Comparison of magnetic field observations of an average magnetic cloud with a simple force free model: the importance of field compression and expansion

International audienceWe investigate the ability of the cylindrically symmetric force-free magnetic cloud (MC) fitting model of Lepping et al. (1990) to faithfully reproduce actual magnetic field observations by examining two quantities: (1) a difference angle, called ?, i.e., the angle between the direction of the observed magnetic field (Bobs) and the derived force free model field (Bmod) and (2) the difference in magnitudes between the observed and modeled fields, i.e., ?B(=|Bobs|?|Bmod|), and a normalized ?B (i.e., ?B/B>) is also examined, all for a judiciously chosen set of 50 WIND interplanetary MCs, based on quality considerations. These three quantities are developed as a percent of MC duration and averaged over this set of MCs to obtain average profiles. It is found that, although B> and its normalize version are significantly enhanced (from a broad central average value) early in an average MC (and to a lesser extent also late in the MC), the angle is small (less than 8°) and approximately constant all throughout the MC. The field intensity enhancements are due mainly to interaction of the MC with the surrounding solar wind plasma causing field compression at front and rear. For example, for a typical MC, ?B/B> is: 0.21±0.27 very early in the MC, ?0.11±0.10 at the center (and ?0.085±0.12 averaged over the full "central region," i.e., for 30% to 80% of duration), and 0.05±0.29 very late in the MC, showing a double sign change as we travel from front to center to back, in the MC. When individual MCs are examined we find that over 80% of them possess field enhancements within several to many hours of the front boundary, but only about 30% show such enhancements at their rear portions. The enhancement of the MC's front field is also due to MC expansion, but this is usually a lesser effect compared to compression. It is expected that this compression is manifested as significant distortion to the MC's cross-section from the ideal circle, first suggested by Crooker et al. (1990), into a more elliptical/oval shape, as some global MC studies seem to confirm (e.g., Riley and Crooker, 2004) and apparently also as confirmed for local studies of MCs (e.g., Hidalgo et al., 2002; Nieves-Chinchilla et al., 2005)

Interplanetary Magnetic Field Power Spectrum Variations: A VHO Enabled Study

The newly reprocessed high time resolution (11/22 vectors/sec) Wind mission interplanetary magnetic field data and the solar wind key parameter search capability of the Virtual Heliospheric Observatory (VHO) affords an opportunity to study magnetic field power spectral density variations as a function of solar wind conditions. In the reprocessed Wind Magnetic Field Investigation (MFI) data, the spin tone and its harmonics are greatly reduced that allows the meaningful fitting of power spectra to the approx.2 Hz limit above which digitization noise becomes apparent. The power spectral density is computed and the spectral index is fitted for the MHD and ion inertial regime separately along with the break point between the two for various solar wind conditions . The time periods of fixed solar wind conditions are obtained from VHO searches that greatly simplify the process. The functional dependence of the ion inertial spectral index and break point on solar wind plasma and magnetic field conditions will be discusse

A summary of WIND magnetic clouds for years 1995-2003: model-fitted parameters, associated errors and classifications

International audienceInterplanetary magnetic clouds (MCs) have been identified for the first 8.6 years of the WIND mission, and their magnetic field structures have been parameter-fitted by a static, force free, cylindrically-symmetric model (Lepping et al., 1990) with various levels of success. This paper summarizes various aspects of the results of the model fitting by providing: seven estimated model fit-parameter values for each of the 82 MCs found, their objectively determined quality estimates, closest approach vectors (in two coordinate frames), fit-parameter errors for the cases of acceptable quality (50 cases, or 61%), axial magnetic fluxes, axial current densities, and total axial current - as well as some examples of MC profiles for various conditions and "categories" for each case (e.g. Bz: N?S or S?N, etc.). MC quality is estimated from a quantitative consideration of a large set of parameters, such as the chi-squared of the model fit, degree of asymmetry of the B profile, and a comparison of two means of estimating radius. This set of MCs was initially identified by visual inspection of relevant field and plasma data. Each resulting MC candidate is then tested through the use of the MC parameter model, for various adjusted durations to determine the best fit, which helps to refine the boundary-times. The resulting MC set is called Set 1. Another, larger, set (Set 2) of MCs is identified through an automated program whose criteria are based on general MC plasma and field characteristics at 1AU determined through past experience. Set 1 is almost fully contained within Set 2, whose frequency of occurrence better matches that of the sunspot cycle than Set 1. The difference-set (Set 2-Set 1) is referred to as the magnetic cloud-like (MCL) set, whose members do not very well represent good flux ropes through modeling. We present a discussion of how a MC's front boundary is specifically identified in terms of multi-parameter considerations (i.e. any one or more of: increase in B, directional discontinuity, magnetic hole in B, drop in proton plasma beta, B-fluctuation level change, proton temperature drop, etc.), as well as through the application of the flux rope model. Also presented are examples of unusual MCs, as well as some commonly occurring relationships, such as the existence and frequency (approx. 1/2 the time) of upstream interplanetary shocks, and less frequent internal shocks

The Space Physics Environment Data Analysis System (SPEDAS)

With the advent of the Heliophysics/Geospace System Observatory (H/GSO), a complement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data Analysis System (SPEDAS), a grass-roots software development platform (www.spedas.org), is now officially supported by NASA Heliophysics as part of its data environment infrastructure. It serves more than a dozen space missions and ground observatories and can integrate the full complement of past and upcoming space physics missions with minimal resources, following clear, simple, and well-proven guidelines. Free, modular and configurable to the needs of individual missions, it works in both command-line (ideal for experienced users) and Graphical User Interface (GUI) mode (reducing the learning curve for first-time users). Both options have “crib-sheets,” user-command sequences in ASCII format that can facilitate record-and-repeat actions, especially for complex operations and plotting. Crib-sheets enhance scientific interactions, as users can move rapidly and accurately from exchanges of technical information on data processing to efficient discussions regarding data interpretation and science. SPEDAS can readily query and ingest all International Solar Terrestrial Physics (ISTP)-compatible products from the Space Physics Data Facility (SPDF), enabling access to a vast collection of historic and current mission data. The planned incorporation of Heliophysics Application Programmer’s Interface (HAPI) standards will facilitate data ingestion from distributed datasets that adhere to these standards. Although SPEDAS is currently Interactive Data Language (IDL)-based (and interfaces to Java-based tools such as Autoplot), efforts are under-way to expand it further to work with python (first as an interface tool and potentially even receiving an under-the-hood replacement). We review the SPEDAS development history, goals, and current implementation. We explain its “modes of use” with examples geared for users and outline its technical implementation and requirements with software developers in mind. We also describe SPEDAS personnel and software management, interfaces with other organizations, resources and support structure available to the community, and future development plans