We present a statistical study of propagation times of solar wind discontinuities between Advanced Composition Explorer (ACE) spacecraft orbiting the L1 libration point and the Cluster quartet of spacecraft near the Earth's magnetopause. The propagation times for almost 200 events are compared with the predicted times from four different models. The simplest model assumes a constant convective motion of solar wind disturbances along the Sun-Earth line, whereas more sophisticated models take the orientation of the discontinuity as well as the real positions of the solar wind monitor and target into account. The results show that taking orientation and real position of the solar wind monitor and target into account gives a more precise time delay estimation in most cases. In particular, we show that recent modifications to the minimum variance technique can improve the estimation of propagation times of solar wind discontinuities.

In this paper we investigate quantitatively the effect of data gaps for four methods of estimating the amplitude spectrum of a time series: fast Fourier transform (FFT), discrete Fourier transform (DFT), Z transform (ZTR) and the Lomb–Scargle algorithm (LST). We devise two tests: the single-large-gap test, which can probe the effect of a single data gap of varying size and the multiple-small-gaps test, used to study the effect of numerous small gaps of variable size distributed within the time series. The tests are applied on two data sets: a synthetic data set composed of a superposition of four sinusoidal modes, and one component of the magnetic field measured by the Venus Express (VEX) spacecraft in orbit around the planet Venus. For single data gaps, FFT and DFT give an amplitude monotonically decreasing with gap size. However, the shape of their amplitude spectrum remains unmodified even for a large data gap. On the other hand, ZTR and LST preserve the absolute level of amplitude but lead to greatly increased spectral noise for increasing gap size. For multiple small data gaps, DFT, ZTR and LST can, unlike FFT, find the correct amplitude of sinusoidal modes even for large data gap percentage. However, for in-situ data collected in a turbulent plasma environment, these three methods overestimate the high frequency part of the amplitude spectrum above a threshold depending on the maximum gap size, while FFT slightly underestimates it.

We investigate Venus Express observations of magnetic field fluctuations performed systematically in the solar wind at 0.72 Astronomical Units (AU), between 2007 and 2009, during the deep minimum of solar cycle 24. The power spectral densities (PSDs) of the magnetic field components have been computed for time intervals that satisfy the data integrity criteria and have been grouped according to the type of wind, fast and slow, defined for speeds larger and smaller, respectively, than 450 km s⁻¹. The PSDs show higher levels of power for the fast wind than for the slow. The spectral slopes estimated for all PSDs in the frequency range 0.005–0.1 Hz exhibit a normal distribution. The average value of the trace of the spectral matrix is −1.60 for fast solar wind and −1.65 for slow wind. Compared to the corresponding average slopes at 1 AU, the PSDs are shallower at 0.72 AU for slow wind conditions suggesting a steepening of the solar wind spectra between Venus and Earth. No significant time variation trend is observed for the spectral behavior of both the slow and fast wind.

In this study, we investigate the global ionospheric impact of high‐speed solar wind streams/corotating interaction regions (HSS/CIR). A series of 10 such events are identified between December 1, 2007 and April 29, 2008, characterized in the frequency domain by the main spectral peaks corresponding to 27, 13.5, 9, and 6.75 days. The spectra of solar wind magnetic field, speed, and proton density, as well as those of the geomagnetic indices AE and SYM‐H, are solely dominated by these features. By contrast, the ionospheric NmF₂ and to a lesser extent the hmF₂ spectra have a much more complex structure, with secondary peaks adding to or replacing the main ones. We argue that this is evidence of the nonlinear nature of the magnetosphere‐ionosphere coupling, highlighted particularly in the NmF₂ ionospheric response. Additionally, we show that hmF₂ is more closely correlated than NmF₂ to all parameters describing the solar wind and geomagnetic activity. Finally, the ionospheric response shows a higher correlation with B_z than any other solar wind parameter, and higher with SYM‐H than AE, indicating that for the low‐frequency part of the spectrum, high‐latitude Joule heating and particle precipitation play a secondary role to that of prompt penetration electric fields in dictating the ionospheric response to geomagnetic activity, in the case of this sequence of HSS/CIR events.

We present a statistical study of the performance of three methods used to predict the propagation delay of solar wind structures. These methods are based on boundary normal estimations between the Advanced Composition Explorer (ACE) spacecraft orbiting the L1 libration point and the Cluster spacecraft near the Earth's magnetopause. The boundary normal estimation methods tested are the cross product method (CP), the minimum variance analysis of the magnetic field (MVAB), and the constrained minimum variance analysis (MVAB0). The estimated delay times are compared with the observed ones to obtain a quantitative measure of each method's accuracy. Boundary normal estimations of magnetic field structures embedded in the solar wind are known to be sensitive to small‐scale fluctuations. Our study uses wavelet denoising to reduce the effect of these fluctuations. The influence of wavelet denoising on the performance of the three methods is also analyzed. We find that the free parameters of the three methods have to be adapted to each event in order to obtain accurate propagation delays. We also find that by using denoising parameters optimized to each event, 88% of our database of 356 events are estimated to arrive within ±2 min from the observed time delay with MVAB, 74% with CP, and 69% with the MVAB0 method. Our results show that wavelet denoising significantly improves the predictions of the propagation time delay of solar wind discontinuities.

Data processing is a challenging problem in space applications. The limited bandwidth available for communication between satellites and the ground and the increasing resolution of scientific instruments make it virtually impossible to transfer all the data recorded on board. Although various mitigation strategies were developed, large amounts of on-board data are still lost. This paper presents a Field Programmable Gate Array (FPGA)-based architecture which is able to perform on-board nonlinear analysis of data and compute probability distribution functions of fluctuations. We propose two implementations for our solution, which can be used for space applications and also other computational contexts. On a spacecraft, the logic resources of the FPGA will typically be shared by several designs running various digital signal processing algorithms. That is why each algorithm should be designed in variants, optimized for different criteria, so that the entire group of algorithms makes an efficient usage of the FPGA resources. The proposed solution focuses on two major optimization criteria, area and speed, such that the FPGA resources are efficiently used. Also, the power consumption is at least two orders of magnitude less in comparison with classical software implementations. The solution was tested with both synthetic and real data and shows excellent results paving the way towards an application that can be ported on a space-grade FPGA.