SSC) increases with |Dst|. Accompanied by CR precursor in average by ~7.2 hrs in advance of the SSC. EV, LC – types of anisotropy. b. Statistical studies.
(Papailiou et al., 2012a) analyzed FDs in 1967 – 2006 with anisotropy Axy > 1.2% (93 events). 27 different FDs, out of 93, were chosen based on their common behavior in the asymptotic longitudinal CR distribution diagrams. Three groups are recognized: 1. pre-decrease in the longitudinal zone 90° – 180° noticed almost 24 h before the shock arrival (5) 2. pre-increase in the longitudes around and above 180° and lasts almost 12 hours until the FD (14) 3. pre-decrease in different longitudes and of different duration observed (8). The increase in the first harmonic of CR anisotropy before the shock arrival is a good tool in searching for predictors of FDs and magnetic storms and can also serve as one of the indices that characterize the occurrence of precursors. Group 1 is analyzed in detail by (Papailiou et al., 2012b). A long pre-decrease up to 24 hours before the shock arrival in a narrow longitudinal zone 90° to 180° is found.
c. fluctuations. Short term fluctuations ( T <1 h) – first studied (Dhanju, Sarabhai, 1967). Significant changes in the spectra of rapid fluctuations are often observed about a day before and during large-scale IMF disturbances (e.g. Kozlov et al., 1973; Dorman and Libin, 1985; Kudela et al., 1996; Starodubtsev et al., 2004, 2006). (Kudela and Storini, 2005) - a different distribution of the CR indices for 24 h before the sharp Dst decreases in comparison with that for geomagnetically quiet periods. Better relation of Dst to “prehistory” of CR fluctuations than to the actual fluctuations.
(Kozlov and Kozlov, 2011) introduced CR fluctuation parameter - indicator of the IMF inhomogeneity degree in the vicinity of shocks. Important for a medium-term prediction of geoeffective 11-year cycle periods with a lead time of ~1 solar rotation and for an online prediction of shocks - lead time of ~1 day. From (Kozlov and Kozlov, 2011, fig.10): Sep. 5 – Nov. 10, Fluct. par. GCR (right), GCR (left) – illustration of the prediction ability
In addition to NM and MT (ground) measurements the informations from satellite detectors with large geometric factors are important for checking the fine structure of CR fluctuations before, during and after geomagnetic storms (and/or FD). Such possibility gives e.g. INTEGRAL measurements. Copied from (Mulligan et al., 2009). Due to high statistics ( more than 1 order higher than NMs at mountains, direct measurements) the authors revealed fine structure of CR within a 3-day interval from to many intensity variations in the GCR on a variety of time scales and amplitudes. In NM forecasts to utilize such type of sat. meas. Fluctuations can be studied with higher statistics.
Pierre Auger project – part. The full SD array was completed in 2008, with a collecting area of more than m 2 and a scaler counting rate 2 × 10 8 counts.min −1. (Dasso et al., 2012). If 1 min data available, ~ , by 2 orders better than the count rate by NMs, Fine structure: at ~ 1 s if available, possible studies ( ~
Relatively recently started measurements which may help in future in Space weather studies by CR (only selected mentioned) (Mishev and Stamenov, 2008, Angelov et al., 2008) Moussala, Bulgaria Muon measurement in Greifswald, Germany (Hippler et al., 2008) MUSTANG SEVAN (Chilingarian et al., 2009) CARPET – (De Mendonca et al., 2011) CaLMa – Spain NM 2012 (Medina et al, 2012) KACST muon detector (Maghrabi et al., 2011) and others…
2.2. Radiation storms. Ions - several tens to hundreds of MeV - most important for the radiation hazard effects during solar radiation storms with electronic element failures on satellites, communication and biological consequences. Before their massive arrival, NM, if good temporal resolution and network by many stations is in real time operation, can provide useful alerts several minutes to tens minutes in advance (Dorman, 2005). Probabilistic models of SEP fluxes (e.g. Tylka et al., 1997; Nymmik, 2007) SEP and NM network(s), solar n, gamma, electrons. a. NM at a single site (high latitude, good statistics) allows to obtain real time energy spectrum of SEP: South Pole combination of NM64 and that lacking usual lead shielding (Bieber, 2006). January 20, 2005 event.
(S. Y. Oh et al., 2009) checked the potential of South Pole NM data for prediction of radiation storm intensity measured by GOES. The energy spectrum was estimated. (S.Y. Oh et al., 2010): 31 SPEs associated with GLEs. Fluences and peak intensities of SPEs have good correlation with % increases in GLEs, best at channels > 350 MeV). For > 350 MeV the threshold values for GOES fluence and peak intensity are found: most SPEs above threshold are associated with GLEs, almost none below the thresholds.
b. Network of high latitude stations. Ground level enhancement real-time alarm based on 8 high latitude NMs including those at high mountain is described by (Kuwabara et al, 2006). Three level alarm system. Out of 10 GLEs in archived data the system produced 9 correct alarms. GLE system gives earlier warning than satellite (SEC/NOAA) alert. From Kuwabara et al, 2006
c. Including NM at various cut-offs. Several steps of GLE alert algorithm using NM network described by Mavromichalaki et al., NMDB project of 7FP EU (http://nmdb.eu). Anashin et al, 2009 – development of alert signal for GLEs.
GLE 71. (Klein, B ü tikofer, 2012, at ). May 17, first in 24 th solar cycle.http://www.nmdb.eu/?q=node/480 Highest signal at South Pole, both detectors, not observed > 3 GV cut-off
Report from the Athens group distributed (Mavromichalaki et al.): The operational real-time Alert Code of the Athens Neutron Monitor via NMDB issued an Alert signal at at 02:13 UT 39 min in advance from GOES ! (Apatity, Oulu, FSMT) NOAA issued an ALERT based on the recordings of the proton channel at 100 MeV when exceeding 1 pfu. This ALERT was issued for the event under investigation at at 02:52 UT. Ack. for ACE e data, R. Gold, PI
Posner, 2007 demonstrates the important possibility of short-term forecasting of the appearance and intensity of solar ion events by means of relativistic electrons measured on satellites. Selected results at: orkshops/2010/Tuesday_pdf/Posner_REl eASE_CCMCWS_final.pdf Even for fastest-rising major proton event on record (Jan. 20, 2005), the electron precursor signal was detected minutes in advance. d. Energetic electron alert.
Relativistic Electron Alert System for Exploration (REleASE) Available at (Posner, 2007)http://costep2.nascom.nasa.gov/ And as a part of integrated Space Weather Analysis System under Heliosphere (Nuňez, 2011), using X rays and higher energy p to forecast E>10 MeV SEP p
e. High energy n, gamma from the Sun. On the ground: Solar Neutron Alert: Low altitude satellite(s). Example: CORONAS-F (500 km, polar), SONG. The observation of a broad MeV excess, associated with decay indicates exact time of energetic p appearance in the solar atmosphere. Kuznetsov,S.N. et al., 2006.
Tool for identification of onset time of p acceleration to HE (Kurt et al., 2010; 2011). Main SCR increase is preceeded by statist. signif. precursor at individual NM. SONG on CORONAS-F. h.e. gamma
f. Short – term warning of SEP based on position, size of flare. (Laurenza et al., 2009) developed a technique to provide short-term warnings of SEP events that meet or exceed the Space Weather Prediction Center threshold of J (>10 MeV) = 10 # cm (-2) s (-1) sr (-1). The method is based on flare location, size, and evidence of particle acceleration/escape as parameterized by flare longitude, time-integrated soft X-ray intensity, and of type III radio emission 1 MHz, respectively. In this technique, warnings are issued 10 min after the maximum of >= M2 soft X-ray flares. (Veselovsky and Yakovchuk, 2011) - analysis and comparison to the 2001– 2006 observations indicate that more than 50% of SEP were omitted if only NM warning is used for forecast. Higher reliability requires using additional data on the state of solar and heliospheric activity. (Valach et al., 2011) used the ANN method to forecast SEP using data on X ray flares (class, position), on radio emissions (type II or IV radio bursts) and on CME (position angle, width of the CME, linear speed). The output was the forecasted flux of energetic protons ( > 10MeV).
Data downloaded from site prepared by the U.S. Dept. of Commerce, NOAA, Space Weather Prediction Center Energy spectra of recent GLEs e.g. by (e.g. Vashenyuk et al., 2011; Adriani et al., 2012)
Power spectra of Oulu and Kiel NMs constructed from daily means of pressure- corrected data for the period from day 92 of year 1964 until the end of year d 3. quasi-periodic variations observed in cosmic rays : information for space-weather related effects in the neighborhood of Earth.
~1.7 years. In CR reported first by (Valdes-Galicia et al., 1996), analyzed by WV (Kudela et al, 2002), found in outer heliosphere Voyager (Kato et al., 2003). Using NM data Calgary and Deep River (Kudela et al., 1991) indicated that a 20m peak occurs, as well as a spectrum instability in the neighborhood of periods m. (Okhlopkov, 2011) reports that length of the q-2 year periodicity in even and odd numbered cycles differs by ~2 m. In cycles 20 and 22, T = 22–23.5 m, in 21 and 23, T = 20.2–20.8 m. (Mendoza et al., 2006) analyzing solar magnetic fluxes in the period 1971–1998 found that ~ 1.7 year is the dominant fluctuation for all the types of fluxes analyzed (total, closed, open, low and high latitude open fluxes) and has a strong tendency to appear during the descending phase of solar activity. (Rouillard and Lockwood, 2004) relate a strong 1.68-year oscillation in GCR fluxes to a corresponding oscillation in the open solar magnetic flux and infer CR propagation paths confirming the predictions of theories in which drift is important in modulating the CR flux. (Charvátová, 2007)
Spectral analysis of surface atmospheric electricity data (42 years of Potential Gradient, PG at Nagycenk, Hungary) showed also ~1.7 year q-per (Harrison and Märcz, 2007). ~1.7 year periodicity in the PG data is present 1978 – 1990, but absent in 1963 – 1977.
Monthly means of mod. parameter (Usoskin et al., 2011) Wavelet Morlet, 1 – 2 years period. Cross section at ~1.7 y, profile ~ 1986 ~1.7 y 1986 ~1.7 yr ~2.2 yr 2.3 y reported by (Mavromichalaki et al., 2005) in coronal index from coronal stations (Rybanský, 1975). QBO (Laurenza et al., 2012)
Monthly means of mod. potential parameter CR (Usoskin et al., 2011) Wavelet Morlet, 1 – 2 years period. Cross section at ~1.3 y, profile ~ 1946 ~1.3 y (Mursula and Zieger, 2000) found ~1.3-year variation in solar wind speed and geomagnetic activity. solar magnetic ﬁelds since 1915 have been inferred from H-alpha ﬁlament observations by (Obridko and Shelting, 2007) ~ 1.3 yr q-per oscillations detected in the Sun during 8 cycles.
~11 yr ~22 yr~5.5 yr ~8.2 yr~6.4 yr ~14 yr Lomb-Scargle Periodogram of Climax NM indicates several quasi-periodicities at very low frequency. q-per below ~11 year reported by different methods from data by (Mavromichalaki et al., 2003). Periodicities 11 and 22 y described e.g. by (Venkatesan and Badruddin, 1990)
~156 d ~150 d Probably fine structure ~ d, wavelet analysis needed (Chowdhury et al., 2010) found several intermediate-term q-per in solar activity characteristics and in CR. Period days was found prominent during ascending phase of cycle 23 in both galactic CR and solar indices. Detailed studies of ~156 d q- per in various time series of solar activity recently (Akimov and Belkina, 2012)
Fluences of p, e have different time profiles, / d 40 d~ 27 d ~ d Q-per in GCR, ssn, coronal index before p arrival from Sun – analyzed for many GLE by (Perez-Peraza et al., 2011)
Three cycle trend in the CR data discussed by (Ahluwalia, 2011) seems to be present in the periodogram constructed from data (Usoskin et al, 2011) : ~ 32 years
~ 30 yr q-per in AMO (Atlantic Multidecadal Oscillations) – (Perez-Peraza et al., 2008) In CR (direct measurements): (Ahluwalia, 1997). Data from stratospheric CR measurements (Stozhkov et al., 2007; 2011) – monthly ~34 y ~ 11 y ~14.8 y
Wavelet, using filter. Climax data, variable structure, two peaks, at ~27 and ~30-31 d, similar to Fig. 10 by (Dunzlaff et al., 2008) for GCR, EPHIN on SOHO. Transport models (Gil et al., 2005), measurements (Richardson, 2004). This method (WSD, Morlet) provides fine structure. ~27 d and harmon ics ~27 d CR variation correlates with B, Bz, v, and B(v x B) – (Agarwal et al., 2011). 1952
(Gil and Alania, 2011; 2012) reported the 3 – 4 cycling structure of ~ 27 day q-per amplitude in NM data. (Sabbah and Kudela, 2012 in preparation) indicate the ~3 Carrington rotation quasi- periodicity is significant even at higher energies of primaries. ~ ~0.32
(Modzelewska and Alania, 2011) – 3D model of ~ 27 day CR variations and indicate this variation of the GCR intensity for di ﬀ erent polarity periods of the solar magnetic cycle are compatible with the NM data. Checking linear cor. To IMF, solar activity, tilt angle, Climax. CHA – derived from green corona line (Rybanský et al., 2001)
~13.5 d. (Krymsky et al., 2008) …temporal change of the power spectrum of and 27-day variations repeats the power spectrum change of the number of sunspots and tilt angle of the current sheet. The dependence of 27-day variation on the polarity of general magnetic field of the Sun is not found. (Vieira et al., 2012) – double structure of ~ 13.5 d per. at muon detector. 1952)
Important for SpW studies is to compare q-per of CR with those of solar, interplanetary and geomagnetic characteristics. Discriminating between solar and cosmic ray forcing on the terrestrial climate (Fichtner et al., 2006). Solar, geomagnetic and IMF parameters recently analyzed by (Katsavrias et al., 2012) by wavelet and the L/S periodogram identified the ~27 day per. (with ~13.5 days being its harmonic) in solar wind parameters, in Bx, By, and the geomagnetic indices. 1–1.4 yr range of per. in the geomagnetic indices, IMF, Vsw, T was also identified. The QBO (1.7–2.2 years), along with its harmonics of ~4 and 8 years, in all solar wind parameters, apart from IMF, and in geomagnetic indices, are reported. In cycle 22 the periodicities were more clearly defined than in the rest of the observation period, with well pronounced spectral peaks. Mid-term q-per (range 1-2 years) in sunspot groups and flare index has shown differences in the solar hemispheres (Mendoza and Velasco-Herrera 2011). Recently (Vecchio et al., 2012) - detail analysis of different components of heliomagnetic field for QBO are also identiﬁed as a fundamental timescale of variability of the magnetic ﬁeld and associated with poleward magnetic ﬂux migration from low lat. around the maximum and descending phase of solar cycle.
4. Summary, suggestions for future. -Relativistic electron variability, its relation to SW “prehistory”, tool for checking the geomagnetic field models and its variability during active intervals – inputs for models. -Magnetospheric transmissivity, its variations during active periods along with the anisotropy in IP space requires to be utilized simultaneously in analysis of SpW events. -Fluctuations of CR – tool for checking IMF inhomogenities. Jointly with high geometrical factors satellite measurements (INTEGRAL, LISA etc.) and with high statistical acurracy of Scaler (if better time resolution available). -Alerts of geoeffective events using NM and GMDN network: case and statistical studies. New measurement devices, importance of joint study with solar physicists. -Alerts of SEP events: existing systems on satellites – need for joining effort with NM high temporal resolution, progress in networking, real time alerts. -q-periodic variations in CR time profiles: 3 solar cycle periodicity both in stratospheric and mod. parameter long time data; empirical dependence of (averaged) ~27 d q-per on solar and IP activity parameters; ~3 cycle periodicity (~27d) present in CR to high energies; fine structure of q-periodicities; difference in q-per in daily fluences of relat. e and p.
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