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Register a new userDiffusion of solar magnetic elements up to supergranular spatial and temporal scales
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The study of spatial and temporal scales on which small magnetic structures (magnetic elements) are organized in the quiet Sun may be approached by determining how they are transported on the solar photosphere by convective motions. The process involved is diffusion. Taking advantage of Hinode high spatial resolution magnetograms of a quiet Sun region at the disk center, we tracked 20145 magnetic elements. The large field of view (~50 Mm) and the long duration of the observations (over 25 hours without interruption at a cadence of 90 seconds) allowed us to investigate the turbulent flows at unprecedented large spatial and temporal scales. In the field of view, in fact, an entire supergranule is clearly recognizable. The magnetic elements displacement spectrum shows a double-regime behavior: superdiffusive (gamma=1.34 +/- 0.02) up to granular spatial scales (~1500 km), and slightly superdiffusive (gamma=1.20 +/- 0.05) up to supergranular scales.
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Small scale magnetic fields (magnetic elements) are ubiquitous in the solar photosphere. Their interaction can provide energy to the upper atmospheric layers, and contribute to heat the solar corona. In this work, the dynamic properties of magnetic elements in the quiet Sun are investigated. The high number of magnetic elements detected in a supegranular cell allowed us to compute their displacement spectrum $langle(Delta r)^2rangleproptotau^gamma$ (being $gamma>0$, and $tau$ the time since the first detection), separating the contribution of the network (NW) and the internetwork (IN) regions. In particular, we found $gamma=1.27pm0.05$ and $gamma=1.08pm0.11$ in NW (at smaller and larger scales, respectively), and $gamma=1.44pm0.08$ in IN. These results are discussed in light of the literature on the topic, as well as the implications for the build up of the magnetic network.
In a recent study, we took advantage of a highly tilted coronal neutral sheet to show that density structures, extending radially over several solar radii (Rs), are released in the forming slow solar wind approximately 4-5 Rs above the solar surface (Sanchez-Diaz et al. 2017). We related the signatures of this formation process to intermittent magnetic reconnection occurring continuously above helmet streamers. We now exploit the heliospheric imagery from the Solar Terrestrial Relation Observatory (STEREO) to map the spatial and temporal distribution of the ejected structures. We demonstrate that streamers experience quasi-periodic bursts of activity with the simultaneous outpouring of small transients over a large range of latitudes in the corona. This cyclic activity leads to the emergence of well-defined and broad structures. Derivation of the trajectories and kinematic properties of the individual small transients that make up these large-scale structures confirms their association with the forming Slow Solar Wind (SSW). We find that these transients are released, on average, every 19.5 hours, simultaneously at all latitudes with a typical radial size of 12 Rs. Their spatial distribution, release rate and three-dimensional extent are used to estimate the contribution of this cyclic activity to the mass flux carried outward by the SSW. Our results suggest that, in interplanetary space, the global structure of the heliospheric current sheet is dominated by a succession of blobs and associated flux ropes. We demonstrated this with an example event using STEREO-A in-situ measurements.
The solar wind is highly structured in fast and slow flows. These two dynamical regimes remarkably differ not only for the average values of magnetic field and plasma parameters but also for the type of fluctuations they transport. Fast wind is characterized by large amplitude, incompressible fluctuations, mainly Alfv{e}nic, slow wind is generally populated by smaller amplitude and less Alfv{e}nic fluctuations, mainly compressive. The typical corotating fast stream is characterized by a stream interface, a fast wind region and a slower rarefaction region formed by the trailing expansion edge of the stream. Moving {between these two regions}, from faster to slower wind, we observe the following behavior: a) the power level of magnetic fluctuations within the inertial range largely decreases, keeping the typical Kolmogorov scaling; b) at proton scales, for about one decade right beyond the high frequency break, the spectral index becomes flatter and flatter towards a value around -2.7; c) at higher frequencies, before the electron scales, the spectral index remains around -2.7 and, {based on suitable observations available for $4$ corotating streams}, the power level does not change, irrespective of the flow speed. All these spectral features, characteristic of high speed streams, suggest the existence of a sort of magnetic field background spectrum. This spectrum would be common to both faster and slower wind but, any time the observer would cross the inner part of a fluxtube channeling the faster wind into the interplanetary space, a turbulent and large amplitude Alfv{e}nic spectrum would be superposed to it.
Dissipation of magnetohydrodynamic (MHD) wave energy has been proposed as a viable heating mechanism in the solar chromospheric plasma. Here, we use a simplified one-dimensional model of the chromosphere to theoretically investigate the physical processes and the spatial scales that are required for the efficient dissipation of Alfven waves and slow magnetoacoustic waves. We consider the governing equations for a partially ionized hydrogen-helium plasma in the single-fluid MHD approximation and include realistic wave damping mechanisms that may operate in the chromosphere, namely Ohmic and ambipolar magnetic diffusion, viscosity, thermal conduction, and radiative losses. We perform an analytic local study in the limit of small amplitudes to approximately derive the lengthscales for critical damping and efficient dissipation of MHD wave energy. We find that the critical dissipation lengthscale for Alfven waves depends strongly on the magnetic field strength and ranges from 10~m to 1~km for realistic field strengths. The damping of Alfven waves is dominated by Ohmic diffusion for weak magnetic field and low heights in the chromosphere, and by ambipolar diffusion for strong magnetic field and medium/large heights in the chromosphere. Conversely, the damping of slow magnetoacoustic waves is less efficient, and spatial scales shorter than 10~m are required for critical damping. Thermal conduction and viscosity govern the damping of slow magnetoacoustic waves and play an equally important role at all heights. These results indicate that the spatial scales at which strong wave heating may work in the chromosphere are currently unresolved by observations.
This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was evaluated across multiple sensors and for three different oceanic variables: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural environmental system -- across the spatial (between individual sensors) and temporal components of the sensor data. Data from four sensors sampling current speed, and eight measuring both temperature and dissolved oxygen evaluated the framework. Results were compared against RF and XGB baseline models that learned on the temporal signal of each sensor independently by extracting the date-time features together with the past history of data using sliding window matrix. Results demonstrated ability to accurately replicate complex signals and provide comparable performance to state-of-the-art benchmarks. Notably, the novel framework provided a simpler pre-processing and training pipeline that handles missing values via a simple masking layer. Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions. Furthermore, sharing of parameters across all input steps makes SPATIAL a fast, scalable, and easily-parameterized forecasting framework.
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