Wind Of Change Instrumental Mp3 Download

[Zacharie Brodhacker]

Windshear describes how the wind changes speed and/or direction with height. There is almost always some degree of wind shear present within the atmosphere: however, it tends to be much greater across central Illinois during the winter and spring when strong storm systems frequently impact the region. For example, when a powerful jet stream shifts overhead, wind speeds from the surface to jet stream level can increase by over 100mph. The example below shows a diagram of winds from ground-level up to an altitude of 6km (about 3.7 mi). The direction the wind barb points tells us the direction from which the wind is blowing and the number of sticks or flags on the barb tell us the speed of the wind. A small stick equals 5kt, long stick 10kt, and a flag is 50kt. As you can see from the diagram, the wind at the surface is from the southeast at 20kt while the wind 6km aloft is from the west-southwest at 55kt. This represents a wind speed change of 35kt (40mph) and a wind direction change of about 45 degrees.

Wind shear is important to severe thunderstorm forecasting, because if it becomes strong enough, it can tilt a storm's updraft in such a manner that the updraft and downdraft remain separate from one another. If this occurs, the storm's energy source (it's warm/moist updraft) will not get choked off by the downdraft, and thus the storm will be able to maintain itself for a prolonged period of time. The image below shows two storms...one in a very weakly sheared environment on the left, and another in a strongly sheared environment on the right. The storm experiencing little or no wind shear will produce a vertical updraft, which will quickly get killed off by falling rain. On the other hand, the storm experiencing strong wind shear will develop a tilted updraft with the rain falling away from the updraft.

Wind shear can also enhance rotation within the thunderstorm updraft, which in turn can lead to the development of a tornado. The picture below shows light westerly winds at the surface with very strong westerly winds aloft. This creates a rolling motion within the atmosphere as shown by the red arrow, which if ingested into a storm's updraft can lead to the formation of a supercell thunderstorm and thus enhance the probability of a tornado.

For these reasons, strong wind shear is a chief indicator of long-lived and potentially severe thunderstorms. Meteorologists assess wind shear in a number of ways, but one of the primary tools is 0-6km bulk shear. This parameter describes the deep-layer wind shear from the surface up to 6km aloft. Supercell thunderstorms become more likely as values increase to greater than 35kt. As always, wind shear is just one of many tools available for severe weather forecasting and should not be used alone in order to determine the potential for strong to severe thunderstorms.

The solar wind is a highly turbulent medium in which most of the energy is carried by Alfvnic fluctuations. These fluctuations have a wide range of scales whose high-frequency tail can be relevant for the sampling techniques commonly used to detect the particle distribution in phase space in situ. We analyze the effect of Alfvnic fluctuations on moments computation of the solar wind proton velocity distribution for a plasma sensor, whose sampling time is comparable or even longer than the typical timescale of the velocity fluctuations induced by these perturbations. In particular, we numerically simulated the sampling procedure used on board Helios 2. We directly employed magnetic field data recorded by the Helios 2 magnetometer, when the s/c was immersed in fast wind during its primary mission to the Sun, to simulate Alfvnic fluctuations. More specifically, we used magnetic field data whose cadence of 4 Hz is considerably higher than that the plasma sensor needed to sample a full velocity distribution function, and we average these data to 1 Hz, which is the spin period of Helios. Density values, which are necessary to build Alfvnic fluctuations at these scales, are not available because the cadence of the Helios plasma data is 40.5 s. The adopted solution is based on the assumption that the available Helios plasma density power spectrum can be extended to the same frequencies as the magnetic field spectrum by extrapolating the power-law fit of the low-frequency range to the frequencies relevant for this study. Surrogate density values in the time domain are then obtained by inverse transforming this spectrum. We show that it cannot be excluded that relevant instrumental effects strongly contribute to generate interesting spectral and kinetic features that have been interpreted in the past literature as exclusively due to physical mechanisms.

Another interesting feature is related to the intermittent character of the fluctuations. In this respect, Bruno et al. (2014) studied fast Alfvnic streams observed by Helios 2 in the inner heliosphere. They found that the intermittency of number density fluctuations, at odds with fluctuations of all the other solar wind parameters, decreases during the wind expansion. In contrast, within the slow and non-Alfvnic wind, density fluctuations do not show any clear radial evolution (Bruno et al. 2014). In particular, the same authors found that the intermittent events seem to derive from some non-Poissonian mechanism, and the behavior of density fluctuations generated in a numerical study of the nonlinear evolution of parametric instability was qualitatively similar to that observed in situ by Helios. Thus, the authors concluded that parametric decay might play a role in generating the observed spectral feature of the density fluctuations.

On the other hand, we cannot exclude a priori that instrumental effects might have altered the estimate of plasma parameters such as the proton number density. This suspicion derives from the consideration that Helios, as we describe in more detail in the following section, took a rather long time to sample the whole proton distribution function in phase space. The sampling time of the Helios plasma instrument was on the same order as the time period of Alfvnic fluctuations, which are particularly relevant within the fast wind at short heliocentric distances. These fluctuations could produce non-negligible oscillations of the particle velocity distribution function (VDF hereafter) in phase space during the sampling time (Perrone et al. 2014). Nicolaou et al. (2019) recently numerically evaluated the effects of turbulence velocity fluctuations on the expected measurements by the Proton Alpha Sensor (PAS) on board Solar Orbiter. The authors compared bulk parameters derived from statistical moments of their modeled turbulent environment to bulk parameters derived from simulated PAS measurements. Based on the short sampling time of about 1 s, they concluded that, in case of PAS measurements, the effects of turbulence would be minimum. In contrast, we show that the rather slow sampling time of the Helios plasma sensor had a non-negligible effect on the moments computation of the proton VDF. In particular, we show that interesting spectral and kinetic features observed in number density and proton temperature, which have been interpreted as exclusively due to physical mechanisms, might also hide relevant instrumental effects.

Fig. 1.Left panel: time series of the bulk speed VSW, the number density N, the temperature Tp, and the magnetic field intensity B from part of the trailing edge of the fast wind stream encountered at 0.3 AU. The time interval we selected corresponds to the shaded area. Middle panel: power spectral densities of the number density for real data (black line) and simulated data (red line) for the selected time interval. The dashed blue line refers to the fit of the low-frequency part of the spectrum. Right panel: power spectral densities of the radial temperature for real data (black line) and simulated data (red line) for the selected time interval. The dashed blue line refers to the fit of the low-frequency part of the spectrum.

Fig. 3.Left panel: time series of number density of data derived from inverse transform of the fit of the low-frequency part of the spectrum (blue trace) used as input data for simulated measurement (red trace) and difference between the previous signals (green trace). Right panel: power spectral density of the number density with the same color code as in left panel.

In Fig. 7, we finally show the result produced by this simulation with regard to the proton number density (left panel) and temperature (right panel) intermittency studied in terms of the kurtosis values. Results from the intermittency analysis highlight the Gaussian character of the 1 s values that were used as input and the rather intermittent character of the 40.5 s values obtained from our simulation, which is quite similar to the intermittency that is observed in real data. These results also agree with a radial evolution of the number density intermittency (Bruno et al. 2014) and show for the first time, to our knowledge, a similar radial trend for the proton temperature.

Although the results are quite encouraging, one aspect of this simulation requires a moment of reflection. The synthetic density and temperature 1 s data that we used to feed the code to produce 40.5 s density and temperature moments, are characterized by fluctuations with typical Gaussian statistics, by construction. However, the fluctuations of the time series of the resulting 40.5 s measurements are clearly intermittent, just like real data. This result is only apparently contradictory when we consider that most of the noise that is generated during the VDF sampling is due to the arc-like motion of the VDF within the 3D velocity space. This motion is due to Alfvnic fluctuations constructed on real magnetic field observations, which, as we know from the literature (see the review by Bruno & Carbone 2013 and the many references listed in there), are increasingly intermittent from large to increasingly smaller scales. This clearly introduces a first element of non-Gaussianity to which we have to add the complexity of the moments calculation, which is not a simple snapshot picture. As a consequence, we should not expect a linear response from the moments computation, and the intermittent character of the 40.5 s density and temperature time series should be less surprising. When the amplitude of the Alfvnic fluctuations decreases with distance, we observe less intermittency, as expected. When we completely remove the Alfvnic fluctuations from our simulation, we obtain time series of the number density and temperature with the same statistical character as the input data.

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