Final Report Summary - PLASMON (A new, ground based data-assimilative modeling of the Earth's plasmasphere - a critical contribution to Radiation Belt modeling for Space Weather purposes)
Executive Summary:
The security of space assets are affected by the high-energy charged particle environment in the radiation belts. The controlling principal source and loss mechanisms in the radiation belts are not yet completely understood. During a geomagnetic storm the length of time during which space assets are in danger is determined by the loss mechanisms, particularly by relativistic electron precipitation.
The primary mechanism for this precipitation is the interaction of several wave modes with resonant electrons which leads to scattering into the atmospheric loss cone.
The nature of the wave activity and the interactions between the waves and radiation belt particles are strongly governed by the properties of the plasmasphere. At this point there are few existing and regular measurements of plasmaspheric properties, with existing plasmaspheric models lacking the structures known to exist in the real plasmasphere. There is evidence that enhanced wave activity and enhanced radiation belt losses occur due to such structures. In addition, there are large uncertainties concerning the fundamental nature of relativistic electron precipitation (REP), due to the difficulties of undertaking quality in-situ measurements. To address these uncertainties in this proposed project we will provide regular longitudinally-resolved measurements plasmaspheric electron and mass densities and hence monitor the changing composition of the plasmasphere, one of the properties which determines wave growth. This will allow us to develop a data assimilative model of the plasmasphere. At the same time, we will monitor the occurrence and properties of REP, tying the time-resolved loss of relativistic electrons to the dynamic plasmasphere observations. Our approach will primarily use ground-based networks of observing stations, operating in the ULF and VLF ranges, deployed on a worldwide level.
Major objectives:
1. Automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler Detector and Analyzer Network (AWDANet)
2. Retrieval of equatorial plasma mass densities by South European GeoMagnetic Array (SEGMA) and Magnetic Meridian 100 (MM100) magnetometer arrays and cross-calibration of whistler and Field Line Resonances (FLR) method
3. Data assimilative modeling of the Earth’s plasmasphere
4. Modeling Relativistic Electron Precipitation Losses from the radiation belts using the Antarctic-Arctic Radiation-belt (Dynamic) Deposition – VLF Atmospheric Research Konsortia (AARDDVARK) network
Achieved results:
- We have setup the quasi real-time mode of operation at 15 AWDANet nodes.
- We have setup 4 new AWDA stations (Eskdalemuir, Scotland; Forks, SE, USA, Karymshina, Kamchatka, Russia; and Nainital, India)
-Seven new magnetometer stations (two in Poland , one in Lithuania, one in Slovakia, one in Croatia, and two in Namibia) have been installed
- The FLRID code for the detection of FLRs has beeen developed.
- The FLRINV code which infers the equatorial plasma mass density from the field line eigenfrequencies obtained by FLRID has been developed and works in quasi real-time mode of operation.
- The whistler and FLR densities have been calibrated with in-situ (IMAGE and RBSP) data
- We were building the data assimilation framework.
- We have started running the plasmasphere model.
- We have made initial comparisons between the plasmasphere model and observations.
- We have built an Ensemble Kalman Filter based data assimilation framework, and begun to use it with in-situ observations.
- We have started to use the data assimilation model with PLASMON observations
- We have started to produce plasma density maps.
- We have created a set of instruction that will allow non-developers to run the assimilation code
- Three new AARDDVARK station has been installed at Forks (Seattle, USA) at Ottawa and St Johns (Canada).
- - Development of two REP models was completed. One model produces an MLT-L map of electron precipitation using a plasmapause model to define the locations where precipitation occurs. The REP model was built on the output of the work to characterise REP. The REP model can produce a movie of how the precipitation changes during geomagnetic storms, or provide numerical data output to the user.
- A second REP model was produced that uses the assimilated plasmasphere output of WP3 to identify the location the plasmapause, and thus define the locations where precipitation occurs.
- 92 conference presentations: 53 oral presentations (of which 20 were invited), 39 posters
- 17 papers published (plus 1 in review)
- 10 stories in local media, 28 seminars and 8 public talks
Project Context and Objectives:
All space weather models and forecasting methods are dependent on data input for either boundary conditions or the specification of parameters needed by the model. These data at best come from in-situ observations or a statistical model parametrized by some geomagnetic indices, and at worst simply “guessed” to be some representative value.
In-situ observations (satellite measurements) suffer from inherent weaknesses. One, very few platforms give comprehensive measurements of particles, waves and fields. Two, the data availability is very often limited in space and time; at best there will be a handful of observations of a given parameter at any given time throughout all of geospace. Three, with very few exceptions (GOES data), data are not generally available in real or even near-real time, limiting their use for forecasting. Four, the high costs of satellite fabrication and launch make it unlikely that these limitations will be overcome any time soon.
However, there is a complementary or alternative approach to provide data sources for space weather models: ground based measurements. Clearly the combination of ground and space based measurements would provide the best result, but the ground based measurements have several advantages over the space based ones. Generally, they are cheap, and they can produce continuous temporal and spatial coverage. As they generally have access to the Internet providing real-time data presents few problems.
In this project we have developed ground based systems to provide key parameters used in comprehensive radiation belt models that are unlikely to ever be provided by space-borne systems, but are vital for any realistic radiation belt model.
One of the most significant hazard to Earth-orbiting satellites is posed by high fluxes of relativistic electrons. These fluxes contribute to the total radiation dose to the satellite and thus its lifetime, or to deep-dielectric charging, where penetrating electrons gives rise to potential differences, which in turn can lead to intense voltage discharges and surges of electric energy deep inside the electric circuits of the spacecraft – causing severe damage to various subsystems. Such discharges can produce short-lived (fractions of a microsecond) but intense (several Amperes) current pulses.
The temporal evolution of trapped relativistic electron fluxes in the radiation belts is highly dynamic and poorly understood, and is currently topic of intense scientific research which has led to the development of several radiation belt models (the Los Alamos DREAM project, the ONERA Salammbo code, the UCLA radiation belt code, the NERC-BAS radiation belt code).
Reeves [1998] found that geomagnetic storms produce all possible responses in the outer belt flux levels, i.e. flux increases (53%), flux decreases (19%), and no change (28%). The dynamics of these particles is the result of a complex interplay of acceleration, loss and transport processes; and for all these processes the underlying mechanism has a strong dependence on the distribution of the overlapping background cold plasma in the plasmasphere: Acceleration and loss are due to resonances with variety of plasma waves – both the generation of these waves and the resulting resonance conditions depend on the ambient plasma density and composition. Transport is due to resonances with ULF wave modes, which depend on mass loading of field lines and thus also on the ambient plasma density.
The plasmasphere itself is also a dynamic region being permanently influenced by the region below (ionosphere) and above (outer magnetosphere) and is controlled by the relative intensities of the solar wind-imposed electric field and the co-rotation electric field. The plasmasphere plays a central role in magnetosphere-ionosphere dynamics. Apart from hosting the waves which are responsible for the acceleration, decay and transport of radiation belt particles, the plasmasphere also plays an important role in spacecraft charging effects, and it is a significant contributor to TEC which contributes to GPS inaccuracies and communications problems. At the simplest level the plasmasphere is controlled by three factors: a global convection electric field, outflow/inflow from/to the ionosphere, and diffusive equilibrium. Therefore the dynamics of the plasmasphere requires monitoring, modeling and forecasting. Fundamental parameters of the plasmasphere are the plasma distribution, density and composition. To obtain this distribution, we will use a combination of ground based networks, and ground based techniques, in combination with a data-assimilative model of the plasmasphere.
One network (AWDANet) measures Very Low Frequency (VLF) waves to capture and analyze whistlers, another network (EMMA) measures Ultra Low Frequency (ULF) signals to capture and analyze Field Line Resonances (FLR). Methods based on the two phenomena are capable of providing plasmaspheric densities. The two methods are complementary to each other due to the spatial and temporal occurrences of whistlers and FLRs. The data from the two ground based network are fed to the data-assimilative modeling of the Earth’s plasmasphere, to provide a hi-fidelity model. The models identifying electron loss to the atmosphere from the different regions of the plasmasphere demonstrate one application of the new plasmasphere model in providing value added information on the loss processes for use in radiation belts models making use of measurements by a third ground based network (AARDDVARK)
Objective 1: Automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler detector and Analyzer Network (AWDANet)
The cold electron density distribution of plasmasphere cannot be easily measured routinely, but is a key parameter for modeling of the plasmasphere and radiation belts. Whistlers have been regarded as cheap and effective tools for plasmasphere diagnostic since the early years of whistler research, but it never became a real operational tool since “reducing” whistler data to equatorial densities was very labor intensive. Recently the Space Research Group of Eötvös University has developed a new, experimental Automatic Whistler Detector and Analyzer (AWDA) system that is capable of detecting whistlers and we plan to use this system to process lightning whistlers with no human interaction. A network formed by AWDA systems (AWDANet) is evolving and now covers low, mid and high magnetic latitudes. A recent developments in whistler inversion methods for multiple-path whistler groups propagating on mid and high latitude will allow us to retrieve electron density profiles automatically for wide range of L-values, with low latitude whistlers practically covering the whole plasmasphere. The AWDANet will be extended to have better spatial and temporal coverage and thus will be able to provide density profiles for different MLTs which can be used as a data source for space weather models. We will
- extend the AWDANet to have better MLT and latitudinal coverage,
- develop an automatic whistler analyzer (AWA) method based on our new whistler inversion method,
- implement the AWA in AWDANet nodes and
- develop AWDANet to work in quasi-real-time mode of operation.
Objective 2: Retrieval of equatorial plasma mass densities by SEGMA and MM100 magnetometer arrays and cross-calibration of whistler and FLR method
Thanks to the recent developments in magnetometry (reduction of noise), data acquisition (resolution and timing) and the theory (wave propagation, event detection, models, inversion) of magnetohydrodynamic (MHD) waves, the routine monitoring of the cold plasma mass density of the plasmasphere became possible. Although the preparation of such monitoring systems in Europe has started, the efforts have so far been made separately in different countries. University of L’Aquila established the SEGMA (South European GeoMagnetic Array), , while Eötvös Loránd Geophysical Institute (Hungary) initiated the MM100 array, both in 2001. One of the main goals of both arrays was to monitor the plasmaspheric mass density based on the detection of geomagnetic field line resonances (FLRs). None of these ‘monitoring’ systems, however, became operational in the sense that they never produced quasi real time products.
Data are transferred a few times a year, and processed on a non-regular basis. The latitude coverage is also not sufficient to monitor the whole plasmasphere. In contrast to the whistler method the FLR method can be used to infer the plasma mass density even in the plasmatrough and to also identify the location of the plasmapause. In the context of the current project we plan to unify the isolated European efforts to call into being a joint European network, EMMA (European quasi-Meridional Magnetometer Array,) with stations ranging from Italy to the northern Finland (L-shells 1.6 – 6.7).
We intend to use and upgrade existing magnetometer networks (IMAGE), which were originally established for other purposes and other requirements (resolution, sampling rate, timing), but the data of which can be exploited for plasmasphere observations, as well. In accordance with these goals we will
1. unify and extend the SEGMA, MM100 and IMAGE networks into EMMA (including stations in Southern Africa maintained by HMO) to have better latitudinal coverage,
2. develop an automatic FLR identification (FLRID) method based on previous experience and recent improvements,
3. develop an automatic FLR inversion (FLRINV) method based on most recent achievements including error estimations,
4. develop all EMMA stations to work in quasi-real-time mode of operation,
5. evaluate relative abundances of heavy ions in the plasma composition from simultaneous determinations of mass density (FLR method) and electron density (whistler method)
Objective 3: Data assimilative modeling of the Earth’s plasmasphere
Even dense measurements only sample the plasmasphere at limited resolution in both space and time. For example, FLR data only provide measurements during the daytime, and only at the local time of the observatory pairs, whereas whistlers provide the best measurements during the night time, and again only at the MLT and L-shells of the observatories. The same restrictions apply to, and are usually more severe, in the case of satellite measurements. Yet determining the effect of wave-particle interactions on the radiation belts require a continuous map of the plasma density in both time and space. In order to provide such a complete map it becomes necessary to interpolate between measurements, again in both time and space. A good approach to this interpolation is data assimilation. The basic idea behind data assimilation is the combination of a physical model of the system with observations relevant to constraining the physical system. The most sophisticated data assimilation schemes preserve the internal physical consistency of the model while matching it optimally to the data, in time and space. This combination of observation and a physical model should, in theory, perform better than either by itself. This is actually the case, as demonstrated by many examples ranging from radar tracking of aircraft to routine weather forecasting. A good data assimilation scheme essentially fits a time-dependent model to a time-series of uncertain observations. It can be visualized as a feedback system in which the measurements provide error signals, which adjust free, or poorly determined, inputs to the model. In the case of the plasmasphere these include the electric field, the refilling and loss rate, and possibly some composition information. Specifically this is carried out by simulating the measurements from the model and comparing those to the observations, taking into account complicated relationships such as the fact that future measurements can constrain past drivers of the model.
At New Mexico Tech we are working with data assimilation schemes to combine plasmaspheric measurements with a numerical physics-based plasmasphere model. The two data assimilation schemes which we are pursuing are Ensemble Kalman filtering and particle filtering. As part of this project we will develop the necessary means to incorporate FLR measurements and whistler measurements into the assimilation scheme. There are two approaches to this, and we will pursue both. The simplest approach is to estimate equatorial and/or field-line-integrated mass and electron density from the measurements using a nominal model. This will be the simplest to incorporate as it just requires matching the model plasma densities to those measurements. A more involved approach computes the FLR frequencies from the model using first principles or simplified solvers. In the case of FLR measurements it requires solving an eigenvalue equation, and in the case of the whistler measurements it requires estimating the whistler parameters. We will also undertake incorporating composition information into the model. When the composition changes slowly over time it is possible to estimate it through the data assimilation even when there is only limited overlap between FLR and whistler measurements.
Objective 4: Modeling REP losses from the radiation belts using the AARDDVARK network
During a geomagnetic storm the length of time during which space assets are in danger is determined by the efficiency of the loss mechanisms, particularly through relativistic electron precipitation into the atmosphere. The primary mechanism for this precipitation is the interaction of several wave modes with resonant electrons, which leads to scattering into the atmospheric loss cone. The nature of the wave activity and the interactions between the waves and radiation belt particles are strongly governed by the properties of the plasmasphere. In this work package we will use the assimilative model of the plasmasphere to identify regions where plasmaspheric structures such as the regions occurring on, inside, and outside of the plasmaspause and/or composition changes are likely to result in enhanced electron losses. We will monitor the occurrence and properties of REP using the ground based AARDDVARK network.
There is evidence that different wave activity and varying radiation belt losses occur due plasmaspheric structures. For processes that occur inside of the plasmapause we would anticipate that plasmaspheric hiss would be the dominant loss mechanism, while outside the plasmapause chorus would be expected to dominate. In addition electro-magnetic ion-cyclotron (EMIC)-driven precipitation associated with the plasmaspause itself can lead to intense bursts of very strong relativistic precipitation from the radiation belts as we have previously shown, and could provide radiation dose hazards for astronauts in (very) low Earth orbit. We will use the AARDDVARK data to determine the electron precipitation flux levels that are associated with the different regions of the plasmasphere, identifying subionospheric transmitter-receiver propagation paths that are influenced solely by processes that occur either inside, or outside of the plasmaspause. In order to achieve this aim we would map the regions of plasmaspheric structures into the Earth-ionosphere waveguide in order to know which part of the AARDDVARK network they would be monitored by; plan to build up the AARDDVARK network to provide additional receiver pairs located such that they combine together to monitor a constant L-shell; include data from riometers and pulsation magnetometers in case studies; and ultimately develop a REP loss module to add on to the plasmasphere model including an indication of radiation dose hazard. This analysis would rely on development of quasi-constant L-shell monitoring paths in the AARDDVARK network.
At the end of the project we will provide real time data of plasmaspheric densities, a data-assimilative model of the plasmasphere and a model of Relativistic Electron Precipitation (REP) losses. All these data, models and information will significantly contribute to European capacity to estimate and prevent damage of space assets from space weather events as well as to improving forecasting and predicting of disruptive space weather events.
The data, models and forecasting capabilities will be available for European and international actors in the field.
No such data and models are available to date, neither separately nor as a complex service or method which we will develop in the project.
As security of space assets from space weather events is a global challenge and thus difficult to confine any activity into the EU, therefore – completely conforming to the call -, we will involve partners from other space-faring nations (USA, New Zealand, and South Africa) as well as IPCP partner (South Africa). Without their specific expertize or the utilization of their special geographic (geomagnetic) location(s), the project objectives cannot be achieved.
ESA SSA preparatory programme – which is in the middle of its first phase – is currently identifying European Space Weather assets, operational services; services and methods that are potentially operational. No complex service or method such as proposed here is currently in the list of space weather-related assets of ESA.
Project Results:
In PLASMON, we had four workpackages on S&T and on for dissemination. The results achieved is presented through the WPs. The figures referred in the text are in the attached pdf file.
WP1. Automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler detector and Analyzer Network (AWDANet)
The general objective of WP1 was the automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler Detector and Analyzer Network (AWDANet). The obtained electron densities, together with plasma mass densities from WP2 and in situ measurements from satellites, were fed into the data assimilative model of the plasmasphere (WP3) to construct a dynamical model of the Earth’s plasmasphere. The specific objectives of WP1 were the following:
1. extend the AWDANet to have better MLT and latitudinal coverage,
2. develop an automatic whistler analyzer (AWA) method based on our new whistler inversion method,
3. implement the AWA in AWDANet nodes and
4. develop AWDANet to work in quasi-real-time mode of operation.
Main results of WP1
Objective 1. Extension of AWDAnet
We have extended the AWDANet with three new stations:
1. Eskdalemuir, Scotland
In cooperation with British Geological Survey, the Scotland AWD has been installed in BGS observatory at Eskdalemuir (Latitude: 55.31 Longitude: 356.80 L=2.72)
2. Karymshina, Kamchatka, Russia
In cooperation with Institute of Cosmophysical Researches and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences we have installed the AWDA station in Karymshina (Latitude: 53.04 Longitude: 158.70 L=2.13).
3. Forks, WA, USA
The third AWDA station was installed in the premises of University of Washington at Forks, near Seattle, USA (Latitude: 47.93 Longitude: 235.61 L=2.76)
The map of the extended AWDANet including the three new stations is on Figure 1.1
Objective 2. Development of an automatic whistler analyzer (AWA) method
The whistler rate is 0.5–2 scalable events per hour for low‐activity regions and 40–140 scalable events/hour for the highest‐activity region. On the other hand, there are days at the Antarctic Peninsula when whistlers are literally observed in each second, such that 180–800 scalable events/hour are expected. In contrast, the highest rate at low‐activity regions is around 5000 traces per day [Lichtenberger et al., 2008; Collier at al., 2009; Rodger et al., 2009, Collier at al., 2010; Collier at al., 2011], thus the estimated scalable whistler rate is 10–40 per hour on those days. Taking into account the time scale of variations in plasmaspheric electron densities (it is on the order of hours), which can be attributed either to MLT variation at a given location or to the dynamics of plasmasphere, 10–15 snapshot observations per hour of the equatorial density with a meridional cross section is likely to be dense enough to describe significant dynamics. This means that a quasi real‐time implementation should be able to complete the analysis of a MP whistler group within 250–300 s.
Based on the new, extended whistler inversion method [Lichtenberger, 2009], we have developed the automatic whistler analyzer algorithm. It is based on a special transformation called Virtual (whistler) Trace Transformation [Lichtenberger, 2009]. (Figures 1.2 and 1.3) .It has three parameters: the time of the causative sferic (dt) that is the one of the major outstanding problem of whistler analysis. The other two parameters, A and B are related to nose frequency fn and nose time tn (Figure 1.4) to the geometric parameters of a whistler. A and B are related to the real physical parameters propagation L-value and equatorial electron density neq through log10 neq = A + B*L [Lichtenberger, 2009].
We have solved the causative sferic problem through an old method, the Q-transformation [Dowden and Allcock, 1971], which is used to determine the nose frequency of a non-nose whistler. There is an important implicit assumption in this transformation: it assumes the time of the causative sferic is known.
We tried this Q-transformation on spectrogram matrices using an arbitrary dt and we found that it works in some aspect, it produces non-straight lines. This led to a 'trial and error' approach: one can measure the travel time from an arbitrary point and see whether the result of the Q transformation is a straight line or not and move the origin until the result is a straight line. Addition to the determination of dt, the output of the Q-transformation can also be used to identify and extract information of the strongest whistler trace (Figure 1.4). Application of whistler inversion to the extracted trace provides a propagation L-value and equatorial electron density, that is a fixed point in (A,B) space. Together with dt obtained, the parameter space is reduced to two a limited range of the two unknowns (A and B).
Since we do not have a good initial set for A and B, an optimization procedure could be applied to find the optimal values – but the objective function of the problem is badly behaving, it has many local minimums slightly smaller than the global minimum.. This can led to in-matured stop of the procedure. Therefore a different approach is used to find the optimal (A,B) pairs. Because the dimensionality of the problem is reduced, VTT is calculated only for a reduced set of (A,B).
Objective 3. Implementation of the AWA in AWDANet nodes
The implementation first required to make a decision on hardware architecture. The parallel processing (PP) architecture serves as a hardware for Automatic Whistler Analyzer (AWA). AWA requires high CPU resources to be run in quasi real-time. The decision of AWA PP is based on the following test results and preliminary requirements:
- inversion of a Multiple Path (MP) whistler group on a single average CPU (single core of a Intel Core 2 Duo) takes 24-48 hours, while the goal is to invert 10-15 MP whistler groups per hour. Further, the final architecture has to be compact enough to fulfill the size, weight and power consumption constrains on far location (e.g. Antarctica) setup.
- during the negotiation phase of PLASMON, we have improved the AWA algorithm by a more sophisticated Look-up Table (pre-calculated whistler models). The usage of improved LUT reduced the run-time from 24-48 hours to 4-5 hours (15-18000sec).
As AWA algorithm is highly suitable for parallel processing, We have tested the AWA algorithm on three architectures:
- PC cluster
- FPGA
-. GPGPU
Based on volume, power consumption and price/budget considerations, we have selected GPU technology and we concluded that AWA can be successfully implemented with the required speed on a small GPU cluster consisting of 8 GTX580 – actually 4 GTX590.
The full AWA processing chain consist of three major steps:
1. pre-processing of AWD outputs. AWD should work in or faster than real-time, therefore it detects a whistler event and saves raw data with the event into a disk file. Due to the dual-purpose of AWD algorithm (see Lichtenberger et al., [2008]), the algorithm may save empty raw data (i.e. signal without whistler). The pre-processing algorithm has to
reliably filter the empty data files and
create the input cleaned, reassigned spectrogram matrix for AWA.
This pre-processing algorithm is an enhanced version of AWD algorithm. It can process a disk file between 10-120sec, depending on the complexity of the whistler event (naturally faster if no whistler event in the file).
2. AWA phase one,
3. AWA phase two.
AWA phase one (Q-transformation to determine dt and fn, extraction of f-t pairs for the strongest trace and running the whistler inversion procedure is implemented in CPU (i7-2600) code and takes 20-30 sec, depending on the complexity of whistler events.
AWA phase two code, finding the optimal values of A and B were migrated to GPU using CUDA library. We have implemented the code using plain cuda implementation on 2xGTX590. Phase two runs take: 20 -60 sec.
Implementation of AWA code for AWDAnet node located at different magnetic latitudes.
Basically there are differences in frequency range of filters in pre-processing codes and VTT for different magnetic latitudes.
These are:
1. 4000Hz-19000Hz for mid-latitudes (1.4
Additionally, pre-processing always requires site dependent noise thresholding. This had to be done from station to station.
As AWDANet has more operating nodes (running AWD only) than PP units purchased in PLASMON, we had to make a selection where to install the PP units. This selection was based on the following criteria:
1. Signal quality
2. location to reach optimal magnetic latitude and longitude coverage
3. whistler activity
Based on these criteria we have selected the station below and installed AWA on PPs:
1. Mid-latitude stations: Tihany (Hungary) and Gyergyóújfalu (Transylvania), Grahamstown (South Africa)
2. higher latitude stations: Eskdalemuir (Scotland), Forks (WA, USA), Karymshina (Kamchatka, Russia) – these are the new stations installed in PLASMON - , Dunedin (New Zealand), Humain (Belgium), Ithaca (NY, USA), Marion Island (South Africa), Rothera (Antarctica, UK), Tvarminne(Finland),
3. high latitude stations: SANAE (Antarctica, South Africa), Halley (Antarctica, UK), Oulu (Finland)
Objective 4. Development of AWDANet to work in quasi-real-time mode of operation.
On the 15 stations listed above the AWA were gradually installed and tested, the full network operates in quasi real-time mode since end of June, 2014.
The block diagram of the configuration of AWDANet nodes with AWA is on Figure 1.5
Maintaining the quasi real-time mode of operation: the quality flag
The implementation of the AWA algorithm on GPUs fulfilled the expected speed for the quasi real-time mode of operation in general. However, we were faced with two particular problems that might prevent the system to achieve the quasi real-time mode of operation on longer term:
a. uneven input data rate – the whistlers are natural phenomena and their occurrence rate is highly varying exhibiting diurnal and seasonal variations. This means that there are periods – days – without a single whistler at a location and there are periods when hundreds of whistlers are detected.
b. fixed processing capacity. The installed GPUs can process 40-100 events per hour, depending on the complexity of the event. When there are high number of events, the long processing queue can prevent the system to keep up the processing in quasi real-time.
To overcome this problem, we introduced an extra pre-processing step. In this step, the quality of the event is estimated and a flag is assigned to each event. The estimation is based on the SNR of the whistler traces, the remnant noise in the simplified reassigned spectrogram and the dispersion of the spectral matrix elements in the time-frequency domain. Sixteen classes have been created and and assigned from QF=00 to QF=15.
Two examples of assigned Qf are shown on Figure 1.6.
The processing stream is controlled by a bash script on AWA preprocessing PC. During low and medium activity, it takes the next detected whistler event and process it: pushing through the event/trace locator – pre-processing - inversion – post-processing pipe regardless of the value of the QF. Naturally, the inversion is the slowest section of the pipe.
However, if new events pop up at the inversion pipe while the previous event is still in the pipe, the event with the lowest QF will be selected, when the pipe is ready for processing the next event. If there are two or more events with the same lowest QF, the latest is selected. The rest are redirected to the 'non real-time queue'. This queue is gradually processed when no new event is detected, i.e. when the pipe is in idle state. The statistics collected during the first period of quasi real-time mode of operation proves the role of QF in maintaining the mode (Figure 1.7).
WP2. Retrieval of equatorial plasma mass densities by SEGMA and MM100 magnetometer arrays and cross-calibration of whistler and FLR method
The scope of WP2 was the retrieval of equatorial plasma mass densities using geomagnetic field line resonances (FLRs) detected at an extended latitudinal array of magnetometer stations. The inferred plasma mass densities have to concur, together with electron concentrations from WP1 and in situ measurements from satellites, to construct a dynamical model of the Earth’s plasmasphere (WP3). The specific objectives of WP2 were the following:
1. unify and extend the pre-existing SEGMA, MM100 and IMAGE networks into EMMA complemented by SANSA network to have a latitudinal coverage suitable for the FLR technique,
2. develop an automatic FLR identification (FLRID) method based on previous experience and recent improvements,
3. develop an automatic FLR inversion (FLRINV) code based on most recent achievements including error estimations,
4. develop all EMMA stations to work in quasi-real-time mode of operation,
5. evaluate relative abundances of heavy ions in the plasma composition from simultaneous determinations of mass density (FLR method) and electron density (whistler method).
Results achieved
1. and 4. Establishment of EMMA and SANSA network and development in real-time mode of operation
Thanks to recent developments in magnetometry (e.g. reduction of noise), data acquisition (improved resolution and timing), and the theory (wave propagation, event detection, models, inversion) of magnetohydrodynamic (MHD) waves, the routine monitoring of the cold plasma mass density of the plasmasphere has become possible. EMMA was established in 2012 within the frame of PLASMON with the main goal to monitor the plasmaspheric mass density based on the detection of FLRs. EMMA was born through the unification and extension of previously existing European magnetic arrays: SEGMA (South European GeoMagnetic Array) (Vellante, et al. 2004) andMM100 (Heilig et al. 2010) including the Finnish stations of IMAGE. 6 new stations were installed: BRZ (Birzai, Lithuania, L = 2.74) HLP (Hel, Poland, L = 2.54) SZC (Szczechowo, Poland, L = 2.35) ZAG (Zagorzyce, Poland, L = 2.10) VYH (Vyhne, Slovakia, L = 1.95) LOP (Lonjsko Polje, Croatia, L = 1.73). At the end of 2012 EMMA consists of 25 stations from north Finland to Italy (L-shells 1.6–6.7 Figure 2.1) as a joint effort of FMI (Finland), IGFPAS (Poland), SAS (Slovakia), MFGI (Hungary), and University of L’Aquila (Italy). PLASMON also has a smaller magnetometer network maintained by SANSA at South-African conjugate area (Figure 2.2). The SANSA observations (SUT-HER) will allow examination of possible effects of north-south ionospheric asymmetries and will give independent estimates of the plasma mass density at L = 1.8 therefore providing a check on the accuracy of the method. In addition, measurements from the new Namibian pair TSU (Tsumeb, L = 1.35)-WBP (Waterberg Plateau Park, L = 1.39) will allow extension of the monitoring to a lower L-shell (Heilig et al., 2013b; Lichtenberger et al., 2013).
All stations of the EMMA and SANSA network are equipped with low noise fluxgate magnetometers of similar performances and GPS antenna for precise time recording. Each institutes upgraded or replaced the data loggers to fulfil PLASMON requirements (remote access of data, >20 Hz sampling rate, precise timing). MFGI has developed a new magnetic data acquisition system. The MFGI DAQs are connected to the output of LEMI-035 (specific LEMI design for PLASMON) and NAROD STE magnetometers. The analogue output of the magnetometers has a cutoff at 20 Hz. Data are sampled at 128 Hz and then filtered to produce the EMMA data at 1 Hz data rate. The noise is typically below 10 pT @ 1 Hz, the overall delay is 32 ms (Merényi et al., 2013).
1-s magnetic data from all stations are transferred in near real-time to the project servers (one at MFGI and the other at UNIVAQ) for processing (Figure 2.3). All data are also collected at these servers (e.g. at http://plasmonserver.aquila.infn.it/) as daily files in a specific binary data format (Figure 2.4). At MFGI a web-based monitor http://geofizika.canet.hu/plasmon/ was also developed. This web monitor also provides near real time (as well as archived) magnetograms and power spectra of all EMMA and SA stations. On the same website the availability of current as well as archived data can be checked. The website also informs on PLASMON and the EMMA network in general and provides detailed information on instrumentation and location of all EMMA sites
2. FLRID
The first step of the data processing is the detection of FLRs. For this scope an automatic method (FLRID) has been developed. FLRID is based on the expected characteristics around the local resonance frequencies of phase difference (phase gradient technique introduced by Waters et al. 1991) and amplitude ratio of the magnetic signals recorded at two stations along the same magnetic meridian closely spaced in latitude (1°-3°, Figure 2.5). FLRID also allows the uncertainty in the detected FLR frequency (Berube et al. 2003) as well as the resonance width (Green et al. 1993, Heilig et al., 2013a) to be estimated. Thanks to the proper planning of the spatial configuration of the EMMA and SANSA networks the method can be applied to more than 40 station pairs. For each station pairs the optimal set of FLRID parameters has been derived and adjusted through repeated statistical analysis of FLRID results. FLRID is run automatically on PLASMON EMMA Servers in every 15 minutes.
The near real time procedure as well as archived cross spectra can also be monitored on the web-based monitor.
3. FLRINV
The second step is the FLRs inversion. We developed a code (FLRINV) which solves the MHD wave equation of the resonance in an arbitrary magnetic field geometry to infer the plasma mass density at the magnetic equatorial point ρeq of a given field line. The T01 Tsyganenko model of the magnetospheric field has been adopted. Along the field line a standard power-law dependence for the density distribution ρ(s)/ρeq = (r/req)-m is assumed, with the power index m generally being equal to 1. Detailed studies on the comparison of results obtaining by assuming either dipole, IGRF, or T01 models have been conducted (Figure 2.6). We have also evaluated the uncertainty in ρeq which derives from the uncertainty in FLR frequency determination, in the T01 model parameters (Vellante et al., 2014), and in the field-aligned density distribution. The overall error of our individual density estimates (> 90% of all) has been evaluated to be of the order of 20-30% for ~2 < L < ~6, which is confirmed by comparison with in situ measurements. Larger uncertainties might occur for L < ~2 (Vellante and Förster, 2006) or for L > ~4 during strong geomagnetic storms (Dst < 100-150 nT) (Vellante et al., 2014).
FLRINV also works in near real time. It cyclically runs every 15 min using the output of FLRID, solar wind and Dst parameters.
Real time solar wind data are taken from the NOAA Space Weather Prediction Center which provides, in near real time, the latest 2 hours of magnetic and plasma data of the ACE satellite located at the L1 libration point, as well as the precise ACE location (http://www.swpc.noaa.gov/ftpdir/lists/ace/). These data are then time-shifted in order to take into account the propagation time of the solar wind from the satellite position to the Earth (typically 1 hour). Finally, these propagated data are resampled at fixed times and hourly running averages (with time step of 1 min) are produced.
Real-time Dst data are taken from the Dcx server of the University of Oulu, Finland through the following link: http://dcx.oulu.fi/DstDcxDxtData/RealTime/Dxt/DxtRT.txt.
The final result of the inversion is stored in daily text files which are updated every 15 min and are available online at http://plasmonserver.aquila.infn.it/EMMA_FLR_DENSITY.
Realizing the importance of the plasmapause position we also developed techniques to derive the location of this boundary automatically both from ground and space observations (e.g. Heilig and Lühr, 2013)
5. Cross-calibration
In order to validate the method of estimating electron and mass densities from the ground-based measurements of whistlers and FLRs we have conducted some comparative analysis of our ground-based measurements with in-situ satellite measurements.
Calibration of equatorial electron densities obtained by whistler inversion with in situ electron density measurements by IMAGE (Figure 2.7) and Van Allen Probes (VAP) satellites has been done. The results evidence no apparent bias in whistler inferences and allow us to reliable use of equatorial electron densities from whistler inversion in plasmasphere modeling in WP3.
Comparisons of plasma mass densities derived from FLRs with in situ electron density measurements provided by IMAGE (Figure 2.8) and Van Allen Probes (Figures 2.9-10) satellites have been conducted for some favourable conjunction events. The median of the plasma mass to electron density ratio in the plasmasphere is found to be 0.96 amu, very close to the value (1.00 amu) expected in a plasma consisting of protons and electrons only. In the plasma trough the same ratio is 2.17 amu indicating the presence of heavier ions (He+ and/or O+). The highest ratios (up to 8-10 amu) are found around the plasmapause which is in line with previous observations indicating the presence of an oxygen torus in this region.
WP3. Data assimilative modeling of the Earth’s plasmasphere
1. Motivation
The plasmasphere is a important component of the inner magnetosphere. It is now well-known that the plasmasphere is critical in the control of the wave processes which are responsible for the acceleration and decay of the radiation belts. Plasma density gradients and boundaries are important sites for the generation of these waves. For that reason accurate knowledge of the plasma density
distribution in the plasmasphere will help improve the accuracy of radiation belt models' specification of the radiation environment.
The plasmasphere is a dynamic system which is sensitive to external drivers. The most important external driver is the global convection electric field. The gross structure of the global convection electric field is understood, but the detailed evolution of this electric field cannot currently be specified accurately enough to drive a plasmasphere model and produce a realistic plasmasphere which is accurate enough to drive radiation belt models.
The solution to this difficulty is to incorporate observations and to constrain the plasmasphere with observations. This merging of observations and models is called data assimilation.
In this project we use data assimilation with ground-based observations in order to improve the accuracy of the specification of the plasmasphere for any given events. For the plasma model we use the Dynamic Global Core Plasma Model, and we considered two different data assimilation approaches, the particle filter, and the ensemble Kalman filter, before settling on the ensemble Kalman filter. In the following sections we describe the
2. Plasmasphere Model
For this project we use the Dynamic Global Core Plasma Model (DGCPM) [Ober and Horowitz, 1997]. DGCPM models flux tube number density for approximately L>1.3. DGCPM is governed by a simple but powerful set of equations which are outlined in Figure 3.1. These equations implement the following physical processes. (a) the outflow of plasma from the dayside ionosphere to fill fluxtubes to a saturation level, (b) the loss of plasma into the ionosphere on the nightside, (c) the convection of the plasma according to imposed magnetic and electric fields. Although DGCPM has the flexibility to function with arbitrary magnetic field we have so far only used it with a simple dipole magnetic field. Figure 3.2 shows a few snapshots of the plasma distribution from a run of the DGCPM..
3. Data-model comparison
A good starting point for data assimilative modeling is that there is at least qualitative agreement between observations and model. For that reason we performed some comparison studies early in the project to analyze how well the model represents reality. In the early phase of the project this comparison was primarily in the form of satellite data since the ground-based data were still being
processes at that stage. Thus, Figure 3.3 shows a comparison between the DGCPM and density observations from LANL geostationary satellites. Figure 3.4 shows the first comparison of the output of the VLF inversion algorithm with the DGCPM. The initial results confirmed that the DGCPM
is adequate for representing the plasmasphere well enough for being used in data assimilation.
4. Data assimilation
We implemented an Ensemble Kalman Filter (EnKF) data assimilation framework. The code was implemented in C++ using openMPI for parallelization and ScaLAPACK to perform the distributed matrix operations that are necessary in a multi-processor Kalman Filter implementation. For the DGCPM we use the original Fortran implementation written by Dr. Daniel Ober at the Air Force Research Laboratory in in Albuquerque, USA. This model is wrapped in a C++ class interface making it easily usable within the EnKF framework.
Because the EnKF filter worked well enough we decided to not implement a particle filter as well, but instead focuses on the application of the EnKF data assimilation filter.
The Ensemble Kalman Filter (EnKF) [e.g. Evensen, 2003] which uses a statistical approach to replace the covariance matrix of the Kalman filter. The approach is illustrated in Figure 3.5. Instead of a mean state and a covariance matrix of the state the EnKF uses a single matrix whose columns are complete model states which are allowed to evolve randomly forward in time according to model noise equivalent to covariance noise matrix used in the Kalman filter. It is then possible to compute any statistical measure across the columns, including all the covariances of the Kalman filter covariance matrix. These are now obtained on a statistical basis so there should be enough columns to fully explore parameter space.
To advance the the model ensemble forward in time we must add model noise. That means driving each ensemble member differently. This driving can be performed anywhere in the model, but in our case we have elected to adjust only the electric field that drives convection in the plasmasphere model. The electric field parameters are allowed to evolve according to a red-noise signal and thus over time the individual ensemble members will tend to diverge. This divergence represents the reduction in the state of the system as the model evolves forward in time without constraining data. When data become available the model ensemble is transformed by a linear transformation to produce a new ensemble which is consistent with the data and the uncertainty of the data. This is illustrated in the upper right corner of Figure 3.5.
5. Data assimilation tests
To test the data assimilation framework in preparation for the ground-based observations we used in-situ density observations from LANL geostationary satellites. Figures 3.6 and 3.7 show the effects of the assimilation.
The assimilation run was done for a 10-day period in December 2006. Figure 3.6 shows the results for the full period. The top panel shows the Kp index for the interval where the storm starting at the end of December 14 is evident. The bottom five panels show the observations and models for the five LANL geostationary satellites. In each panel the black trace is the observations. The blue trace is the model run without data assimilation. That means that the model was run using the Sojka et al. [1986] electric field model parameterized by the values in the top panel. The red traces represent the data assimilation output, in which the electric field model parameter is adjusted to maximize agreement of the model to the observations. In most cases the assimilation output agrees more closely with the observations than does the model run without data assimilation.
Figure 3.7 provides a closer look at December 18-19 with the figure having the same format at Figure 3.6. In this case it is quite clear that the assimilation output agrees better with the observations. However it is also obvious that there still are significant differences. In this plot the estimated standard uncertainty on the model output is also included as the thinner red traces surrounding the central thicker trace. In almost all cases the uncertainty range is underestimated compared to the difference between assimilation output and observations, perhaps because the electric field model in this case is not sufficiently flexible to represent real magnetospheric electric fields.
6. Electric field models
The electric potential is the driver of the plasmasphere model and the free adjustable parameters are parameters to the electric field model. Thus the electric field model must be flexible enough to represent the electric fields expected in the plasmasphere, but also do so with a relatively small number of parameter because of the limited number of observations available. A good compromise appears to be the harmonic function sets used in the earlier versions of the Weimer models. In Figure 3.8 is the equation for the Weimer potential patterns, an illustration of the radial functions, and an example of a potential pattern derived from the model. In our use of the model the parameters of the Weimer model are freely adjustable, and depending on the degree of flexibility required and the amount of data available we can elect to take the model to lower or higher order.
7. Observations
The two primary sources of observations for this project were the AWDANet VLF electron density observations and the EMMA ULF FLR mass density observations. Figure 3.9 shows one example of a EMMA data set. There are several prominent feature visible in the data sets. Firstly, the FLR density measurements are only available during the daytime, because that is the time when the ionosphere is sufficiently strong to reflect the waves. Secondly, on each day an increasing density trend is apparent. This is the refilling of the plasmasphere. The second feature is the depletion of plasma during strong convection intervals. The start of such convection intervals is illustrated as the vertical red lines.
9. Events
During the project we analyzed a number of different events. Here we will discuss one event, the storm event surrounding 15 July 2012. The Dst index for the event is shown in Figure 3.10. There was a drop in Dst at the start of 15 July 2012, reaching a peak of almost -100 nT. We collected data for data assimilation during a 10-day interval surrounding the storm onset, and processes them through data assimilation. Figure 3.11 shows the data coverage for the event. On the horizontal scale is time and on the vertical scale are each for the different data sets. Red indicates that data are available and white that no data are available.
The data are used to constrain the plasmasphere model and the results are a series of plasma density maps, and the determination of the parameters for the electric potential model which generates those plasma density maps. Figure 3.12 shows the input data compared with the assimilation output sampled at the same location. The green curves are the reference DGCPM model run without assimilation. The blue curves are the observations. The red curves are the assimilation mean and standard deviation densities.
From the density maps we can derive the location of the plasmapause. Figure 3.13 shows the plasmapause location derived from the assimilation results and from a non-assimilative model run. The black and grey lines are the median and range, over MLT, of the location of the plasmapause for the non-assimilative model run. The red lines are the corresponding curves for the assimilation run. The sharp drop at the start of the plot on 12 July is simple a startup effect of the model.
WP4. Modeling REP losses in radiation belts based on AARDDVARK network
Objectives:
4.1 Extend AARDDVARK network to have a better L-shell, MLT coverage.
4.2 Analyse the characteristics of REP, case by case, and at different L-shells.
4.3 Develop a model which identifies the size, location, MLT zone, geomagnetic conditions, and flux
characteristics of the REP.
4.4 Refine the REP model to describe on/inside/outside plasmapause precipitation using input from the WP3 model.
Description of work
Work progress and achievements during the period
Summary of progress towards objectives and details
Three new AARDDVARK stations were installed at Seattle (USA), Ottawa and St Johns(Canada) in Years 1 and 2. This objective was met.
Seven papers have been accepted or published within the workpackage which identify and characterise energetic electron precipitation which is caused by processes occurring inside/on/outside the plasmapause. The precipitation characteristics of all three regions were identified, and characterised. Three additional papers are being prepared and will be submitted in the future. This objective was met.
Development of two REP models was completed. One model produces an MLT-L map of electron precipitation using a plasmapause model to define the locations where precipitation occurs. The REP model was built on the output of the work to characterise REP. The REP model can produce a movie of how the precipitation changes during geomagnetic storms, or provide numerical data output to the user. This objective was met.
A second REP model was produced that uses the assimilated plasmasphere output of WP3 to identify the location the plasmapause, and thus define the locations where precipitation occurs. This REP model has been made available to WP3, and both REP models are held within the PLAMSON archive directory. This objective was met.
The initial phase of WP4 was to install and setup three new AARDDVARK sites by month 24. The requirement was to place AARDDVARK receivers at sites that had scientifically useful subionospheric paths from transmitter to receiver, the possibility of good signal to noise measurements, somewhere to safely house the equipment, and a reliable internet connection to return the data back to BAS. The sites chosen were Forks near Seattle hosted by Prof Holzworth at University of Washington, Ottawa, and St Johns in Canada hosted by Natural Resources Canada (NRCan). These sites all provided some quasi-constant L-shell paths from nearby VLF transmitters, thus providing good opportunities to investigate electron precipitation over a narrow L-shell range. By Month 24 we had installed all three sites, and provided installation reports on each to PLASMON. Figure 4.1 (provided in the attachments) shows the current AARDDVARK receiver network in the northern hemisphere, and their potential propagation paths from nearby transmitters. As a result of these installations the AARDDVARK network was well placed to allow quasi constant L-shell data analysis, both inside and outside of the plasmaspause. The data from the three new AARDDVARK sites funded through PLASMON is automatically transferred to BAS, Cambridge, each night and archived on the web-based database. Thus it is quickly made available to all workpackage 4 team members, as well as the rest of the PLASMON project members. Thus objective 4.1 was fully met.
The objective to undertake analysis of the characteristics of REP, case by case, and at different L-shells spanned the middle and later parts of the PLASMON project. We approached this task in two ways, by investigating in detail specific case studies, and by inspection of the large POES electron precipitation dataset. The specific case studies were usually driven by ground -based AARDDVARK data, and were able to identify regions of the plasmaphere and MLT times that were significant in producing electron precipitation, such as the inner plasmasphere during the daytime. The three new AARDDVARK sites were chosen specifically to provide sub-ionospheric propagation paths that would give insight into processes going on inside the plasmapause, i.e. they we chosen to provide quasi-constant, L~3 propagation paths. In addition the new sites gave some good higher latitude propagation paths which could be used to augment the data coverage from outside of the plasmapause, as well as some very well placed paths which give plasmapause information. The latter is particularly well studied by the Iceland transmitter received at St Johns, which responds to EMIC-driven precipitation, and is quasi conjugate to the instrument suite at the BAS Antarctic base, Halley. The POES dataset analysis of electron precipitation was particularly useful in identifying the characteristics of relatively rare, localised events such as EMIC-induced precipitation close to the plasmapause. The POES database was also inspected statistically, to gain insights into highly variable precipitation conditions.
An example of the use of the AARDDVARK data in order to characterise electron precipitation was published by Simon Wedlund et al., [2014] [A statistical approach to determining energetic outer radiation-belt electron precipitation fluxes. Mea Simon Wedlund, Mark A. Clilverd, Craig J. Rodger, Kathy Cresswell-Moorcock, Neil Cobbett, Paul Breen, Donald Danskin, Emma Spanswick, and Juan V. Rodriguez – JGR Space Physics]. AARDDVARK data for a geomagnetic disturbance period in 2010 was analysed, and the resulting electron precipitation fluxes compared with POES measurement precipitation fluxes, and those calculated from a ground-based riometer located just outside the plasmapause field-line footprint. Good agreement was found throughout the study period, with the fluxes being associated with chorus waves occurring outside of the plasmapause (see Figure 4.2 in the attachments). Additionally the precipitation flux energy spectrum was described and compared with POES measurements. Both the precipitation flux and energy spectrum were shown to be consistent with a model based on the geomagnetic activity index Dst.
An example of the large POES electron precipitation dataset to characterise relatively rare, localised events such as EMIC-induced precipitation close to the plasmapause was published by Carson et al., [2013] [POES Satellite Observations of EMIC-wave driven Relativistic Electron Precipitation during 1998-2010, Bonar R. Carson ,Craig J. Rodger, Mark A. Clilverd – JGR Space Physics]. Using six satellites that carried the SEM-2 instrument package, individual half orbits between 1998 and 2010 were inspected by an automatic detection algorithm searching for EMIC-driven relativistic electron precipitation (REP). In all, 2,331 proton precipitation associated REP (PPAREP) events were identified. The majority of events were observed at L-values within the outer radiation belt (3
A similar approach was undertaken in order to determine the the flux and energy spectral gradient of the electron precipitation from plasmaspheric hiss waves and their location relative to the plasmapause. As can be seen from the form of the plasmapsheric hiss equation shown in the equations attachment, our findings suggest that the plasmaspheric hiss generates precipitation with energies of about 300 keV, and its location inside the plasmapause is constant in L-shell, and does not move with the location of the plasmapause. The POES super-posed epoch analysis suggests that plasmaspheric hiss does not generate any significant electron precipitation with energies <300 keV, which is a surprising finding, and one that will need to be studied in detail in the future. A further publication on this topic, which combines POES and AARDDVARK data at L-shells inside the plasampause, will be published after the end of the project. With this type of analysis the characteristics of electron precipitation on/inside/outside of the plasmapause during geomangetic storms was described, characterised, and proxies developed. Thus the objective 4.2 was sussessfully undertaken, and fully met.
The last two goals of the PLASMON WP4 were to develop REP models. One REP model was for generic use, and one REP model was specifically to take the output of the assimulative plasmasphere results from WP3 and use that to determine where electron precipitation should be occuring, and with what characteristics.
Key for the generic precipitation model is the establishment of the plasmapause location. To met this objective we used the O'Brien & Moldwin (2003) empirical model [referred to as “OM3”] to determine the position of the plasmapause at each MLT. However, the model code was written such that any plasmapause model could be used. The OM3 model is used to find the L-shell location of the plasmapause [Lpp], which is based on the AE index value during the study period (or real time). All precipitation is determined relative to the determined plasmapause locations.
We used the flux and energy spectrum characteristics of the chorus-driven, plasmaspheric hiss-driven, and EMIC-driven electron precipitation. Each of the wave-types were assigned a specific region of influence relative to the plasmapause location, i.e. outside the plasmapause for chorus-driven precipitation, and primarily on the dawn side of the Earth. The time variation of the precipitation characteristics was specified by using the time-varying Dst index, where the relationships between the precipitation and Dst were determined in objective 4.2. Thus the precipitation electron flux can be evaluated for each wave-type and at a given L, MLT, Dst and plasmapause location (Lpp). These fluxes are only defined within certain regions, based on the position of the plasmapause, as given by Chorus-driven precipitation from Lpp < L < 9, and 23h < MLT < 11h; Hiss-driven precipitation from 3.1 < L < 4.2 and 11h < MLT < 16h; EMIC-driven precipitation from Lpp < L < Lpp+1, and 16h < MLT < 23h.
The key features of the model precipitation can be summarised as follows. The chorus-driven precipitation follows the variation of Dst with no delay, and thus peak fluxes occur outside of the plasmapause, and at the time of the largest excursion of the Dst index. The recovery of the chorus-driven fluxes takes several days, as Dst takes several days to return to non-disturbed levels. The plasmaspheric hiss-driven precipitation as follows the variation of Dst, but with a delay of 2 days, thus the peak fluxes occur inside the plasmasphere 2 days after the time of the largest excursion of the Dst index. Hiss-driven precipitation fluxes continue after Dst has returned to non-disturbed conditions. The EMIC-driven precipitation follows the variation of Dst with no delay, however, an additional condition was imposed such that the geomagnetic index Kp had to be greater than 4 before any precipitation occurred.
The electron precipitation associated with EMIC was the least well resolved by PLASMON objective 4.2 analysis. The precipitation is described only when Kp>4, and then simply as an increasing flux as geomagnetic activity becomes more disturbed. Future work is expected to provide more detailed information on the characteristics of the precipitation from EMIC waves. In the model developed here the energy spectral gradient was assumed to be a power-law of -2.5 which is a finding that will be published with a PLASMON acknowledgement in due course, and can be refined in the model in the future. With the development of the electron precipitation model with a generic plasmapause model objective 4.3 was fully met.
In order to undertake objective 4.4 the assimilated plasmaspheric output from WP3 had to be used to determine the plasmasphere structures. For the WP3-WP4 integrated precipitation model the PLASMON WP3 assimilated plasmasphere model is scrutinised to provide the location of the plasmapause. It was found that the PLASMON WP3 plasmasphere model did not always display a 'knee' structure indicative of the plasmapause. As such, the criterion used in satellite studies of the plasmasphere of a drop in density of a factor 5 in L-shell range of 0.5 (e.g. Carpenter & Anderson (1992)) could not always be applied. Instead, a threshold value of 5 x 107 m-3 was used. This also circumnavigates problems that arise when, even for a real plasmasphere radial density profile, the plasmasphere does not genuinely have a knee-like structure. The precipitation model uses the plasmasphere density maps generated by WP3 every 20 minutes, determines the plasmapause location, finds the geomagnetic conditions, and calculates the fluxes and spectrum. Figure 4.5 provides an example of the type of output generated by the precipitation model.
As with the electron precipitation model developed in objective 4.3 the electron precipitation characteristics were defined by the results from objective 4.2. We used the flux and energy spectrum characteristics of the chorus-driven, plasmaspheric hiss-driven, and EMIC-driven electron precipitation. Each of the wave-types were assigned a specific region of influence relative to the plasmapause location, i.e. outside the plasmapause for chorus-driven precipitation, and primarily on the dawn side of the Earth. The time variation of the precipitation characteristics was specified by using the time-varying Dst index, where the relationships between the precipitation and Dst were determined in objective 4.2. Thus the precipitation electron flux can be evaluated for each wave-type and at a given L, MLT, Dst and plasmapause location (Lpp). These fluxes are only defined within certain regions, based on the position of the plasmapause, as given by Chorus-driven precipitation from Lpp < L < 9, and 23h < MLT < 11h; Hiss-driven precipitation from 3.1 < L < 4.2 and 11h < MLT < 16h; EMIC-driven precipitation from Lpp < L < Lpp+1, and 16h < MLT < 23h.
The main difference between the electron precipitation models developed in objective 4.3 and 4.4 is that the plasmapause location in specified by the output from the assimilated plasmasphere model from WP3, which takes data provided by WP1 (electron density measurements) and WP2 (ion density measurements). Both models show variations in the L-shell of precipitation regions as the location of the plasmapause changes as a result of changes in geomagnetic activity, particularly as the plasmapause moves inwards to lower L-shells during the onset and main phase of large geomagnetic storms. As objective 4.3 used a statistical model of the plasmapause, and objective 4.4 uses a data-driven plasmasphere model, it is likely that the model from objective 4.3 is more suited to a statistical interpretation of the electron precipitation pattern, while the model from objective 4.4 is more suited to a case-study, or quasi-real time analysis. With the development of the electron precipitation model with a plasmapause model provided by WP3, objective 4.4 was fully met.
WP5. Dissemination and exploitation of the results
We have published 17 papers on peer-reviewed scientific journal:
Scientific Papers
1. Whittaker, I. C., C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, The effects and correction of the geometric factor for the POES/MEPED electron flux instrument using a multi-satellite comparison, J. Geophys. Res., 2014JA020021 (in press), 2014.
2. Antel, C., A. B. Collier, J. Lichtenberger, and C. J. Rodger, Investigating Dunedin Whistlers using Volcanic Lightning, Geophys. Res. Lett., 41, doi:10.1002/2014GL060332 2014.
3. Vellante, M., M. Piersanti, and E. Pietropaolo, Comparison of equatorial plasma mass densities deduced from field line resonances observed at ground for dipole and IGRF models, J. Geophys. Res., doi: 10.1002/2013JA019568 2014.
4. Whittaker, I. C., R. J. Gamble, C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, Determining the spectra of radiation belt electron losses: Fitting DEMETER IDP observations for typical and storm-times, J. Geophys. Res., doi:10.1002/2013JA019228 2013.
5. Lichtenberger, J., M. Clilverd, B. Heilig, M. Vellante, J. Manninen, C. Rodger, A. Collier, A. Jorgensen, J. Reda, R. Holzworth, R. Friedel and M. Simon-Wedlund, The plasmasphere during a space weather event: First results from the PLASMON, J. Space Weather Space Climate, 3 (A3), http://dx.doi.org/10.1051/swsc/2013045 2013.
6. Heilig, B., and H. Lühr, New plasmapause model derived from CHAMP field-aligned current signatures, Ann. Geophys., 31, 529-539, doi:10.5194/angeo-31-529-2013 2013.
7. Heilig, B., P. R. Sutcliffe, D. C. Ndiitwani, and A. Collier, A statistical study of geomagnetic field line resonances observed by CHAMP and on the ground, J. Geophys. Res., 118, doi: 10.1002/jgra.50215 2013.
8. Carson, B. R., C. J. Rodger, and M. A. Clilverd, POES Satellite Observations of EMIC-wave driven Relativistic Electron Precipitation during 1998-2010, J. Geophys. Res., 118, 1–12, doi:10.1029/2012JA017998 2013.
9. Hendry, A. T., C. J. Rodger, M. A. Clilverd, N. R. Thomson, S. K. Morley, and T. Raita, Rapid radiation belt losses occurring during high-speed solar wind stream–driven storms: Importance of energetic electron precipitation, in Dynamics of the Earth's Radiation Belts and Inner Magnetosphere, Geophys. Monogr. Ser., vol. 199, edited by D. Summers et al., 213–223, AGU, Washington, D. C., doi:10.1029/2012GM001299 2013.
10. Merényi, L, B. Heilig, and L. Szabados, Geomagnetic Data Acquisition System developed for the PLASMON project, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp. 54-56, San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
11. Heilig B., J. Lichtenberger, M. Vellante, J. Reda, T. Raita, P. Sutcliffe, M. Váczyová, D. Herak, M. Neska, L. Merényi, A. Csontos, P. Kovács, M. Srbecky, and I. Mandic, EMMA for near real time Monitoring of the Plasmasphere, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp., 127-130 San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
12. Lichtenberger J., and Cs. Ferencz, Űridőjárási folyamatok a magnetoszférában (Space weather processes in the magnetosphere), Magyar Geofizika, 53 (3), pp.182-190 2012.
13. Kovács P., A. Csontos, B. Heilig, L. Hegymegi, L. Merényi, G. Vadász, A. Koppán, and A. Földmágnesség, A Tihanyi Geofizikai Obszervatórium (The Tihany Geophysical Observatory), Magyar Geofizika, 53. évf. 3. sz., 191-203. p. (in Hungarian), 2012.
14. Rodger, C. J., M. A. Clilverd, A. J. Kavanagh, C. E. J. Watt, P. T. Verronen, and T. Raita, Contrasting the responses of three different ground-based instruments to energetic electron precipitation, Radio Sci., doi:10.1029/2011RS004971 2012.
15. Clilverd, M. A., C. J. Rodger, D. Danskin, M. E. Usanova, T. Raita, Th. Ulich, and E. L. Spanswick, Energetic Particle injection, acceleration, and loss during the geomagnetic disturbances which upset Galaxy 15, J. Geophys. Res., 117, A12213, doi:10.1029/2012JA018175 2012.
16. Delport, B., A. B. Collier, J. Lichtenberger, C. J. Rodger, M. Parrot, M. A. Clilverd, and R. H. W. Friedel, Simultaneous observation of chorus and hiss near the plasmapause, J. Geophys. Res., 117, A12218, doi:10.1029/2012JA017609 2012.
17. Clilverd, M A, C J Rodger, I J Rae, J B Brundell, N R Thomson, N Cobbett, P T Verronen, and F W Menk, Combined THEMIS and ground-based observations of a pair of substorm associated electron precipitation events, J. Geophys. Res., 117, A02313, doi:10.1029/2011ja016933 2012.
We have presented our results on
- 20 invited oral conference presentations
- 53 normal oral conference presentations
- 39 poster conference presentations
- 28 seminars
- 8 public/popular talks and papers
- 10 media coverages
References:
Berube, D., M.B. Moldwin, and J.M. Weygand, An automated method for the detection of field line resonance frequencies using ground magnetometer techniques, J. Geophys. Res., 108, 1348, 2003.
Dowden, R.L and Allcock G. McK (1971): Determination of nose frequency of non-nose whistlers. J. Atmos. Terrestrial Phys.33 pp 1125-1129.
Green, A.W. E.W. Worthington, L.N. Baransky, E.N. Fedorov, N.A. Kurneva, V.A. Pilipenko, D.N. Shvetzov, A.A. Bektemirov, and G.V. Philipov, Alfve´n field line resonances at low latitudes
(L = 1.5) J. Geophys. Res., 98, 15693–15699, 1993.
Heilig, B., and H. Lühr, New plasmapause model derived from CHAMP field-aligned current signatures, Ann. Geophys., 31, 529-539, doi:10.5194/angeo-31-529-2013 2013
Heilig, B., S. Lotz, J. Vero, P. Sutcliffe, K.P.J. Reda, and T. Raita, Empirically modelled pc3 activity based on solar wind parameters, Ann. Geophys., 28, 1703–1722, 2010.
Heilig, B., P. R. Sutcliffe, D. C. Ndiitwani, and A. Collier, A statistical study of geomagnetic field line resonances observed by CHAMP and on the ground, J. Geophys. Res., 118, doi: 10.1002/jgra.50215 2013.
Heilig B., J. Lichtenberger, M. Vellante, J. Reda, T. Raita, P. Sutcliffe, M. Váczyová, D. Herak, M. Neska, L. Merényi, A. Csontos, P. Kovács, M. Srbecky, and I. Mandic, EMMA for near real time Monitoring of the Plasmasphere, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp., 127-130 San Fernando, Spain, 2013
Lichtenberger, J., Ferencz, Cs., Bodnár, L., Hamar, D. and Steinbach P. (2008): Automatic Whistler Detector and Analyzer (AWDA) system. Automatic Whistler Detector, J. Geophys. Res. 113, A12201, Doi: 10.1029/2008JA013467.
Lichtenberger, J. (2009): A new whistler inversion model, J. Geophys. Res., 114 A07222, Doi: 10.1029/2008JA013799
Lichtenberger, J., C. Ferencz, D. Hamar, P. Steinbach, C. J. Rodger, M. A. Clilverd, and A. B. Collier (2010), The Automatic Whistler Detector and Analyzer (AWDA) system: Implementation of the Analyzer Algorithm, J. Geophys. Res., 115 A12214, doi:10.1029/2010JA015931
Lichtenberger, J., M. Clilverd, B. Heilig, M. Vellante, J. Manninen, C. Rodger, A. Collier, A. Jorgensen, J. Reda, R. Holzworth, R. Friedel and M. Simon-Wedlund, The plasmasphere during a space weather event: First results from the PLASMON, J. Space Weather Space Climate, 3 (A3), http://dx.doi.org/10.1051/swsc/2013045 2013
Merényi, L, B. Heilig, and L. Szabados, Geomagnetic Data Acquisition System developed for the PLASMON project, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp. 54-56, San Fernando, Spain, 2013.
Singer, H.J. D.J. Southwood, R.J. Walker, and M.G. Kivelson, Alfve´n wave resonances in a realistic magnetospheric magnetic field geometry, J. Geophys. Res., 86, 4589–4596, 1981.
Tsyganenko, N.A. A model of the near magnetosphere with a dawndusk asymmetry 1. Mathematical structure, J. Geophys. Res., 107 (A8), SMP 12-1–SMP12-5, 2002a.
Tsyganenko, N.A. A model of the near magnetosphere with a dawndusk asymmetry 2. Parameterization and fitting to observations, J. Geophys. Res., 107 (A8), SMP 10-1–SMP 10-17, 2002b.
Vellante, M., and M. Förster, Inference of the magnetospheric plasma mass density from field line resonances: a test using a plasmasphere model, J. Geophys. Res., 111, A11204, 2006.
Vellante, M., H. Lühr, T.L. Zhang, V. Wesztergom, U. Villante, M. DeLauretis, A. Piancatelli, M. Rother, K. Schwingenschuh, W. Koren, and W. Magnes, Ground/satellite signatures of field line resonance: a test of theoretical predictions, J. Geophys. Res., 109, A06210, 2004.
Vellante, M., M. Piersanti, and E. Pietropaolo, Comparison of equatorial plasma mass densities deduced from field line resonances observed at ground for dipole and IGRF models, J. Geophys. Res., doi: 10.1002/2013JA019568 2014.
Waters, C.L. F.W. Menk, and B.J. Fraser, The resonance structure of low latitude pc3 geomagnetic pulsations, Geophys. Res. Lett., 18, 2283–2296, 1991.
Potential Impact:
WP1
Extended AWDANet:
The spatial and time occurrences of whistler events and whistler traces will be used to further investigate the whistler generation and propagation as well as the formation of plasmaspheric ducts. Thus will lead to optimal selection of further AWDANet locations.
Automatic Whistler Analayzer:
The palsmaspheric densities provided by the real-time mode of operation will be used in modeling wave-particle interactions in radiation belts. The cold density data can also be used for modeling-forecasting spacecraft charges.
We will process all the archive whistler data collected by AWDANet since 2002 creating a unique dataset of plasmaspheric equatorial electron densities that can be used in space weather model development and case studies.
The real-time data is planned to be used (together with plasma mass densities from WP2) in data assimilation model of the plasmasphere after the end of the project.
WP2
FLRID and FLRINV codes for monitoring the plasmasphere by means of FLRs:
FLRID and FLRINV codes can be applied to data recorded in another longitude sectors, too. In a case study our codes were used to process McMAC (USA) and CARISMA (Canada) data recorded in the American longitude sector. The adjustment of the FLRID parameters for the local conditions is a prerequisite in such a case, however.
In the future we plan collaborations with the above mentioned and SAMBA magnetometer arrays to extend the monitoring facility to a global one, at least for some case studies.
In an ideal case at least one magnetometer chain should be exposed to the compressional waves entering the magnetosphere at the subsolar point of the terrestrial bow shock. With such a global network (including MAGDAS stations in the Japan-Australian sector) the continuous global monitoring of the dayside plasmasphere could be achieved.
EMMA and SANSA magnetometer data:
The 1 s magnetic data recorded along close to the same magnetic meridian during the PLASMON period form a unique data set, especially in Europe. This dataset makes possible the investigation of different natural phenomena (processes in the Earth interior, currents in the ionosphere and magnetosphere, ULF waves and their interaction with the ionosphere) especially when combined with independent data sources (e.g. in-situ satellite magnetic and wave measurements) as well as their meridional distributions.
Direct use of EMMA and SANSA data is inside the PLASMON consortium. These data are used to monitor the plasma mass density in the plasmasphere and in the plasma trough.
*EMMA data (or part of the chain) are also used in other projects (e.g. EU FP7 STORM, ESA SWARM EMISSARS). We expect a lot of users of our data from different fields: geomagnetism, exploration for raw materials, global modeling of the geomagnetic field, space physics, education, etc. EMMA and SANSA has been completed in good time to support ESA’s SWARM mission (magnetic field modeling, current systems, ULF waves, ionosphere etc.) and NASA’s VAP mission (plasmasphere, radiation belts).
To reach the widest community we make all our data at 1 min resolution openly available through the SuperMAG consortium (http://supermag.jhuapl.edu/).
For the sake of the ULF wave research community we also make all the 1s data available on the EMMA servers (e.g. at http://plasmonserver.aquila.infn.it/) where also the ‘Rules of Road’ of the data policy are being defined. The availability of archived data refreshed daily can be checked at http://geofizika.canet.hu/plasmon/emmaavailarchy.php.
Equatorial plasma mass density data:
Equatorial plasma mass density data inferred by FLRID and FLRINV from EMMA and SANSA magnetometer data are the main product of WP2. WP3 uses these data directly as an input of a data assimilative model of the plasmasphere. Our equatorial plasma mass densities are obtained from daytime FLR observations made in the European-African sector. Data are available for the L range 1.4-6.4 from 2012 depending on the availability and quality of the corresponding magnetometer data and also on ULF wave activity for the period in question.
We plan to use the density to monitor the state of the plasmasphere, to obtain average ion mass estimates, to develop empirical as well as data assimilative models of the plasmasphere.
An important product derivable from radial mass density profiles is the position of the plasmapause. To support this goal we are developing alternative ways to obtain the plasmapause position from EMMA data as well as from magnetic observations made by SWARM at low-Earth-orbit.
Validation/calibration activity will be continued making use of the availability of VAP in-situ observations in the plasmasphere and in the plasma trough.
Our approach offers a cheap and efficient way to monitor the dynamics of the plasmasphere using only ground observations. Our data are published on the L’Aquila PLASMON EMMA Server:
http://plasmonserver.aquila.infn.it/EMMA_FLR_DENSITY
PLASMON magnetometer data loggers
The magnetic data acquisition system developed by MFGI could be used at any geomagnetic observatory or variation station to produce 1 s data fulfilling the requirements of INTERMAGNET (www.intermagnet.org).
WP3:
The plasmasphere is a critical component of the magnetosphere ionosphere system where it is a source of wave activity responsible for decay and acceleration of radiation belt particles, an important component of “space weather." Space weather in turn threatens both ground-based and space-based technological infrastructure.
Mitigating the eff ects of space weather requires reliable forecasts of the sate of the radiation belt and inner magnetosphere. Waves play an important role in both the acceleration and decay of radiation belt particles. The thermal plasma in the plasmasphere determines resonance conditions for particles, instability criteria for waves generation, and propagation characteristics of waves. Boundary gradients such as the plasmapause separate distinct regions of wave activity, and thus control the location of dominant loss or acceleration mechanisms due to these waves. The plasmasphere is thus one of the most signi ficant drivers of radiation belt activity, yet is the least constrained in recent inner magnetosphere modeling efforts.
Current plasmasphere models implement the essential features of the plasmasphere such as ionospheric sources and sinks of plasma as well as the convective transport. But an important piece is unknown, and that is the detailed form and strength of the convection electric fi eld. It appears that our current knowledge of the electric field is insuffi cient to produce the most reliable plasma density maps needed as inputs to radiation belt models. The solution is to use data assimilation to constrain the evolution of the convection electric field and thus the plasma density distribution with observations. This work will have a direct impact on the accuracy of radiation belt models because it will improve the accuracy of some critical driver inputs to radiation belt models. Several groups which do radiation belt modeling have been in contact with us to use the PLASMON assimilative plasmasphere model to improve the accuracy of their predictions.
Main dissemination activities:
The data assimilation model and related results have been presented at a number of conferences. A journal publication is in the process of being prepared. The data assimilation model code is available for download from the PLASMON web site. We are also in the process of creating a website which will produce real-time plasma density maps from real-time PLASMON data. We have begun discussions with radiation belt modeling groups about using the PLASMON assimilation model for their plasmasphere input
Explanation of results:
We have designed and implemented a data assimilation framework for plasmasphere data assimilation. This framework will accept any measurements which can be used to constrain the plasmasphere. The data assimilation and model combination has produce a new, improved model of the plasma con guration in the inner magnetosphere. This model can be run with, in principle, any future data sets to produce a better plasma density map than existing non-assimilative models.
WP4
Extended AARDDVARK:
The extended AARDDVARK network is able to detect the precipitation of high energy electron from the outer radiation belts in the Northern Hemisphere. The detected precipitations can be used to validate precipitations predicted by the radiation belts models.
REP models:
We have developed numerical algorithms to calculate the Relativistic Electron Precipitation fluxes at three regions (outside/inside and on the plasmapause) thus algorithm can be used to predict the REP and its effects in operational space weather application and services.
The plasmasphere models from data assimilation runs will be used in the future together with the REP flux algorithm. The precipitation model uses the plasmasphere density maps generated by WP3 every 20 minutes, determines the plasmapause location, finds the geomagnetic conditions, and calculates the fluxes and spectrum.
WP5
Dissemination of the results:
Scientific Papers
1. Whittaker, I. C., C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, The effects and correction of the geometric factor for the POES/MEPED electron flux instrument using a multi-satellite comparison, J. Geophys. Res., 2014JA020021 (in press), 2014.
2. Antel, C., A. B. Collier, J. Lichtenberger, and C. J. Rodger, Investigating Dunedin Whistlers using Volcanic Lightning, Geophys. Res. Lett., 41, doi:10.1002/2014GL060332 2014.
3. Vellante, M., M. Piersanti, and E. Pietropaolo, Comparison of equatorial plasma mass densities deduced from field line resonances observed at ground for dipole and IGRF models, J. Geophys. Res., doi: 10.1002/2013JA019568 2014.
4. Whittaker, I. C., R. J. Gamble, C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, Determining the spectra of radiation belt electron losses: Fitting DEMETER IDP observations for typical and storm-times, J. Geophys. Res., doi:10.1002/2013JA019228 2013.
5. Lichtenberger, J., M. Clilverd, B. Heilig, M. Vellante, J. Manninen, C. Rodger, A. Collier, A. Jorgensen, J. Reda, R. Holzworth, R. Friedel and M. Simon-Wedlund, The plasmasphere during a space weather event: First results from the PLASMON, J. Space Weather Space Climate, 3 (A3), http://dx.doi.org/10.1051/swsc/2013045 2013.
6. Heilig, B., and H. Lühr, New plasmapause model derived from CHAMP field-aligned current signatures, Ann. Geophys., 31, 529-539, doi:10.5194/angeo-31-529-2013 2013.
7. Heilig, B., P. R. Sutcliffe, D. C. Ndiitwani, and A. Collier, A statistical study of geomagnetic field line resonances observed by CHAMP and on the ground, J. Geophys. Res., 118, doi: 10.1002/jgra.50215 2013.
8. Carson, B. R., C. J. Rodger, and M. A. Clilverd, POES Satellite Observations of EMIC-wave driven Relativistic Electron Precipitation during 1998-2010, J. Geophys. Res., 118, 1–12, doi:10.1029/2012JA017998 2013.
9. Hendry, A. T., C. J. Rodger, M. A. Clilverd, N. R. Thomson, S. K. Morley, and T. Raita, Rapid radiation belt losses occurring during high-speed solar wind stream–driven storms: Importance of energetic electron precipitation, in Dynamics of the Earth's Radiation Belts and Inner Magnetosphere, Geophys. Monogr. Ser., vol. 199, edited by D. Summers et al., 213–223, AGU, Washington, D. C., doi:10.1029/2012GM001299 2013.
10. Merényi, L, B. Heilig, and L. Szabados, Geomagnetic Data Acquisition System developed for the PLASMON project, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp. 54-56, San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
11. Heilig B., J. Lichtenberger, M. Vellante, J. Reda, T. Raita, P. Sutcliffe, M. Váczyová, D. Herak, M. Neska, L. Merényi, A. Csontos, P. Kovács, M. Srbecky, and I. Mandic, EMMA for near real time Monitoring of the Plasmasphere, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp., 127-130 San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
12. Lichtenberger J., and Cs. Ferencz, Űridőjárási folyamatok a magnetoszférában (Space weather processes in the magnetosphere), Magyar Geofizika, 53 (3), pp.182-190 2012.
13. Kovács P., A. Csontos, B. Heilig, L. Hegymegi, L. Merényi, G. Vadász, A. Koppán, and A. Földmágnesség, A Tihanyi Geofizikai Obszervatórium (The Tihany Geophysical Observatory), Magyar Geofizika, 53. évf. 3. sz., 191-203. p. (in Hungarian), 2012.
14. Rodger, C. J., M. A. Clilverd, A. J. Kavanagh, C. E. J. Watt, P. T. Verronen, and T. Raita, Contrasting the responses of three different ground-based instruments to energetic electron precipitation, Radio Sci., doi:10.1029/2011RS004971 2012.
15. Clilverd, M. A., C. J. Rodger, D. Danskin, M. E. Usanova, T. Raita, Th. Ulich, and E. L. Spanswick, Energetic Particle injection, acceleration, and loss during the geomagnetic disturbances which upset Galaxy 15, J. Geophys. Res., 117, A12213, doi:10.1029/2012JA018175 2012.
16. Delport, B., A. B. Collier, J. Lichtenberger, C. J. Rodger, M. Parrot, M. A. Clilverd, and R. H. W. Friedel, Simultaneous observation of chorus and hiss near the plasmapause, J. Geophys. Res., 117, A12218, doi:10.1029/2012JA017609 2012.
17. Clilverd, M A, C J Rodger, I J Rae, J B Brundell, N R Thomson, N Cobbett, P T Verronen, and F W Menk, Combined THEMIS and ground-based observations of a pair of substorm associated electron precipitation events, J. Geophys. Res., 117, A02313, doi:10.1029/2011ja016933 2012.
We have presented our results on
- 20 invited oral conference presentations
- 53 normal oral conference presentations
- 39 poster conference presentations
- 28 seminars
- 8 public/popular talks and papers
- 10 media coverages
Education
Both the data and the know-how developed in frame of PLASMON have already been used in university courses and as research topic of PhD students (University of L’Aquila, Italy; Eötvös University, Hungary, University of Otago, New Zealand and New Mexico Tech, NM, USA).
List of Websites:
http://plasmon.elte.hu
The security of space assets are affected by the high-energy charged particle environment in the radiation belts. The controlling principal source and loss mechanisms in the radiation belts are not yet completely understood. During a geomagnetic storm the length of time during which space assets are in danger is determined by the loss mechanisms, particularly by relativistic electron precipitation.
The primary mechanism for this precipitation is the interaction of several wave modes with resonant electrons which leads to scattering into the atmospheric loss cone.
The nature of the wave activity and the interactions between the waves and radiation belt particles are strongly governed by the properties of the plasmasphere. At this point there are few existing and regular measurements of plasmaspheric properties, with existing plasmaspheric models lacking the structures known to exist in the real plasmasphere. There is evidence that enhanced wave activity and enhanced radiation belt losses occur due to such structures. In addition, there are large uncertainties concerning the fundamental nature of relativistic electron precipitation (REP), due to the difficulties of undertaking quality in-situ measurements. To address these uncertainties in this proposed project we will provide regular longitudinally-resolved measurements plasmaspheric electron and mass densities and hence monitor the changing composition of the plasmasphere, one of the properties which determines wave growth. This will allow us to develop a data assimilative model of the plasmasphere. At the same time, we will monitor the occurrence and properties of REP, tying the time-resolved loss of relativistic electrons to the dynamic plasmasphere observations. Our approach will primarily use ground-based networks of observing stations, operating in the ULF and VLF ranges, deployed on a worldwide level.
Major objectives:
1. Automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler Detector and Analyzer Network (AWDANet)
2. Retrieval of equatorial plasma mass densities by South European GeoMagnetic Array (SEGMA) and Magnetic Meridian 100 (MM100) magnetometer arrays and cross-calibration of whistler and Field Line Resonances (FLR) method
3. Data assimilative modeling of the Earth’s plasmasphere
4. Modeling Relativistic Electron Precipitation Losses from the radiation belts using the Antarctic-Arctic Radiation-belt (Dynamic) Deposition – VLF Atmospheric Research Konsortia (AARDDVARK) network
Achieved results:
- We have setup the quasi real-time mode of operation at 15 AWDANet nodes.
- We have setup 4 new AWDA stations (Eskdalemuir, Scotland; Forks, SE, USA, Karymshina, Kamchatka, Russia; and Nainital, India)
-Seven new magnetometer stations (two in Poland , one in Lithuania, one in Slovakia, one in Croatia, and two in Namibia) have been installed
- The FLRID code for the detection of FLRs has beeen developed.
- The FLRINV code which infers the equatorial plasma mass density from the field line eigenfrequencies obtained by FLRID has been developed and works in quasi real-time mode of operation.
- The whistler and FLR densities have been calibrated with in-situ (IMAGE and RBSP) data
- We were building the data assimilation framework.
- We have started running the plasmasphere model.
- We have made initial comparisons between the plasmasphere model and observations.
- We have built an Ensemble Kalman Filter based data assimilation framework, and begun to use it with in-situ observations.
- We have started to use the data assimilation model with PLASMON observations
- We have started to produce plasma density maps.
- We have created a set of instruction that will allow non-developers to run the assimilation code
- Three new AARDDVARK station has been installed at Forks (Seattle, USA) at Ottawa and St Johns (Canada).
- - Development of two REP models was completed. One model produces an MLT-L map of electron precipitation using a plasmapause model to define the locations where precipitation occurs. The REP model was built on the output of the work to characterise REP. The REP model can produce a movie of how the precipitation changes during geomagnetic storms, or provide numerical data output to the user.
- A second REP model was produced that uses the assimilated plasmasphere output of WP3 to identify the location the plasmapause, and thus define the locations where precipitation occurs.
- 92 conference presentations: 53 oral presentations (of which 20 were invited), 39 posters
- 17 papers published (plus 1 in review)
- 10 stories in local media, 28 seminars and 8 public talks
Project Context and Objectives:
All space weather models and forecasting methods are dependent on data input for either boundary conditions or the specification of parameters needed by the model. These data at best come from in-situ observations or a statistical model parametrized by some geomagnetic indices, and at worst simply “guessed” to be some representative value.
In-situ observations (satellite measurements) suffer from inherent weaknesses. One, very few platforms give comprehensive measurements of particles, waves and fields. Two, the data availability is very often limited in space and time; at best there will be a handful of observations of a given parameter at any given time throughout all of geospace. Three, with very few exceptions (GOES data), data are not generally available in real or even near-real time, limiting their use for forecasting. Four, the high costs of satellite fabrication and launch make it unlikely that these limitations will be overcome any time soon.
However, there is a complementary or alternative approach to provide data sources for space weather models: ground based measurements. Clearly the combination of ground and space based measurements would provide the best result, but the ground based measurements have several advantages over the space based ones. Generally, they are cheap, and they can produce continuous temporal and spatial coverage. As they generally have access to the Internet providing real-time data presents few problems.
In this project we have developed ground based systems to provide key parameters used in comprehensive radiation belt models that are unlikely to ever be provided by space-borne systems, but are vital for any realistic radiation belt model.
One of the most significant hazard to Earth-orbiting satellites is posed by high fluxes of relativistic electrons. These fluxes contribute to the total radiation dose to the satellite and thus its lifetime, or to deep-dielectric charging, where penetrating electrons gives rise to potential differences, which in turn can lead to intense voltage discharges and surges of electric energy deep inside the electric circuits of the spacecraft – causing severe damage to various subsystems. Such discharges can produce short-lived (fractions of a microsecond) but intense (several Amperes) current pulses.
The temporal evolution of trapped relativistic electron fluxes in the radiation belts is highly dynamic and poorly understood, and is currently topic of intense scientific research which has led to the development of several radiation belt models (the Los Alamos DREAM project, the ONERA Salammbo code, the UCLA radiation belt code, the NERC-BAS radiation belt code).
Reeves [1998] found that geomagnetic storms produce all possible responses in the outer belt flux levels, i.e. flux increases (53%), flux decreases (19%), and no change (28%). The dynamics of these particles is the result of a complex interplay of acceleration, loss and transport processes; and for all these processes the underlying mechanism has a strong dependence on the distribution of the overlapping background cold plasma in the plasmasphere: Acceleration and loss are due to resonances with variety of plasma waves – both the generation of these waves and the resulting resonance conditions depend on the ambient plasma density and composition. Transport is due to resonances with ULF wave modes, which depend on mass loading of field lines and thus also on the ambient plasma density.
The plasmasphere itself is also a dynamic region being permanently influenced by the region below (ionosphere) and above (outer magnetosphere) and is controlled by the relative intensities of the solar wind-imposed electric field and the co-rotation electric field. The plasmasphere plays a central role in magnetosphere-ionosphere dynamics. Apart from hosting the waves which are responsible for the acceleration, decay and transport of radiation belt particles, the plasmasphere also plays an important role in spacecraft charging effects, and it is a significant contributor to TEC which contributes to GPS inaccuracies and communications problems. At the simplest level the plasmasphere is controlled by three factors: a global convection electric field, outflow/inflow from/to the ionosphere, and diffusive equilibrium. Therefore the dynamics of the plasmasphere requires monitoring, modeling and forecasting. Fundamental parameters of the plasmasphere are the plasma distribution, density and composition. To obtain this distribution, we will use a combination of ground based networks, and ground based techniques, in combination with a data-assimilative model of the plasmasphere.
One network (AWDANet) measures Very Low Frequency (VLF) waves to capture and analyze whistlers, another network (EMMA) measures Ultra Low Frequency (ULF) signals to capture and analyze Field Line Resonances (FLR). Methods based on the two phenomena are capable of providing plasmaspheric densities. The two methods are complementary to each other due to the spatial and temporal occurrences of whistlers and FLRs. The data from the two ground based network are fed to the data-assimilative modeling of the Earth’s plasmasphere, to provide a hi-fidelity model. The models identifying electron loss to the atmosphere from the different regions of the plasmasphere demonstrate one application of the new plasmasphere model in providing value added information on the loss processes for use in radiation belts models making use of measurements by a third ground based network (AARDDVARK)
Objective 1: Automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler detector and Analyzer Network (AWDANet)
The cold electron density distribution of plasmasphere cannot be easily measured routinely, but is a key parameter for modeling of the plasmasphere and radiation belts. Whistlers have been regarded as cheap and effective tools for plasmasphere diagnostic since the early years of whistler research, but it never became a real operational tool since “reducing” whistler data to equatorial densities was very labor intensive. Recently the Space Research Group of Eötvös University has developed a new, experimental Automatic Whistler Detector and Analyzer (AWDA) system that is capable of detecting whistlers and we plan to use this system to process lightning whistlers with no human interaction. A network formed by AWDA systems (AWDANet) is evolving and now covers low, mid and high magnetic latitudes. A recent developments in whistler inversion methods for multiple-path whistler groups propagating on mid and high latitude will allow us to retrieve electron density profiles automatically for wide range of L-values, with low latitude whistlers practically covering the whole plasmasphere. The AWDANet will be extended to have better spatial and temporal coverage and thus will be able to provide density profiles for different MLTs which can be used as a data source for space weather models. We will
- extend the AWDANet to have better MLT and latitudinal coverage,
- develop an automatic whistler analyzer (AWA) method based on our new whistler inversion method,
- implement the AWA in AWDANet nodes and
- develop AWDANet to work in quasi-real-time mode of operation.
Objective 2: Retrieval of equatorial plasma mass densities by SEGMA and MM100 magnetometer arrays and cross-calibration of whistler and FLR method
Thanks to the recent developments in magnetometry (reduction of noise), data acquisition (resolution and timing) and the theory (wave propagation, event detection, models, inversion) of magnetohydrodynamic (MHD) waves, the routine monitoring of the cold plasma mass density of the plasmasphere became possible. Although the preparation of such monitoring systems in Europe has started, the efforts have so far been made separately in different countries. University of L’Aquila established the SEGMA (South European GeoMagnetic Array), , while Eötvös Loránd Geophysical Institute (Hungary) initiated the MM100 array, both in 2001. One of the main goals of both arrays was to monitor the plasmaspheric mass density based on the detection of geomagnetic field line resonances (FLRs). None of these ‘monitoring’ systems, however, became operational in the sense that they never produced quasi real time products.
Data are transferred a few times a year, and processed on a non-regular basis. The latitude coverage is also not sufficient to monitor the whole plasmasphere. In contrast to the whistler method the FLR method can be used to infer the plasma mass density even in the plasmatrough and to also identify the location of the plasmapause. In the context of the current project we plan to unify the isolated European efforts to call into being a joint European network, EMMA (European quasi-Meridional Magnetometer Array,) with stations ranging from Italy to the northern Finland (L-shells 1.6 – 6.7).
We intend to use and upgrade existing magnetometer networks (IMAGE), which were originally established for other purposes and other requirements (resolution, sampling rate, timing), but the data of which can be exploited for plasmasphere observations, as well. In accordance with these goals we will
1. unify and extend the SEGMA, MM100 and IMAGE networks into EMMA (including stations in Southern Africa maintained by HMO) to have better latitudinal coverage,
2. develop an automatic FLR identification (FLRID) method based on previous experience and recent improvements,
3. develop an automatic FLR inversion (FLRINV) method based on most recent achievements including error estimations,
4. develop all EMMA stations to work in quasi-real-time mode of operation,
5. evaluate relative abundances of heavy ions in the plasma composition from simultaneous determinations of mass density (FLR method) and electron density (whistler method)
Objective 3: Data assimilative modeling of the Earth’s plasmasphere
Even dense measurements only sample the plasmasphere at limited resolution in both space and time. For example, FLR data only provide measurements during the daytime, and only at the local time of the observatory pairs, whereas whistlers provide the best measurements during the night time, and again only at the MLT and L-shells of the observatories. The same restrictions apply to, and are usually more severe, in the case of satellite measurements. Yet determining the effect of wave-particle interactions on the radiation belts require a continuous map of the plasma density in both time and space. In order to provide such a complete map it becomes necessary to interpolate between measurements, again in both time and space. A good approach to this interpolation is data assimilation. The basic idea behind data assimilation is the combination of a physical model of the system with observations relevant to constraining the physical system. The most sophisticated data assimilation schemes preserve the internal physical consistency of the model while matching it optimally to the data, in time and space. This combination of observation and a physical model should, in theory, perform better than either by itself. This is actually the case, as demonstrated by many examples ranging from radar tracking of aircraft to routine weather forecasting. A good data assimilation scheme essentially fits a time-dependent model to a time-series of uncertain observations. It can be visualized as a feedback system in which the measurements provide error signals, which adjust free, or poorly determined, inputs to the model. In the case of the plasmasphere these include the electric field, the refilling and loss rate, and possibly some composition information. Specifically this is carried out by simulating the measurements from the model and comparing those to the observations, taking into account complicated relationships such as the fact that future measurements can constrain past drivers of the model.
At New Mexico Tech we are working with data assimilation schemes to combine plasmaspheric measurements with a numerical physics-based plasmasphere model. The two data assimilation schemes which we are pursuing are Ensemble Kalman filtering and particle filtering. As part of this project we will develop the necessary means to incorporate FLR measurements and whistler measurements into the assimilation scheme. There are two approaches to this, and we will pursue both. The simplest approach is to estimate equatorial and/or field-line-integrated mass and electron density from the measurements using a nominal model. This will be the simplest to incorporate as it just requires matching the model plasma densities to those measurements. A more involved approach computes the FLR frequencies from the model using first principles or simplified solvers. In the case of FLR measurements it requires solving an eigenvalue equation, and in the case of the whistler measurements it requires estimating the whistler parameters. We will also undertake incorporating composition information into the model. When the composition changes slowly over time it is possible to estimate it through the data assimilation even when there is only limited overlap between FLR and whistler measurements.
Objective 4: Modeling REP losses from the radiation belts using the AARDDVARK network
During a geomagnetic storm the length of time during which space assets are in danger is determined by the efficiency of the loss mechanisms, particularly through relativistic electron precipitation into the atmosphere. The primary mechanism for this precipitation is the interaction of several wave modes with resonant electrons, which leads to scattering into the atmospheric loss cone. The nature of the wave activity and the interactions between the waves and radiation belt particles are strongly governed by the properties of the plasmasphere. In this work package we will use the assimilative model of the plasmasphere to identify regions where plasmaspheric structures such as the regions occurring on, inside, and outside of the plasmaspause and/or composition changes are likely to result in enhanced electron losses. We will monitor the occurrence and properties of REP using the ground based AARDDVARK network.
There is evidence that different wave activity and varying radiation belt losses occur due plasmaspheric structures. For processes that occur inside of the plasmapause we would anticipate that plasmaspheric hiss would be the dominant loss mechanism, while outside the plasmapause chorus would be expected to dominate. In addition electro-magnetic ion-cyclotron (EMIC)-driven precipitation associated with the plasmaspause itself can lead to intense bursts of very strong relativistic precipitation from the radiation belts as we have previously shown, and could provide radiation dose hazards for astronauts in (very) low Earth orbit. We will use the AARDDVARK data to determine the electron precipitation flux levels that are associated with the different regions of the plasmasphere, identifying subionospheric transmitter-receiver propagation paths that are influenced solely by processes that occur either inside, or outside of the plasmaspause. In order to achieve this aim we would map the regions of plasmaspheric structures into the Earth-ionosphere waveguide in order to know which part of the AARDDVARK network they would be monitored by; plan to build up the AARDDVARK network to provide additional receiver pairs located such that they combine together to monitor a constant L-shell; include data from riometers and pulsation magnetometers in case studies; and ultimately develop a REP loss module to add on to the plasmasphere model including an indication of radiation dose hazard. This analysis would rely on development of quasi-constant L-shell monitoring paths in the AARDDVARK network.
At the end of the project we will provide real time data of plasmaspheric densities, a data-assimilative model of the plasmasphere and a model of Relativistic Electron Precipitation (REP) losses. All these data, models and information will significantly contribute to European capacity to estimate and prevent damage of space assets from space weather events as well as to improving forecasting and predicting of disruptive space weather events.
The data, models and forecasting capabilities will be available for European and international actors in the field.
No such data and models are available to date, neither separately nor as a complex service or method which we will develop in the project.
As security of space assets from space weather events is a global challenge and thus difficult to confine any activity into the EU, therefore – completely conforming to the call -, we will involve partners from other space-faring nations (USA, New Zealand, and South Africa) as well as IPCP partner (South Africa). Without their specific expertize or the utilization of their special geographic (geomagnetic) location(s), the project objectives cannot be achieved.
ESA SSA preparatory programme – which is in the middle of its first phase – is currently identifying European Space Weather assets, operational services; services and methods that are potentially operational. No complex service or method such as proposed here is currently in the list of space weather-related assets of ESA.
Project Results:
In PLASMON, we had four workpackages on S&T and on for dissemination. The results achieved is presented through the WPs. The figures referred in the text are in the attached pdf file.
WP1. Automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler detector and Analyzer Network (AWDANet)
The general objective of WP1 was the automatic retrieval of equatorial electron densities and density profiles by Automatic Whistler Detector and Analyzer Network (AWDANet). The obtained electron densities, together with plasma mass densities from WP2 and in situ measurements from satellites, were fed into the data assimilative model of the plasmasphere (WP3) to construct a dynamical model of the Earth’s plasmasphere. The specific objectives of WP1 were the following:
1. extend the AWDANet to have better MLT and latitudinal coverage,
2. develop an automatic whistler analyzer (AWA) method based on our new whistler inversion method,
3. implement the AWA in AWDANet nodes and
4. develop AWDANet to work in quasi-real-time mode of operation.
Main results of WP1
Objective 1. Extension of AWDAnet
We have extended the AWDANet with three new stations:
1. Eskdalemuir, Scotland
In cooperation with British Geological Survey, the Scotland AWD has been installed in BGS observatory at Eskdalemuir (Latitude: 55.31 Longitude: 356.80 L=2.72)
2. Karymshina, Kamchatka, Russia
In cooperation with Institute of Cosmophysical Researches and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences we have installed the AWDA station in Karymshina (Latitude: 53.04 Longitude: 158.70 L=2.13).
3. Forks, WA, USA
The third AWDA station was installed in the premises of University of Washington at Forks, near Seattle, USA (Latitude: 47.93 Longitude: 235.61 L=2.76)
The map of the extended AWDANet including the three new stations is on Figure 1.1
Objective 2. Development of an automatic whistler analyzer (AWA) method
The whistler rate is 0.5–2 scalable events per hour for low‐activity regions and 40–140 scalable events/hour for the highest‐activity region. On the other hand, there are days at the Antarctic Peninsula when whistlers are literally observed in each second, such that 180–800 scalable events/hour are expected. In contrast, the highest rate at low‐activity regions is around 5000 traces per day [Lichtenberger et al., 2008; Collier at al., 2009; Rodger et al., 2009, Collier at al., 2010; Collier at al., 2011], thus the estimated scalable whistler rate is 10–40 per hour on those days. Taking into account the time scale of variations in plasmaspheric electron densities (it is on the order of hours), which can be attributed either to MLT variation at a given location or to the dynamics of plasmasphere, 10–15 snapshot observations per hour of the equatorial density with a meridional cross section is likely to be dense enough to describe significant dynamics. This means that a quasi real‐time implementation should be able to complete the analysis of a MP whistler group within 250–300 s.
Based on the new, extended whistler inversion method [Lichtenberger, 2009], we have developed the automatic whistler analyzer algorithm. It is based on a special transformation called Virtual (whistler) Trace Transformation [Lichtenberger, 2009]. (Figures 1.2 and 1.3) .It has three parameters: the time of the causative sferic (dt) that is the one of the major outstanding problem of whistler analysis. The other two parameters, A and B are related to nose frequency fn and nose time tn (Figure 1.4) to the geometric parameters of a whistler. A and B are related to the real physical parameters propagation L-value and equatorial electron density neq through log10 neq = A + B*L [Lichtenberger, 2009].
We have solved the causative sferic problem through an old method, the Q-transformation [Dowden and Allcock, 1971], which is used to determine the nose frequency of a non-nose whistler. There is an important implicit assumption in this transformation: it assumes the time of the causative sferic is known.
We tried this Q-transformation on spectrogram matrices using an arbitrary dt and we found that it works in some aspect, it produces non-straight lines. This led to a 'trial and error' approach: one can measure the travel time from an arbitrary point and see whether the result of the Q transformation is a straight line or not and move the origin until the result is a straight line. Addition to the determination of dt, the output of the Q-transformation can also be used to identify and extract information of the strongest whistler trace (Figure 1.4). Application of whistler inversion to the extracted trace provides a propagation L-value and equatorial electron density, that is a fixed point in (A,B) space. Together with dt obtained, the parameter space is reduced to two a limited range of the two unknowns (A and B).
Since we do not have a good initial set for A and B, an optimization procedure could be applied to find the optimal values – but the objective function of the problem is badly behaving, it has many local minimums slightly smaller than the global minimum.. This can led to in-matured stop of the procedure. Therefore a different approach is used to find the optimal (A,B) pairs. Because the dimensionality of the problem is reduced, VTT is calculated only for a reduced set of (A,B).
Objective 3. Implementation of the AWA in AWDANet nodes
The implementation first required to make a decision on hardware architecture. The parallel processing (PP) architecture serves as a hardware for Automatic Whistler Analyzer (AWA). AWA requires high CPU resources to be run in quasi real-time. The decision of AWA PP is based on the following test results and preliminary requirements:
- inversion of a Multiple Path (MP) whistler group on a single average CPU (single core of a Intel Core 2 Duo) takes 24-48 hours, while the goal is to invert 10-15 MP whistler groups per hour. Further, the final architecture has to be compact enough to fulfill the size, weight and power consumption constrains on far location (e.g. Antarctica) setup.
- during the negotiation phase of PLASMON, we have improved the AWA algorithm by a more sophisticated Look-up Table (pre-calculated whistler models). The usage of improved LUT reduced the run-time from 24-48 hours to 4-5 hours (15-18000sec).
As AWA algorithm is highly suitable for parallel processing, We have tested the AWA algorithm on three architectures:
- PC cluster
- FPGA
-. GPGPU
Based on volume, power consumption and price/budget considerations, we have selected GPU technology and we concluded that AWA can be successfully implemented with the required speed on a small GPU cluster consisting of 8 GTX580 – actually 4 GTX590.
The full AWA processing chain consist of three major steps:
1. pre-processing of AWD outputs. AWD should work in or faster than real-time, therefore it detects a whistler event and saves raw data with the event into a disk file. Due to the dual-purpose of AWD algorithm (see Lichtenberger et al., [2008]), the algorithm may save empty raw data (i.e. signal without whistler). The pre-processing algorithm has to
reliably filter the empty data files and
create the input cleaned, reassigned spectrogram matrix for AWA.
This pre-processing algorithm is an enhanced version of AWD algorithm. It can process a disk file between 10-120sec, depending on the complexity of the whistler event (naturally faster if no whistler event in the file).
2. AWA phase one,
3. AWA phase two.
AWA phase one (Q-transformation to determine dt and fn, extraction of f-t pairs for the strongest trace and running the whistler inversion procedure is implemented in CPU (i7-2600) code and takes 20-30 sec, depending on the complexity of whistler events.
AWA phase two code, finding the optimal values of A and B were migrated to GPU using CUDA library. We have implemented the code using plain cuda implementation on 2xGTX590. Phase two runs take: 20 -60 sec.
Implementation of AWA code for AWDAnet node located at different magnetic latitudes.
Basically there are differences in frequency range of filters in pre-processing codes and VTT for different magnetic latitudes.
These are:
1. 4000Hz-19000Hz for mid-latitudes (1.4
Additionally, pre-processing always requires site dependent noise thresholding. This had to be done from station to station.
As AWDANet has more operating nodes (running AWD only) than PP units purchased in PLASMON, we had to make a selection where to install the PP units. This selection was based on the following criteria:
1. Signal quality
2. location to reach optimal magnetic latitude and longitude coverage
3. whistler activity
Based on these criteria we have selected the station below and installed AWA on PPs:
1. Mid-latitude stations: Tihany (Hungary) and Gyergyóújfalu (Transylvania), Grahamstown (South Africa)
2. higher latitude stations: Eskdalemuir (Scotland), Forks (WA, USA), Karymshina (Kamchatka, Russia) – these are the new stations installed in PLASMON - , Dunedin (New Zealand), Humain (Belgium), Ithaca (NY, USA), Marion Island (South Africa), Rothera (Antarctica, UK), Tvarminne(Finland),
3. high latitude stations: SANAE (Antarctica, South Africa), Halley (Antarctica, UK), Oulu (Finland)
Objective 4. Development of AWDANet to work in quasi-real-time mode of operation.
On the 15 stations listed above the AWA were gradually installed and tested, the full network operates in quasi real-time mode since end of June, 2014.
The block diagram of the configuration of AWDANet nodes with AWA is on Figure 1.5
Maintaining the quasi real-time mode of operation: the quality flag
The implementation of the AWA algorithm on GPUs fulfilled the expected speed for the quasi real-time mode of operation in general. However, we were faced with two particular problems that might prevent the system to achieve the quasi real-time mode of operation on longer term:
a. uneven input data rate – the whistlers are natural phenomena and their occurrence rate is highly varying exhibiting diurnal and seasonal variations. This means that there are periods – days – without a single whistler at a location and there are periods when hundreds of whistlers are detected.
b. fixed processing capacity. The installed GPUs can process 40-100 events per hour, depending on the complexity of the event. When there are high number of events, the long processing queue can prevent the system to keep up the processing in quasi real-time.
To overcome this problem, we introduced an extra pre-processing step. In this step, the quality of the event is estimated and a flag is assigned to each event. The estimation is based on the SNR of the whistler traces, the remnant noise in the simplified reassigned spectrogram and the dispersion of the spectral matrix elements in the time-frequency domain. Sixteen classes have been created and and assigned from QF=00 to QF=15.
Two examples of assigned Qf are shown on Figure 1.6.
The processing stream is controlled by a bash script on AWA preprocessing PC. During low and medium activity, it takes the next detected whistler event and process it: pushing through the event/trace locator – pre-processing - inversion – post-processing pipe regardless of the value of the QF. Naturally, the inversion is the slowest section of the pipe.
However, if new events pop up at the inversion pipe while the previous event is still in the pipe, the event with the lowest QF will be selected, when the pipe is ready for processing the next event. If there are two or more events with the same lowest QF, the latest is selected. The rest are redirected to the 'non real-time queue'. This queue is gradually processed when no new event is detected, i.e. when the pipe is in idle state. The statistics collected during the first period of quasi real-time mode of operation proves the role of QF in maintaining the mode (Figure 1.7).
WP2. Retrieval of equatorial plasma mass densities by SEGMA and MM100 magnetometer arrays and cross-calibration of whistler and FLR method
The scope of WP2 was the retrieval of equatorial plasma mass densities using geomagnetic field line resonances (FLRs) detected at an extended latitudinal array of magnetometer stations. The inferred plasma mass densities have to concur, together with electron concentrations from WP1 and in situ measurements from satellites, to construct a dynamical model of the Earth’s plasmasphere (WP3). The specific objectives of WP2 were the following:
1. unify and extend the pre-existing SEGMA, MM100 and IMAGE networks into EMMA complemented by SANSA network to have a latitudinal coverage suitable for the FLR technique,
2. develop an automatic FLR identification (FLRID) method based on previous experience and recent improvements,
3. develop an automatic FLR inversion (FLRINV) code based on most recent achievements including error estimations,
4. develop all EMMA stations to work in quasi-real-time mode of operation,
5. evaluate relative abundances of heavy ions in the plasma composition from simultaneous determinations of mass density (FLR method) and electron density (whistler method).
Results achieved
1. and 4. Establishment of EMMA and SANSA network and development in real-time mode of operation
Thanks to recent developments in magnetometry (e.g. reduction of noise), data acquisition (improved resolution and timing), and the theory (wave propagation, event detection, models, inversion) of magnetohydrodynamic (MHD) waves, the routine monitoring of the cold plasma mass density of the plasmasphere has become possible. EMMA was established in 2012 within the frame of PLASMON with the main goal to monitor the plasmaspheric mass density based on the detection of FLRs. EMMA was born through the unification and extension of previously existing European magnetic arrays: SEGMA (South European GeoMagnetic Array) (Vellante, et al. 2004) andMM100 (Heilig et al. 2010) including the Finnish stations of IMAGE. 6 new stations were installed: BRZ (Birzai, Lithuania, L = 2.74) HLP (Hel, Poland, L = 2.54) SZC (Szczechowo, Poland, L = 2.35) ZAG (Zagorzyce, Poland, L = 2.10) VYH (Vyhne, Slovakia, L = 1.95) LOP (Lonjsko Polje, Croatia, L = 1.73). At the end of 2012 EMMA consists of 25 stations from north Finland to Italy (L-shells 1.6–6.7 Figure 2.1) as a joint effort of FMI (Finland), IGFPAS (Poland), SAS (Slovakia), MFGI (Hungary), and University of L’Aquila (Italy). PLASMON also has a smaller magnetometer network maintained by SANSA at South-African conjugate area (Figure 2.2). The SANSA observations (SUT-HER) will allow examination of possible effects of north-south ionospheric asymmetries and will give independent estimates of the plasma mass density at L = 1.8 therefore providing a check on the accuracy of the method. In addition, measurements from the new Namibian pair TSU (Tsumeb, L = 1.35)-WBP (Waterberg Plateau Park, L = 1.39) will allow extension of the monitoring to a lower L-shell (Heilig et al., 2013b; Lichtenberger et al., 2013).
All stations of the EMMA and SANSA network are equipped with low noise fluxgate magnetometers of similar performances and GPS antenna for precise time recording. Each institutes upgraded or replaced the data loggers to fulfil PLASMON requirements (remote access of data, >20 Hz sampling rate, precise timing). MFGI has developed a new magnetic data acquisition system. The MFGI DAQs are connected to the output of LEMI-035 (specific LEMI design for PLASMON) and NAROD STE magnetometers. The analogue output of the magnetometers has a cutoff at 20 Hz. Data are sampled at 128 Hz and then filtered to produce the EMMA data at 1 Hz data rate. The noise is typically below 10 pT @ 1 Hz, the overall delay is 32 ms (Merényi et al., 2013).
1-s magnetic data from all stations are transferred in near real-time to the project servers (one at MFGI and the other at UNIVAQ) for processing (Figure 2.3). All data are also collected at these servers (e.g. at http://plasmonserver.aquila.infn.it/) as daily files in a specific binary data format (Figure 2.4). At MFGI a web-based monitor http://geofizika.canet.hu/plasmon/ was also developed. This web monitor also provides near real time (as well as archived) magnetograms and power spectra of all EMMA and SA stations. On the same website the availability of current as well as archived data can be checked. The website also informs on PLASMON and the EMMA network in general and provides detailed information on instrumentation and location of all EMMA sites
2. FLRID
The first step of the data processing is the detection of FLRs. For this scope an automatic method (FLRID) has been developed. FLRID is based on the expected characteristics around the local resonance frequencies of phase difference (phase gradient technique introduced by Waters et al. 1991) and amplitude ratio of the magnetic signals recorded at two stations along the same magnetic meridian closely spaced in latitude (1°-3°, Figure 2.5). FLRID also allows the uncertainty in the detected FLR frequency (Berube et al. 2003) as well as the resonance width (Green et al. 1993, Heilig et al., 2013a) to be estimated. Thanks to the proper planning of the spatial configuration of the EMMA and SANSA networks the method can be applied to more than 40 station pairs. For each station pairs the optimal set of FLRID parameters has been derived and adjusted through repeated statistical analysis of FLRID results. FLRID is run automatically on PLASMON EMMA Servers in every 15 minutes.
The near real time procedure as well as archived cross spectra can also be monitored on the web-based monitor.
3. FLRINV
The second step is the FLRs inversion. We developed a code (FLRINV) which solves the MHD wave equation of the resonance in an arbitrary magnetic field geometry to infer the plasma mass density at the magnetic equatorial point ρeq of a given field line. The T01 Tsyganenko model of the magnetospheric field has been adopted. Along the field line a standard power-law dependence for the density distribution ρ(s)/ρeq = (r/req)-m is assumed, with the power index m generally being equal to 1. Detailed studies on the comparison of results obtaining by assuming either dipole, IGRF, or T01 models have been conducted (Figure 2.6). We have also evaluated the uncertainty in ρeq which derives from the uncertainty in FLR frequency determination, in the T01 model parameters (Vellante et al., 2014), and in the field-aligned density distribution. The overall error of our individual density estimates (> 90% of all) has been evaluated to be of the order of 20-30% for ~2 < L < ~6, which is confirmed by comparison with in situ measurements. Larger uncertainties might occur for L < ~2 (Vellante and Förster, 2006) or for L > ~4 during strong geomagnetic storms (Dst < 100-150 nT) (Vellante et al., 2014).
FLRINV also works in near real time. It cyclically runs every 15 min using the output of FLRID, solar wind and Dst parameters.
Real time solar wind data are taken from the NOAA Space Weather Prediction Center which provides, in near real time, the latest 2 hours of magnetic and plasma data of the ACE satellite located at the L1 libration point, as well as the precise ACE location (http://www.swpc.noaa.gov/ftpdir/lists/ace/). These data are then time-shifted in order to take into account the propagation time of the solar wind from the satellite position to the Earth (typically 1 hour). Finally, these propagated data are resampled at fixed times and hourly running averages (with time step of 1 min) are produced.
Real-time Dst data are taken from the Dcx server of the University of Oulu, Finland through the following link: http://dcx.oulu.fi/DstDcxDxtData/RealTime/Dxt/DxtRT.txt.
The final result of the inversion is stored in daily text files which are updated every 15 min and are available online at http://plasmonserver.aquila.infn.it/EMMA_FLR_DENSITY.
Realizing the importance of the plasmapause position we also developed techniques to derive the location of this boundary automatically both from ground and space observations (e.g. Heilig and Lühr, 2013)
5. Cross-calibration
In order to validate the method of estimating electron and mass densities from the ground-based measurements of whistlers and FLRs we have conducted some comparative analysis of our ground-based measurements with in-situ satellite measurements.
Calibration of equatorial electron densities obtained by whistler inversion with in situ electron density measurements by IMAGE (Figure 2.7) and Van Allen Probes (VAP) satellites has been done. The results evidence no apparent bias in whistler inferences and allow us to reliable use of equatorial electron densities from whistler inversion in plasmasphere modeling in WP3.
Comparisons of plasma mass densities derived from FLRs with in situ electron density measurements provided by IMAGE (Figure 2.8) and Van Allen Probes (Figures 2.9-10) satellites have been conducted for some favourable conjunction events. The median of the plasma mass to electron density ratio in the plasmasphere is found to be 0.96 amu, very close to the value (1.00 amu) expected in a plasma consisting of protons and electrons only. In the plasma trough the same ratio is 2.17 amu indicating the presence of heavier ions (He+ and/or O+). The highest ratios (up to 8-10 amu) are found around the plasmapause which is in line with previous observations indicating the presence of an oxygen torus in this region.
WP3. Data assimilative modeling of the Earth’s plasmasphere
1. Motivation
The plasmasphere is a important component of the inner magnetosphere. It is now well-known that the plasmasphere is critical in the control of the wave processes which are responsible for the acceleration and decay of the radiation belts. Plasma density gradients and boundaries are important sites for the generation of these waves. For that reason accurate knowledge of the plasma density
distribution in the plasmasphere will help improve the accuracy of radiation belt models' specification of the radiation environment.
The plasmasphere is a dynamic system which is sensitive to external drivers. The most important external driver is the global convection electric field. The gross structure of the global convection electric field is understood, but the detailed evolution of this electric field cannot currently be specified accurately enough to drive a plasmasphere model and produce a realistic plasmasphere which is accurate enough to drive radiation belt models.
The solution to this difficulty is to incorporate observations and to constrain the plasmasphere with observations. This merging of observations and models is called data assimilation.
In this project we use data assimilation with ground-based observations in order to improve the accuracy of the specification of the plasmasphere for any given events. For the plasma model we use the Dynamic Global Core Plasma Model, and we considered two different data assimilation approaches, the particle filter, and the ensemble Kalman filter, before settling on the ensemble Kalman filter. In the following sections we describe the
2. Plasmasphere Model
For this project we use the Dynamic Global Core Plasma Model (DGCPM) [Ober and Horowitz, 1997]. DGCPM models flux tube number density for approximately L>1.3. DGCPM is governed by a simple but powerful set of equations which are outlined in Figure 3.1. These equations implement the following physical processes. (a) the outflow of plasma from the dayside ionosphere to fill fluxtubes to a saturation level, (b) the loss of plasma into the ionosphere on the nightside, (c) the convection of the plasma according to imposed magnetic and electric fields. Although DGCPM has the flexibility to function with arbitrary magnetic field we have so far only used it with a simple dipole magnetic field. Figure 3.2 shows a few snapshots of the plasma distribution from a run of the DGCPM..
3. Data-model comparison
A good starting point for data assimilative modeling is that there is at least qualitative agreement between observations and model. For that reason we performed some comparison studies early in the project to analyze how well the model represents reality. In the early phase of the project this comparison was primarily in the form of satellite data since the ground-based data were still being
processes at that stage. Thus, Figure 3.3 shows a comparison between the DGCPM and density observations from LANL geostationary satellites. Figure 3.4 shows the first comparison of the output of the VLF inversion algorithm with the DGCPM. The initial results confirmed that the DGCPM
is adequate for representing the plasmasphere well enough for being used in data assimilation.
4. Data assimilation
We implemented an Ensemble Kalman Filter (EnKF) data assimilation framework. The code was implemented in C++ using openMPI for parallelization and ScaLAPACK to perform the distributed matrix operations that are necessary in a multi-processor Kalman Filter implementation. For the DGCPM we use the original Fortran implementation written by Dr. Daniel Ober at the Air Force Research Laboratory in in Albuquerque, USA. This model is wrapped in a C++ class interface making it easily usable within the EnKF framework.
Because the EnKF filter worked well enough we decided to not implement a particle filter as well, but instead focuses on the application of the EnKF data assimilation filter.
The Ensemble Kalman Filter (EnKF) [e.g. Evensen, 2003] which uses a statistical approach to replace the covariance matrix of the Kalman filter. The approach is illustrated in Figure 3.5. Instead of a mean state and a covariance matrix of the state the EnKF uses a single matrix whose columns are complete model states which are allowed to evolve randomly forward in time according to model noise equivalent to covariance noise matrix used in the Kalman filter. It is then possible to compute any statistical measure across the columns, including all the covariances of the Kalman filter covariance matrix. These are now obtained on a statistical basis so there should be enough columns to fully explore parameter space.
To advance the the model ensemble forward in time we must add model noise. That means driving each ensemble member differently. This driving can be performed anywhere in the model, but in our case we have elected to adjust only the electric field that drives convection in the plasmasphere model. The electric field parameters are allowed to evolve according to a red-noise signal and thus over time the individual ensemble members will tend to diverge. This divergence represents the reduction in the state of the system as the model evolves forward in time without constraining data. When data become available the model ensemble is transformed by a linear transformation to produce a new ensemble which is consistent with the data and the uncertainty of the data. This is illustrated in the upper right corner of Figure 3.5.
5. Data assimilation tests
To test the data assimilation framework in preparation for the ground-based observations we used in-situ density observations from LANL geostationary satellites. Figures 3.6 and 3.7 show the effects of the assimilation.
The assimilation run was done for a 10-day period in December 2006. Figure 3.6 shows the results for the full period. The top panel shows the Kp index for the interval where the storm starting at the end of December 14 is evident. The bottom five panels show the observations and models for the five LANL geostationary satellites. In each panel the black trace is the observations. The blue trace is the model run without data assimilation. That means that the model was run using the Sojka et al. [1986] electric field model parameterized by the values in the top panel. The red traces represent the data assimilation output, in which the electric field model parameter is adjusted to maximize agreement of the model to the observations. In most cases the assimilation output agrees more closely with the observations than does the model run without data assimilation.
Figure 3.7 provides a closer look at December 18-19 with the figure having the same format at Figure 3.6. In this case it is quite clear that the assimilation output agrees better with the observations. However it is also obvious that there still are significant differences. In this plot the estimated standard uncertainty on the model output is also included as the thinner red traces surrounding the central thicker trace. In almost all cases the uncertainty range is underestimated compared to the difference between assimilation output and observations, perhaps because the electric field model in this case is not sufficiently flexible to represent real magnetospheric electric fields.
6. Electric field models
The electric potential is the driver of the plasmasphere model and the free adjustable parameters are parameters to the electric field model. Thus the electric field model must be flexible enough to represent the electric fields expected in the plasmasphere, but also do so with a relatively small number of parameter because of the limited number of observations available. A good compromise appears to be the harmonic function sets used in the earlier versions of the Weimer models. In Figure 3.8 is the equation for the Weimer potential patterns, an illustration of the radial functions, and an example of a potential pattern derived from the model. In our use of the model the parameters of the Weimer model are freely adjustable, and depending on the degree of flexibility required and the amount of data available we can elect to take the model to lower or higher order.
7. Observations
The two primary sources of observations for this project were the AWDANet VLF electron density observations and the EMMA ULF FLR mass density observations. Figure 3.9 shows one example of a EMMA data set. There are several prominent feature visible in the data sets. Firstly, the FLR density measurements are only available during the daytime, because that is the time when the ionosphere is sufficiently strong to reflect the waves. Secondly, on each day an increasing density trend is apparent. This is the refilling of the plasmasphere. The second feature is the depletion of plasma during strong convection intervals. The start of such convection intervals is illustrated as the vertical red lines.
9. Events
During the project we analyzed a number of different events. Here we will discuss one event, the storm event surrounding 15 July 2012. The Dst index for the event is shown in Figure 3.10. There was a drop in Dst at the start of 15 July 2012, reaching a peak of almost -100 nT. We collected data for data assimilation during a 10-day interval surrounding the storm onset, and processes them through data assimilation. Figure 3.11 shows the data coverage for the event. On the horizontal scale is time and on the vertical scale are each for the different data sets. Red indicates that data are available and white that no data are available.
The data are used to constrain the plasmasphere model and the results are a series of plasma density maps, and the determination of the parameters for the electric potential model which generates those plasma density maps. Figure 3.12 shows the input data compared with the assimilation output sampled at the same location. The green curves are the reference DGCPM model run without assimilation. The blue curves are the observations. The red curves are the assimilation mean and standard deviation densities.
From the density maps we can derive the location of the plasmapause. Figure 3.13 shows the plasmapause location derived from the assimilation results and from a non-assimilative model run. The black and grey lines are the median and range, over MLT, of the location of the plasmapause for the non-assimilative model run. The red lines are the corresponding curves for the assimilation run. The sharp drop at the start of the plot on 12 July is simple a startup effect of the model.
WP4. Modeling REP losses in radiation belts based on AARDDVARK network
Objectives:
4.1 Extend AARDDVARK network to have a better L-shell, MLT coverage.
4.2 Analyse the characteristics of REP, case by case, and at different L-shells.
4.3 Develop a model which identifies the size, location, MLT zone, geomagnetic conditions, and flux
characteristics of the REP.
4.4 Refine the REP model to describe on/inside/outside plasmapause precipitation using input from the WP3 model.
Description of work
Work progress and achievements during the period
Summary of progress towards objectives and details
Three new AARDDVARK stations were installed at Seattle (USA), Ottawa and St Johns(Canada) in Years 1 and 2. This objective was met.
Seven papers have been accepted or published within the workpackage which identify and characterise energetic electron precipitation which is caused by processes occurring inside/on/outside the plasmapause. The precipitation characteristics of all three regions were identified, and characterised. Three additional papers are being prepared and will be submitted in the future. This objective was met.
Development of two REP models was completed. One model produces an MLT-L map of electron precipitation using a plasmapause model to define the locations where precipitation occurs. The REP model was built on the output of the work to characterise REP. The REP model can produce a movie of how the precipitation changes during geomagnetic storms, or provide numerical data output to the user. This objective was met.
A second REP model was produced that uses the assimilated plasmasphere output of WP3 to identify the location the plasmapause, and thus define the locations where precipitation occurs. This REP model has been made available to WP3, and both REP models are held within the PLAMSON archive directory. This objective was met.
The initial phase of WP4 was to install and setup three new AARDDVARK sites by month 24. The requirement was to place AARDDVARK receivers at sites that had scientifically useful subionospheric paths from transmitter to receiver, the possibility of good signal to noise measurements, somewhere to safely house the equipment, and a reliable internet connection to return the data back to BAS. The sites chosen were Forks near Seattle hosted by Prof Holzworth at University of Washington, Ottawa, and St Johns in Canada hosted by Natural Resources Canada (NRCan). These sites all provided some quasi-constant L-shell paths from nearby VLF transmitters, thus providing good opportunities to investigate electron precipitation over a narrow L-shell range. By Month 24 we had installed all three sites, and provided installation reports on each to PLASMON. Figure 4.1 (provided in the attachments) shows the current AARDDVARK receiver network in the northern hemisphere, and their potential propagation paths from nearby transmitters. As a result of these installations the AARDDVARK network was well placed to allow quasi constant L-shell data analysis, both inside and outside of the plasmaspause. The data from the three new AARDDVARK sites funded through PLASMON is automatically transferred to BAS, Cambridge, each night and archived on the web-based database. Thus it is quickly made available to all workpackage 4 team members, as well as the rest of the PLASMON project members. Thus objective 4.1 was fully met.
The objective to undertake analysis of the characteristics of REP, case by case, and at different L-shells spanned the middle and later parts of the PLASMON project. We approached this task in two ways, by investigating in detail specific case studies, and by inspection of the large POES electron precipitation dataset. The specific case studies were usually driven by ground -based AARDDVARK data, and were able to identify regions of the plasmaphere and MLT times that were significant in producing electron precipitation, such as the inner plasmasphere during the daytime. The three new AARDDVARK sites were chosen specifically to provide sub-ionospheric propagation paths that would give insight into processes going on inside the plasmapause, i.e. they we chosen to provide quasi-constant, L~3 propagation paths. In addition the new sites gave some good higher latitude propagation paths which could be used to augment the data coverage from outside of the plasmapause, as well as some very well placed paths which give plasmapause information. The latter is particularly well studied by the Iceland transmitter received at St Johns, which responds to EMIC-driven precipitation, and is quasi conjugate to the instrument suite at the BAS Antarctic base, Halley. The POES dataset analysis of electron precipitation was particularly useful in identifying the characteristics of relatively rare, localised events such as EMIC-induced precipitation close to the plasmapause. The POES database was also inspected statistically, to gain insights into highly variable precipitation conditions.
An example of the use of the AARDDVARK data in order to characterise electron precipitation was published by Simon Wedlund et al., [2014] [A statistical approach to determining energetic outer radiation-belt electron precipitation fluxes. Mea Simon Wedlund, Mark A. Clilverd, Craig J. Rodger, Kathy Cresswell-Moorcock, Neil Cobbett, Paul Breen, Donald Danskin, Emma Spanswick, and Juan V. Rodriguez – JGR Space Physics]. AARDDVARK data for a geomagnetic disturbance period in 2010 was analysed, and the resulting electron precipitation fluxes compared with POES measurement precipitation fluxes, and those calculated from a ground-based riometer located just outside the plasmapause field-line footprint. Good agreement was found throughout the study period, with the fluxes being associated with chorus waves occurring outside of the plasmapause (see Figure 4.2 in the attachments). Additionally the precipitation flux energy spectrum was described and compared with POES measurements. Both the precipitation flux and energy spectrum were shown to be consistent with a model based on the geomagnetic activity index Dst.
An example of the large POES electron precipitation dataset to characterise relatively rare, localised events such as EMIC-induced precipitation close to the plasmapause was published by Carson et al., [2013] [POES Satellite Observations of EMIC-wave driven Relativistic Electron Precipitation during 1998-2010, Bonar R. Carson ,Craig J. Rodger, Mark A. Clilverd – JGR Space Physics]. Using six satellites that carried the SEM-2 instrument package, individual half orbits between 1998 and 2010 were inspected by an automatic detection algorithm searching for EMIC-driven relativistic electron precipitation (REP). In all, 2,331 proton precipitation associated REP (PPAREP) events were identified. The majority of events were observed at L-values within the outer radiation belt (3
A similar approach was undertaken in order to determine the the flux and energy spectral gradient of the electron precipitation from plasmaspheric hiss waves and their location relative to the plasmapause. As can be seen from the form of the plasmapsheric hiss equation shown in the equations attachment, our findings suggest that the plasmaspheric hiss generates precipitation with energies of about 300 keV, and its location inside the plasmapause is constant in L-shell, and does not move with the location of the plasmapause. The POES super-posed epoch analysis suggests that plasmaspheric hiss does not generate any significant electron precipitation with energies <300 keV, which is a surprising finding, and one that will need to be studied in detail in the future. A further publication on this topic, which combines POES and AARDDVARK data at L-shells inside the plasampause, will be published after the end of the project. With this type of analysis the characteristics of electron precipitation on/inside/outside of the plasmapause during geomangetic storms was described, characterised, and proxies developed. Thus the objective 4.2 was sussessfully undertaken, and fully met.
The last two goals of the PLASMON WP4 were to develop REP models. One REP model was for generic use, and one REP model was specifically to take the output of the assimulative plasmasphere results from WP3 and use that to determine where electron precipitation should be occuring, and with what characteristics.
Key for the generic precipitation model is the establishment of the plasmapause location. To met this objective we used the O'Brien & Moldwin (2003) empirical model [referred to as “OM3”] to determine the position of the plasmapause at each MLT. However, the model code was written such that any plasmapause model could be used. The OM3 model is used to find the L-shell location of the plasmapause [Lpp], which is based on the AE index value during the study period (or real time). All precipitation is determined relative to the determined plasmapause locations.
We used the flux and energy spectrum characteristics of the chorus-driven, plasmaspheric hiss-driven, and EMIC-driven electron precipitation. Each of the wave-types were assigned a specific region of influence relative to the plasmapause location, i.e. outside the plasmapause for chorus-driven precipitation, and primarily on the dawn side of the Earth. The time variation of the precipitation characteristics was specified by using the time-varying Dst index, where the relationships between the precipitation and Dst were determined in objective 4.2. Thus the precipitation electron flux can be evaluated for each wave-type and at a given L, MLT, Dst and plasmapause location (Lpp). These fluxes are only defined within certain regions, based on the position of the plasmapause, as given by Chorus-driven precipitation from Lpp < L < 9, and 23h < MLT < 11h; Hiss-driven precipitation from 3.1 < L < 4.2 and 11h < MLT < 16h; EMIC-driven precipitation from Lpp < L < Lpp+1, and 16h < MLT < 23h.
The key features of the model precipitation can be summarised as follows. The chorus-driven precipitation follows the variation of Dst with no delay, and thus peak fluxes occur outside of the plasmapause, and at the time of the largest excursion of the Dst index. The recovery of the chorus-driven fluxes takes several days, as Dst takes several days to return to non-disturbed levels. The plasmaspheric hiss-driven precipitation as follows the variation of Dst, but with a delay of 2 days, thus the peak fluxes occur inside the plasmasphere 2 days after the time of the largest excursion of the Dst index. Hiss-driven precipitation fluxes continue after Dst has returned to non-disturbed conditions. The EMIC-driven precipitation follows the variation of Dst with no delay, however, an additional condition was imposed such that the geomagnetic index Kp had to be greater than 4 before any precipitation occurred.
The electron precipitation associated with EMIC was the least well resolved by PLASMON objective 4.2 analysis. The precipitation is described only when Kp>4, and then simply as an increasing flux as geomagnetic activity becomes more disturbed. Future work is expected to provide more detailed information on the characteristics of the precipitation from EMIC waves. In the model developed here the energy spectral gradient was assumed to be a power-law of -2.5 which is a finding that will be published with a PLASMON acknowledgement in due course, and can be refined in the model in the future. With the development of the electron precipitation model with a generic plasmapause model objective 4.3 was fully met.
In order to undertake objective 4.4 the assimilated plasmaspheric output from WP3 had to be used to determine the plasmasphere structures. For the WP3-WP4 integrated precipitation model the PLASMON WP3 assimilated plasmasphere model is scrutinised to provide the location of the plasmapause. It was found that the PLASMON WP3 plasmasphere model did not always display a 'knee' structure indicative of the plasmapause. As such, the criterion used in satellite studies of the plasmasphere of a drop in density of a factor 5 in L-shell range of 0.5 (e.g. Carpenter & Anderson (1992)) could not always be applied. Instead, a threshold value of 5 x 107 m-3 was used. This also circumnavigates problems that arise when, even for a real plasmasphere radial density profile, the plasmasphere does not genuinely have a knee-like structure. The precipitation model uses the plasmasphere density maps generated by WP3 every 20 minutes, determines the plasmapause location, finds the geomagnetic conditions, and calculates the fluxes and spectrum. Figure 4.5 provides an example of the type of output generated by the precipitation model.
As with the electron precipitation model developed in objective 4.3 the electron precipitation characteristics were defined by the results from objective 4.2. We used the flux and energy spectrum characteristics of the chorus-driven, plasmaspheric hiss-driven, and EMIC-driven electron precipitation. Each of the wave-types were assigned a specific region of influence relative to the plasmapause location, i.e. outside the plasmapause for chorus-driven precipitation, and primarily on the dawn side of the Earth. The time variation of the precipitation characteristics was specified by using the time-varying Dst index, where the relationships between the precipitation and Dst were determined in objective 4.2. Thus the precipitation electron flux can be evaluated for each wave-type and at a given L, MLT, Dst and plasmapause location (Lpp). These fluxes are only defined within certain regions, based on the position of the plasmapause, as given by Chorus-driven precipitation from Lpp < L < 9, and 23h < MLT < 11h; Hiss-driven precipitation from 3.1 < L < 4.2 and 11h < MLT < 16h; EMIC-driven precipitation from Lpp < L < Lpp+1, and 16h < MLT < 23h.
The main difference between the electron precipitation models developed in objective 4.3 and 4.4 is that the plasmapause location in specified by the output from the assimilated plasmasphere model from WP3, which takes data provided by WP1 (electron density measurements) and WP2 (ion density measurements). Both models show variations in the L-shell of precipitation regions as the location of the plasmapause changes as a result of changes in geomagnetic activity, particularly as the plasmapause moves inwards to lower L-shells during the onset and main phase of large geomagnetic storms. As objective 4.3 used a statistical model of the plasmapause, and objective 4.4 uses a data-driven plasmasphere model, it is likely that the model from objective 4.3 is more suited to a statistical interpretation of the electron precipitation pattern, while the model from objective 4.4 is more suited to a case-study, or quasi-real time analysis. With the development of the electron precipitation model with a plasmapause model provided by WP3, objective 4.4 was fully met.
WP5. Dissemination and exploitation of the results
We have published 17 papers on peer-reviewed scientific journal:
Scientific Papers
1. Whittaker, I. C., C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, The effects and correction of the geometric factor for the POES/MEPED electron flux instrument using a multi-satellite comparison, J. Geophys. Res., 2014JA020021 (in press), 2014.
2. Antel, C., A. B. Collier, J. Lichtenberger, and C. J. Rodger, Investigating Dunedin Whistlers using Volcanic Lightning, Geophys. Res. Lett., 41, doi:10.1002/2014GL060332 2014.
3. Vellante, M., M. Piersanti, and E. Pietropaolo, Comparison of equatorial plasma mass densities deduced from field line resonances observed at ground for dipole and IGRF models, J. Geophys. Res., doi: 10.1002/2013JA019568 2014.
4. Whittaker, I. C., R. J. Gamble, C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, Determining the spectra of radiation belt electron losses: Fitting DEMETER IDP observations for typical and storm-times, J. Geophys. Res., doi:10.1002/2013JA019228 2013.
5. Lichtenberger, J., M. Clilverd, B. Heilig, M. Vellante, J. Manninen, C. Rodger, A. Collier, A. Jorgensen, J. Reda, R. Holzworth, R. Friedel and M. Simon-Wedlund, The plasmasphere during a space weather event: First results from the PLASMON, J. Space Weather Space Climate, 3 (A3), http://dx.doi.org/10.1051/swsc/2013045 2013.
6. Heilig, B., and H. Lühr, New plasmapause model derived from CHAMP field-aligned current signatures, Ann. Geophys., 31, 529-539, doi:10.5194/angeo-31-529-2013 2013.
7. Heilig, B., P. R. Sutcliffe, D. C. Ndiitwani, and A. Collier, A statistical study of geomagnetic field line resonances observed by CHAMP and on the ground, J. Geophys. Res., 118, doi: 10.1002/jgra.50215 2013.
8. Carson, B. R., C. J. Rodger, and M. A. Clilverd, POES Satellite Observations of EMIC-wave driven Relativistic Electron Precipitation during 1998-2010, J. Geophys. Res., 118, 1–12, doi:10.1029/2012JA017998 2013.
9. Hendry, A. T., C. J. Rodger, M. A. Clilverd, N. R. Thomson, S. K. Morley, and T. Raita, Rapid radiation belt losses occurring during high-speed solar wind stream–driven storms: Importance of energetic electron precipitation, in Dynamics of the Earth's Radiation Belts and Inner Magnetosphere, Geophys. Monogr. Ser., vol. 199, edited by D. Summers et al., 213–223, AGU, Washington, D. C., doi:10.1029/2012GM001299 2013.
10. Merényi, L, B. Heilig, and L. Szabados, Geomagnetic Data Acquisition System developed for the PLASMON project, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp. 54-56, San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
11. Heilig B., J. Lichtenberger, M. Vellante, J. Reda, T. Raita, P. Sutcliffe, M. Váczyová, D. Herak, M. Neska, L. Merényi, A. Csontos, P. Kovács, M. Srbecky, and I. Mandic, EMMA for near real time Monitoring of the Plasmasphere, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp., 127-130 San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
12. Lichtenberger J., and Cs. Ferencz, Űridőjárási folyamatok a magnetoszférában (Space weather processes in the magnetosphere), Magyar Geofizika, 53 (3), pp.182-190 2012.
13. Kovács P., A. Csontos, B. Heilig, L. Hegymegi, L. Merényi, G. Vadász, A. Koppán, and A. Földmágnesség, A Tihanyi Geofizikai Obszervatórium (The Tihany Geophysical Observatory), Magyar Geofizika, 53. évf. 3. sz., 191-203. p. (in Hungarian), 2012.
14. Rodger, C. J., M. A. Clilverd, A. J. Kavanagh, C. E. J. Watt, P. T. Verronen, and T. Raita, Contrasting the responses of three different ground-based instruments to energetic electron precipitation, Radio Sci., doi:10.1029/2011RS004971 2012.
15. Clilverd, M. A., C. J. Rodger, D. Danskin, M. E. Usanova, T. Raita, Th. Ulich, and E. L. Spanswick, Energetic Particle injection, acceleration, and loss during the geomagnetic disturbances which upset Galaxy 15, J. Geophys. Res., 117, A12213, doi:10.1029/2012JA018175 2012.
16. Delport, B., A. B. Collier, J. Lichtenberger, C. J. Rodger, M. Parrot, M. A. Clilverd, and R. H. W. Friedel, Simultaneous observation of chorus and hiss near the plasmapause, J. Geophys. Res., 117, A12218, doi:10.1029/2012JA017609 2012.
17. Clilverd, M A, C J Rodger, I J Rae, J B Brundell, N R Thomson, N Cobbett, P T Verronen, and F W Menk, Combined THEMIS and ground-based observations of a pair of substorm associated electron precipitation events, J. Geophys. Res., 117, A02313, doi:10.1029/2011ja016933 2012.
We have presented our results on
- 20 invited oral conference presentations
- 53 normal oral conference presentations
- 39 poster conference presentations
- 28 seminars
- 8 public/popular talks and papers
- 10 media coverages
References:
Berube, D., M.B. Moldwin, and J.M. Weygand, An automated method for the detection of field line resonance frequencies using ground magnetometer techniques, J. Geophys. Res., 108, 1348, 2003.
Dowden, R.L and Allcock G. McK (1971): Determination of nose frequency of non-nose whistlers. J. Atmos. Terrestrial Phys.33 pp 1125-1129.
Green, A.W. E.W. Worthington, L.N. Baransky, E.N. Fedorov, N.A. Kurneva, V.A. Pilipenko, D.N. Shvetzov, A.A. Bektemirov, and G.V. Philipov, Alfve´n field line resonances at low latitudes
(L = 1.5) J. Geophys. Res., 98, 15693–15699, 1993.
Heilig, B., and H. Lühr, New plasmapause model derived from CHAMP field-aligned current signatures, Ann. Geophys., 31, 529-539, doi:10.5194/angeo-31-529-2013 2013
Heilig, B., S. Lotz, J. Vero, P. Sutcliffe, K.P.J. Reda, and T. Raita, Empirically modelled pc3 activity based on solar wind parameters, Ann. Geophys., 28, 1703–1722, 2010.
Heilig, B., P. R. Sutcliffe, D. C. Ndiitwani, and A. Collier, A statistical study of geomagnetic field line resonances observed by CHAMP and on the ground, J. Geophys. Res., 118, doi: 10.1002/jgra.50215 2013.
Heilig B., J. Lichtenberger, M. Vellante, J. Reda, T. Raita, P. Sutcliffe, M. Váczyová, D. Herak, M. Neska, L. Merényi, A. Csontos, P. Kovács, M. Srbecky, and I. Mandic, EMMA for near real time Monitoring of the Plasmasphere, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp., 127-130 San Fernando, Spain, 2013
Lichtenberger, J., Ferencz, Cs., Bodnár, L., Hamar, D. and Steinbach P. (2008): Automatic Whistler Detector and Analyzer (AWDA) system. Automatic Whistler Detector, J. Geophys. Res. 113, A12201, Doi: 10.1029/2008JA013467.
Lichtenberger, J. (2009): A new whistler inversion model, J. Geophys. Res., 114 A07222, Doi: 10.1029/2008JA013799
Lichtenberger, J., C. Ferencz, D. Hamar, P. Steinbach, C. J. Rodger, M. A. Clilverd, and A. B. Collier (2010), The Automatic Whistler Detector and Analyzer (AWDA) system: Implementation of the Analyzer Algorithm, J. Geophys. Res., 115 A12214, doi:10.1029/2010JA015931
Lichtenberger, J., M. Clilverd, B. Heilig, M. Vellante, J. Manninen, C. Rodger, A. Collier, A. Jorgensen, J. Reda, R. Holzworth, R. Friedel and M. Simon-Wedlund, The plasmasphere during a space weather event: First results from the PLASMON, J. Space Weather Space Climate, 3 (A3), http://dx.doi.org/10.1051/swsc/2013045 2013
Merényi, L, B. Heilig, and L. Szabados, Geomagnetic Data Acquisition System developed for the PLASMON project, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp. 54-56, San Fernando, Spain, 2013.
Singer, H.J. D.J. Southwood, R.J. Walker, and M.G. Kivelson, Alfve´n wave resonances in a realistic magnetospheric magnetic field geometry, J. Geophys. Res., 86, 4589–4596, 1981.
Tsyganenko, N.A. A model of the near magnetosphere with a dawndusk asymmetry 1. Mathematical structure, J. Geophys. Res., 107 (A8), SMP 12-1–SMP12-5, 2002a.
Tsyganenko, N.A. A model of the near magnetosphere with a dawndusk asymmetry 2. Parameterization and fitting to observations, J. Geophys. Res., 107 (A8), SMP 10-1–SMP 10-17, 2002b.
Vellante, M., and M. Förster, Inference of the magnetospheric plasma mass density from field line resonances: a test using a plasmasphere model, J. Geophys. Res., 111, A11204, 2006.
Vellante, M., H. Lühr, T.L. Zhang, V. Wesztergom, U. Villante, M. DeLauretis, A. Piancatelli, M. Rother, K. Schwingenschuh, W. Koren, and W. Magnes, Ground/satellite signatures of field line resonance: a test of theoretical predictions, J. Geophys. Res., 109, A06210, 2004.
Vellante, M., M. Piersanti, and E. Pietropaolo, Comparison of equatorial plasma mass densities deduced from field line resonances observed at ground for dipole and IGRF models, J. Geophys. Res., doi: 10.1002/2013JA019568 2014.
Waters, C.L. F.W. Menk, and B.J. Fraser, The resonance structure of low latitude pc3 geomagnetic pulsations, Geophys. Res. Lett., 18, 2283–2296, 1991.
Potential Impact:
WP1
Extended AWDANet:
The spatial and time occurrences of whistler events and whistler traces will be used to further investigate the whistler generation and propagation as well as the formation of plasmaspheric ducts. Thus will lead to optimal selection of further AWDANet locations.
Automatic Whistler Analayzer:
The palsmaspheric densities provided by the real-time mode of operation will be used in modeling wave-particle interactions in radiation belts. The cold density data can also be used for modeling-forecasting spacecraft charges.
We will process all the archive whistler data collected by AWDANet since 2002 creating a unique dataset of plasmaspheric equatorial electron densities that can be used in space weather model development and case studies.
The real-time data is planned to be used (together with plasma mass densities from WP2) in data assimilation model of the plasmasphere after the end of the project.
WP2
FLRID and FLRINV codes for monitoring the plasmasphere by means of FLRs:
FLRID and FLRINV codes can be applied to data recorded in another longitude sectors, too. In a case study our codes were used to process McMAC (USA) and CARISMA (Canada) data recorded in the American longitude sector. The adjustment of the FLRID parameters for the local conditions is a prerequisite in such a case, however.
In the future we plan collaborations with the above mentioned and SAMBA magnetometer arrays to extend the monitoring facility to a global one, at least for some case studies.
In an ideal case at least one magnetometer chain should be exposed to the compressional waves entering the magnetosphere at the subsolar point of the terrestrial bow shock. With such a global network (including MAGDAS stations in the Japan-Australian sector) the continuous global monitoring of the dayside plasmasphere could be achieved.
EMMA and SANSA magnetometer data:
The 1 s magnetic data recorded along close to the same magnetic meridian during the PLASMON period form a unique data set, especially in Europe. This dataset makes possible the investigation of different natural phenomena (processes in the Earth interior, currents in the ionosphere and magnetosphere, ULF waves and their interaction with the ionosphere) especially when combined with independent data sources (e.g. in-situ satellite magnetic and wave measurements) as well as their meridional distributions.
Direct use of EMMA and SANSA data is inside the PLASMON consortium. These data are used to monitor the plasma mass density in the plasmasphere and in the plasma trough.
*EMMA data (or part of the chain) are also used in other projects (e.g. EU FP7 STORM, ESA SWARM EMISSARS). We expect a lot of users of our data from different fields: geomagnetism, exploration for raw materials, global modeling of the geomagnetic field, space physics, education, etc. EMMA and SANSA has been completed in good time to support ESA’s SWARM mission (magnetic field modeling, current systems, ULF waves, ionosphere etc.) and NASA’s VAP mission (plasmasphere, radiation belts).
To reach the widest community we make all our data at 1 min resolution openly available through the SuperMAG consortium (http://supermag.jhuapl.edu/).
For the sake of the ULF wave research community we also make all the 1s data available on the EMMA servers (e.g. at http://plasmonserver.aquila.infn.it/) where also the ‘Rules of Road’ of the data policy are being defined. The availability of archived data refreshed daily can be checked at http://geofizika.canet.hu/plasmon/emmaavailarchy.php.
Equatorial plasma mass density data:
Equatorial plasma mass density data inferred by FLRID and FLRINV from EMMA and SANSA magnetometer data are the main product of WP2. WP3 uses these data directly as an input of a data assimilative model of the plasmasphere. Our equatorial plasma mass densities are obtained from daytime FLR observations made in the European-African sector. Data are available for the L range 1.4-6.4 from 2012 depending on the availability and quality of the corresponding magnetometer data and also on ULF wave activity for the period in question.
We plan to use the density to monitor the state of the plasmasphere, to obtain average ion mass estimates, to develop empirical as well as data assimilative models of the plasmasphere.
An important product derivable from radial mass density profiles is the position of the plasmapause. To support this goal we are developing alternative ways to obtain the plasmapause position from EMMA data as well as from magnetic observations made by SWARM at low-Earth-orbit.
Validation/calibration activity will be continued making use of the availability of VAP in-situ observations in the plasmasphere and in the plasma trough.
Our approach offers a cheap and efficient way to monitor the dynamics of the plasmasphere using only ground observations. Our data are published on the L’Aquila PLASMON EMMA Server:
http://plasmonserver.aquila.infn.it/EMMA_FLR_DENSITY
PLASMON magnetometer data loggers
The magnetic data acquisition system developed by MFGI could be used at any geomagnetic observatory or variation station to produce 1 s data fulfilling the requirements of INTERMAGNET (www.intermagnet.org).
WP3:
The plasmasphere is a critical component of the magnetosphere ionosphere system where it is a source of wave activity responsible for decay and acceleration of radiation belt particles, an important component of “space weather." Space weather in turn threatens both ground-based and space-based technological infrastructure.
Mitigating the eff ects of space weather requires reliable forecasts of the sate of the radiation belt and inner magnetosphere. Waves play an important role in both the acceleration and decay of radiation belt particles. The thermal plasma in the plasmasphere determines resonance conditions for particles, instability criteria for waves generation, and propagation characteristics of waves. Boundary gradients such as the plasmapause separate distinct regions of wave activity, and thus control the location of dominant loss or acceleration mechanisms due to these waves. The plasmasphere is thus one of the most signi ficant drivers of radiation belt activity, yet is the least constrained in recent inner magnetosphere modeling efforts.
Current plasmasphere models implement the essential features of the plasmasphere such as ionospheric sources and sinks of plasma as well as the convective transport. But an important piece is unknown, and that is the detailed form and strength of the convection electric fi eld. It appears that our current knowledge of the electric field is insuffi cient to produce the most reliable plasma density maps needed as inputs to radiation belt models. The solution is to use data assimilation to constrain the evolution of the convection electric field and thus the plasma density distribution with observations. This work will have a direct impact on the accuracy of radiation belt models because it will improve the accuracy of some critical driver inputs to radiation belt models. Several groups which do radiation belt modeling have been in contact with us to use the PLASMON assimilative plasmasphere model to improve the accuracy of their predictions.
Main dissemination activities:
The data assimilation model and related results have been presented at a number of conferences. A journal publication is in the process of being prepared. The data assimilation model code is available for download from the PLASMON web site. We are also in the process of creating a website which will produce real-time plasma density maps from real-time PLASMON data. We have begun discussions with radiation belt modeling groups about using the PLASMON assimilation model for their plasmasphere input
Explanation of results:
We have designed and implemented a data assimilation framework for plasmasphere data assimilation. This framework will accept any measurements which can be used to constrain the plasmasphere. The data assimilation and model combination has produce a new, improved model of the plasma con guration in the inner magnetosphere. This model can be run with, in principle, any future data sets to produce a better plasma density map than existing non-assimilative models.
WP4
Extended AARDDVARK:
The extended AARDDVARK network is able to detect the precipitation of high energy electron from the outer radiation belts in the Northern Hemisphere. The detected precipitations can be used to validate precipitations predicted by the radiation belts models.
REP models:
We have developed numerical algorithms to calculate the Relativistic Electron Precipitation fluxes at three regions (outside/inside and on the plasmapause) thus algorithm can be used to predict the REP and its effects in operational space weather application and services.
The plasmasphere models from data assimilation runs will be used in the future together with the REP flux algorithm. The precipitation model uses the plasmasphere density maps generated by WP3 every 20 minutes, determines the plasmapause location, finds the geomagnetic conditions, and calculates the fluxes and spectrum.
WP5
Dissemination of the results:
Scientific Papers
1. Whittaker, I. C., C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, The effects and correction of the geometric factor for the POES/MEPED electron flux instrument using a multi-satellite comparison, J. Geophys. Res., 2014JA020021 (in press), 2014.
2. Antel, C., A. B. Collier, J. Lichtenberger, and C. J. Rodger, Investigating Dunedin Whistlers using Volcanic Lightning, Geophys. Res. Lett., 41, doi:10.1002/2014GL060332 2014.
3. Vellante, M., M. Piersanti, and E. Pietropaolo, Comparison of equatorial plasma mass densities deduced from field line resonances observed at ground for dipole and IGRF models, J. Geophys. Res., doi: 10.1002/2013JA019568 2014.
4. Whittaker, I. C., R. J. Gamble, C. J. Rodger, M. A. Clilverd and J. A. Sauvaud, Determining the spectra of radiation belt electron losses: Fitting DEMETER IDP observations for typical and storm-times, J. Geophys. Res., doi:10.1002/2013JA019228 2013.
5. Lichtenberger, J., M. Clilverd, B. Heilig, M. Vellante, J. Manninen, C. Rodger, A. Collier, A. Jorgensen, J. Reda, R. Holzworth, R. Friedel and M. Simon-Wedlund, The plasmasphere during a space weather event: First results from the PLASMON, J. Space Weather Space Climate, 3 (A3), http://dx.doi.org/10.1051/swsc/2013045 2013.
6. Heilig, B., and H. Lühr, New plasmapause model derived from CHAMP field-aligned current signatures, Ann. Geophys., 31, 529-539, doi:10.5194/angeo-31-529-2013 2013.
7. Heilig, B., P. R. Sutcliffe, D. C. Ndiitwani, and A. Collier, A statistical study of geomagnetic field line resonances observed by CHAMP and on the ground, J. Geophys. Res., 118, doi: 10.1002/jgra.50215 2013.
8. Carson, B. R., C. J. Rodger, and M. A. Clilverd, POES Satellite Observations of EMIC-wave driven Relativistic Electron Precipitation during 1998-2010, J. Geophys. Res., 118, 1–12, doi:10.1029/2012JA017998 2013.
9. Hendry, A. T., C. J. Rodger, M. A. Clilverd, N. R. Thomson, S. K. Morley, and T. Raita, Rapid radiation belt losses occurring during high-speed solar wind stream–driven storms: Importance of energetic electron precipitation, in Dynamics of the Earth's Radiation Belts and Inner Magnetosphere, Geophys. Monogr. Ser., vol. 199, edited by D. Summers et al., 213–223, AGU, Washington, D. C., doi:10.1029/2012GM001299 2013.
10. Merényi, L, B. Heilig, and L. Szabados, Geomagnetic Data Acquisition System developed for the PLASMON project, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp. 54-56, San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
11. Heilig B., J. Lichtenberger, M. Vellante, J. Reda, T. Raita, P. Sutcliffe, M. Váczyová, D. Herak, M. Neska, L. Merényi, A. Csontos, P. Kovács, M. Srbecky, and I. Mandic, EMMA for near real time Monitoring of the Plasmasphere, Proceedings of the XVth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing, 2012, pp., 127-130 San Fernando, Spain, 2013. Available online from http://www.portalcultura.mde.es/Galerias/revistas/ficheros/Boletin_ROA_03_2013.pdf
12. Lichtenberger J., and Cs. Ferencz, Űridőjárási folyamatok a magnetoszférában (Space weather processes in the magnetosphere), Magyar Geofizika, 53 (3), pp.182-190 2012.
13. Kovács P., A. Csontos, B. Heilig, L. Hegymegi, L. Merényi, G. Vadász, A. Koppán, and A. Földmágnesség, A Tihanyi Geofizikai Obszervatórium (The Tihany Geophysical Observatory), Magyar Geofizika, 53. évf. 3. sz., 191-203. p. (in Hungarian), 2012.
14. Rodger, C. J., M. A. Clilverd, A. J. Kavanagh, C. E. J. Watt, P. T. Verronen, and T. Raita, Contrasting the responses of three different ground-based instruments to energetic electron precipitation, Radio Sci., doi:10.1029/2011RS004971 2012.
15. Clilverd, M. A., C. J. Rodger, D. Danskin, M. E. Usanova, T. Raita, Th. Ulich, and E. L. Spanswick, Energetic Particle injection, acceleration, and loss during the geomagnetic disturbances which upset Galaxy 15, J. Geophys. Res., 117, A12213, doi:10.1029/2012JA018175 2012.
16. Delport, B., A. B. Collier, J. Lichtenberger, C. J. Rodger, M. Parrot, M. A. Clilverd, and R. H. W. Friedel, Simultaneous observation of chorus and hiss near the plasmapause, J. Geophys. Res., 117, A12218, doi:10.1029/2012JA017609 2012.
17. Clilverd, M A, C J Rodger, I J Rae, J B Brundell, N R Thomson, N Cobbett, P T Verronen, and F W Menk, Combined THEMIS and ground-based observations of a pair of substorm associated electron precipitation events, J. Geophys. Res., 117, A02313, doi:10.1029/2011ja016933 2012.
We have presented our results on
- 20 invited oral conference presentations
- 53 normal oral conference presentations
- 39 poster conference presentations
- 28 seminars
- 8 public/popular talks and papers
- 10 media coverages
Education
Both the data and the know-how developed in frame of PLASMON have already been used in university courses and as research topic of PhD students (University of L’Aquila, Italy; Eötvös University, Hungary, University of Otago, New Zealand and New Mexico Tech, NM, USA).
List of Websites:
http://plasmon.elte.hu
