Analysis of SIF Intrinsic Dynamics

Analysis of SIF Intrinsic DynamicsAbstract. It was recently constitute that apparitional solar incident merge (SIF) as a control of the ultraviolet wavelengths exhibit 1/f geek causality-law correlations. In this study, an plan of attack was made to explore the SIF intrinsic kinetics versus a wider range of wavelengths, from 115.5nm to 629.5nm. It seemed that the intermittence of SIF information groom was very(prenominal) high and the revealed DFA-n top executives were very close to building blocky. Moreover, the index spectral engrossment was better fitted algebraic on the wholey than counselor-at-lawially with exponent very close to unity. Eliminating the fitting of three assorted types of Planck formula from SIF information amaze, scaling exponents very close to unity were derived, indicating that the 1/f scaling dynamics cin one casern not the Plancks law but its varietys.1. IntroductionAs it is well known, electromagnetic radiation sickness is continuously em itted by every corporal body. This emitted radiation is adequately described by Plancks law near thermodynamic equilibrium at a definite temperature. There is a incontrovertible correlation mingled with the temperature of an emitting body and the Planck radiation at every wavelength. As the temperature of an emitting surface increases, the maximum wavelength of the emitted radiation increases too.Smith and Gottlieb (1974) re-examined the subject of photon solar amalgamate and its variations versus wavelength and showed that variations in the extreme ultraviolet (UV) spectrum and in the X-ray of solar menstruate may reach at high crops of magnitude ca development epoch-making changes in the Earths ionosphere, especially during major solar flares (Kondratyev et al. 1995 Kondratyev and Varotsos 1996 Alexandris et al. 1999 Melnikova 2009 Xue et al. 2011).Simon (1978) examined the solar irradiance fluxes from 120 to 400 nm and suggested that the internal scaling properties of sola r radiation during the eleven-year cycle is still unknown, as the cause of the available solar info discrepancies can not be attributed to solar activity.Solanki and Unruh (1998) proposed simple models of the total solar irradiance variations versus wavelength showing that variations on solar flux are chief(prenominal)ly caused by magnetic fields at the solar surface. Solar observations may be reproduced by a model of three parameters the quiet Sun, a facular fixings and the temperature stratification of sunspots.Tobiska et al. (2000) developed a forecasting solar irradiance model, called SOLAR2000, covering the spectral range of 11,000,000 nm. Using this tool, the authors attempted to describe solar variation versus wavelength and through time from X-ray through infrared wavelengths, in order to predict the solar radiation component of the space environment.Very recently, Varotsos et al. (2013a,b) suggested the world of strong persistent long correlations in the solar flux flu ctuations versus UV wavelengths. More precisely, by applying the detrending fluctuation compendium (DFA) to the sign SIF versus UV wavelengths entropy practise tycoon-law correlations of the type 1/f, which is omni largess in nature, was found.In the present study, we examine whether the scaling feature of 1/f noise is evident in a wider spectrum of SIF, namely for wavelengths (WL) in the midst of 115.5 and 629.5nm.2. Data and analysisAs mentioned just higher up solar incident flux information for WL ranging from 115.5nm to 629.5nm with a step of 1nm were employed. The spectrophotometric data of spectral extraterrestrial solar flux concord been taken from the book by Makarova et al. (1991) (see also Makarova et al. 1994 Melnikova and Vasilyev 2005). Fig. 1a depicts SIF localise for the wavelength range of 115.5-629.5nm. The trader feature shown in this figure is the existence of non- institutionalisearities into the solar spectral statistical distribution and the stron g upward trend. The detrending of this data set was accomplished by applying elongate as well as polynomial topper fit. To eliminate the non-stationarities of this data set and to detect the intrinsic self-similarity, we utilize the well-known DFA system (Peng et al. 1994 weber and Talkner 2001 Varotsos 2005 Skordas et al. 2010).Furthermore, we calculated the power spectrum for the sign SIF-WL data set, victimization Fast Fourier read (FFT) algorithm as well as the maximum entropy method (MEM) of the TISEAN package (http//www.mpipks-dresden.mpg.de/tisean/TISEAN_2.1/index.html).For comparison reasons, we used the Planck formula taking into account the impelling temperature obtained from NASA (http//nssdc.gsfc.nasa.gov/planetary/factsheet/sunfact.html).A brief description of DFA-tool may be disposed(p) as followsConsider the SIF data set x(i) of length N which is incorporated over WL. In more detail, to integrate the data, we calculate the uctuations of the N observations x(i) from their destine value xave, notably x(i) xave. Therefore, the incorporate data set, y(i), is consisting of the followers pointsy(1) = x(1) xave, y(2) = x(1) xave + x(2) xave, . . . , y(i) = (1)We split the integrated data set into non-overlapping boxes of equal length, . In each box, a best polynomial topical anesthetic trend (of order n) is tted in order to detrend the integrated profile (by subtracting the locally fitted trend).The root- think about-square fluctuations Fd () of this integrated and detrended profile is calculated over all scales (box sizes). The detrended fluctuation sound F is defined byk = 0, 1, 2,, , (2)where z(i) is a linear least-square fit to the data contained within a box (Kantelhardt et al. 2002).In case the signals involve scaling, a power-law behavior for the root-mean-square uctuation function Fd () is observedFd () a (3)where is the scaling exponent, a self-afnity parameter that represents the long-range power-law correlation (Ausloos and Ivanova 2001).It is worthy of air that a is a generalise Hurst exponent (H) being equal between them for the case of a fractional Gaussian noise (FGN) data set, where 0 1. For a fractional Brownian motion (FBM) data set (1 2) the relation between these exponents is H = a 1 (Ausloos and Ivanova, 2001).For uncorrelated data, the scaling exponent is = 0.5. An exponent 0.5 in a certain range of values implies the existence of long-range correlations in that time interval. If 0 1.0 (and if once more the data set is nonintermittent), thusly persistent long-range power-law correlations prevail (the case = 1 corresponds to the so-called 1/f noise) (Weber and Talkner 2001).Finally, the scaling properties of SIF-WL data set were also studied using Haar analysis (Lovejoy and Schertzer, 2012a,b). According to Haar analysis the variation of X parameter fluctuations X with scale t can be defined using the generalised qth order structure function Sq(t) = (t)q , where the symbol st ands for ensemble averaging. In a scaling regimen, Sq(t) t(q), where the exponent (q)= qH K(q) and K(q) indicates the scaling intermittency (satisfying K(1) = 0).3. Discussion and forgetsVarotsos et al. (2013a) analyse the high-resolution observations of SIF reaching the ground and the top of the atmosphere, suggested that SIF versus ultraviolet WL exhibit 1/ftype power-law correlations. This result was derived by applying the DFA method on the SIF dataset obtained from the Villard St. Pancrace station of the Lille University of Sciences and Technology and was based on the slope (i.e., 1.020.02) of the log log mend of the root mean square fluctuation function of SIF versus the WL segment size .In order to further establish the power-law scaling mentioned before, Varotsos et al. (2013b) employed the two criteria suggested by Maraun et al. (2004), notably the rejection of the exponential decay of the autocorrelation function and the constancy of local slopes in a certain range towards the low frequencies. The results showed that the power-law fit on the power spectral density (of the detrended SIF-WL versus log) was much better than exponential one, for scales higher than 1.4 nm, art object the method of local slopes exhibited constancy with constant scepter (a 0.92) at the same range, 1.4 nm. Therefore, the two criteria of Maraum et al. (2004) were satisfied ensuring the 1/ftype power-law correlations in SIF versus WL suggested by Varotsos et al. (2013a).In the present study, the scaling dynamics of a wider spectrum of SIF-WL data set was studied, for wavelengths between 115.5 and 629.5nm. Firstly, DFA-n seemed to can take care of the trends bring out a DFA-exponent close to unity (after DFA-2), as shown in Fig. 1b.In the following we plotted the power spectral density (using FFT) of the linearly detrended SIF data set. The derived power spectral density showed that the power-law fitting gives coefficient of determination, R2, higher than the ex ponential one (see Fig. 2a). This was corroborate by F-test, at 95% confidence level. From the other hand, the DFA-1 exponent was 1.24, plot of ground by applying the multiple DFA on the linearly detrended SIF data, the derived exponents ranged from 0.94 to 1.Similar results were derived when we repeated the above mentioned analysis for the polynomially (of 6th degree) detrended SIF data set. The derived power spectral density was, once more, better fitted algebraically than exponentially (a result which was confirmed by F-test, at 95% confidence level), while the linearly DFA-exponent was 1.01 (see Fig.2b). In other words, it seemed that no detrending is needed for the studied data set.Next, to resume our results we analysed the initial SIF-WL data set by using Haar analysis (Lovejoy and Schertzer, 2012a,b). According to Haar analysis, as also mentioned in the Section 2, the variation of SIF fluctuations versus wavelength can be defined using the generalized qth order structure function Sq() = RF()q , for which it holds that in a scaling regime Sq() (q), where the exponent (q)= qH K(q) and K(q) illustrates the scaling intermittency (satisfying K(1) = 0 and (1) = H). Fig. 3b shows that the intermittency of SIF data set is very high (C1=0.20), hence the RMS exponent = (2)/2=0.60 is quite divergent from the q=1 exponent (H) and the data are far from Gaussian. In the important quasi-Gaussian case, K(q) = 0 so that (q) is linear. More generally, if the field is intermittent for example if it is the result of a multifractal process then the exponent K(q) is generally non linear and bellying and characterizes the intermittency. The physical significance of H is thus that it determines the rate at which mean fluctuations grow (H 0) or decrease (H . According to Fig. 3a,b, the exponent (2) of the structure function equals to zero (at scales on a lower floor 10 nm), a fact which means that the power spectrum exponent =1+(2) equals to 1 (1/f structure). On th e other hand, at larger scales, the exponents (2) and seem to equal to 1.2 and 2.2, respectively.To clarify this aspect, we calculated the power spectrum for the initial SIF-WL data set, using the MEM. Then, we plotted in Fig. 4a the power spectral density versus frequency and we compared these results with 1/f and 1/f2.2 structure, following the exponent (2)= 1.2 shown in Fig. 3. Interestingly, the results lied in between these two lines.Furthermore, we calculated the power spectrum for the initial as well as for the polynomially (of 6th and 7th degree) detrended SIF data set, using over again the MEM. We plotted in Fig. 4b the power spectral density versus WL and the tally algebraically fitting in the range from 10 to 100 nm. In the latter fitting we found exponent very close to unity.However, Varotsos et al. (2013a) attempt to formulate the above-shown finding, i.e., that the solar spectral irradiance obeys 1/f power-law as a function of UV wavelength, using the well-known Pl ancks law which, in the saltation of small wavelengths tends to the Wien approximation , where I(,) is the amount of energy emitted at a wavelength per unit surface area per unit time per unit solid locomote per unit wavelength, T is the temperature of the black body, h is Plancks constant, c is the speed of light, and k is Boltzmanns constant. By applying the DFA method on the various values of I(,) Varotsos et al. (2013a) showed that the calculated I(,) values do not obey the 1/ftype scaling.In an effort to clarify this aspect, we firstly applied on the initial SIF data set a fitting that was based on the Planck formula Planck()= and the derived exponents were a1 = 6799.86 45.39(0.6674%) and b1= 2754.38 15.85 (0.5755%) (see Fig. 5). Next, we applied the formula p()= which releases the exponent in front of the Bose-Einstein distribution giving a possible explanation for the different distribution of photons. The derived exponents were a= 3250.17 90.65 (2.79%), b= 4456.26 12 1.3 (2.72%) and c= 8.81914 0.2589 (2.94%). Our last step was to use the Planck formula taking into account the legal temperature obtained from NASA.However, since hc = 1.986-10-25 Jm and k = 1.38-10-23 J/K, the Planck formula gives sun = 5224.3 K while the p() formula gives sun = 3229.5 K. Furthermore, the Wien displacement law max -T = b, where max = 450 nm is the nib wavelength and b = 2.898-103mK is a constant of proportionality called Wiens displacement constant, gives sun = 6440K.The analysis based on Planck and Wien laws gives sun = (5224 + 6440) / 2 K = 5832 K which is less(prenominal) than 1% from the effective temperature obtained from NASA.Finally, the application of DFA method on the detrended SIF data, eliminating the fitting of Planck, p() and Plank(, Tef) formula, gave again DFA-exponents very close to 1 (ranging from 1.01 to 1.08) as shown in Fig. 6.All the antecedent discussion indicates that the 1/f scaling dynamics observed in solar flux concerns not the Planck s law but its variations.4. ConclusionsThe main conclusions of the present survey wereDFA-n applied on the initial SIF data set revealed DFA-exponents very close to unity (after DFA-2).Power spectral density for the linearly detrended SIF data set showed that the power-law fitting gives coefficient of determination, R2, higher than the exponential one, while DFA-1 exponent was 1.24 and DFA-n exponents ranged from 0.94 to 1. Similar results were extracted when we repeated the above mentioned analysis for the polynomially (of 6th degree) detrended SIF data set and it seemed that no detrending is needed for the studied data set.To resume our results we analysed the initial SIF-WL data set by using Haar analysis. As t was derived, the intermittency of SIF data set was very high and the data were far from Gaussian. At scales below 10 nm, the power spectrum exponent was almost 1 (1/f structure), while at larger scales, the exponents (2) and seemed to equal to 1.2 and 2.2, respectively.T he results of the power spectral density for the initial SIF-WL data set (using the MEM) versus frequency seemed to lie in between 1/f and 1/f2.2 structure.The power spectral density versus WL for the initial as well as for the polynomially (of 6th and 7th degree) detrended SIF data set (using again the MEM) revealed also exponent very close to unity.Eliminating the fitting of three different types of Planck formula from SIF data set, scaling exponents very close to unity were derivedFinally, the 1/f scaling dynamics observed in solar flux concerns not the Plancks law but its variations.