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Meteorological parameters, such as temperature, rainfall, pressure etc., exhibit selfsimilar space-time fractal fluctuations generic to dynamical systems in nature such as fluid flows, spread of forest fires, earthquakes, etc. The power spectra of fractal fluctuations display inverse power-law form signifying long-range correlations. The author has developed a general systems theory which predicts universal inverse power-law form incorporating the golden mean for the fractal fluctuations of all size scales, i.e., small, large and extreme values. The model predicted distribution is in close agreement with observed fractal fluctuations in the historic month-wise temperature (maximum and minimum) and rainfall in the UK region. The present study suggests that fractal fluctuations result from the superimposition of an eddy continuum fluctuations. The observed extreme values result from superimposition of maxima (or minima) of dominant eddies (waves) in the eddy continuum., Comment: 11 pages, 2 figures

A cumulus cloud model which can explain the observed characteristics of warm rain formation in monsoon clouds is presented. The model is based on classical statistical physical concepts and satisfies the principle of maximum entropy production. Atmospheric flows exhibit selfsimilar fractal fluctuations that are ubiquitous to all dynamical systems in nature, such as physical, chemical, social, etc and are characterized by inverse power law form for power (eddy energy) spectrum signifying long-range space-time correlations. A general systems theory model for atmospheric flows developed by the author is based on the concept that the large eddy energy is the integrated mean of enclosed turbulent (small scale) eddies. This model gives scale-free universal governing equations for cloud growth processes. The model predicted cloud parameters are in agreement with reported observations, in particular, the cloud dropsize distribution. Rain formation can occur in warm clouds within 30minutes lifetime under favourable conditions of moisture supply in the environment., Comment: 39 pages, 14 figures. arXiv admin note: substantial text overlap with arXiv:1005.1336, arXiv:1105.0172, arXiv:0908.2321, arXiv:1111.3132, arXiv:1002.3230, arXiv:0704.2114

Atmospheric flows exhibit fractal fluctuations and inverse power law form for power spectra indicating an eddy continuum structure for the selfsimilar fluctuations. A general systems theory for fractal fluctuations developed by the author is based on the simple visualisation that large eddies form by space-time integration of enclosed turbulent eddies, a concept analogous to Kinetic Theory of Gases in Classical Statistical Physics. The ordered growth of atmospheric eddy continuum is in dynamical equilibrium and is associated with Maximum Entropy Production. The model predicts universal (scale-free) inverse power law form for fractal fluctuations expressed in terms of the golden mean. Atmospheric particulates are held in suspension in the fractal fluctuations of vertical wind velocity. The mass or radius (size) distribution for homogeneous suspended atmospheric particulates is expressed as a universal scale-independent function of the golden mean, the total number concentration and the mean volume radius. Model predicted spectrum is in agreement (within two standard deviations on either side of the mean) with total averaged radius size spectra for the AERONET (aerosol inversions) stations Davos and Mauna Loa for the year 2010 and Izana for the year 2009 daily averages. The general systems theory model for aerosol size distribution is scale free and is derived directly from atmospheric eddy dynamical concepts. At present empirical models such as the log normal distribution with arbitrary constants for the size distribution of atmospheric suspended particulates are used for quantitative estimation of earth-atmosphere radiation budget related to climate warming/cooling trends. The universal aerosol size spectrum will have applications in computations of radiation balance of earth-atmosphere system in climate models., Comment: 18 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:1105.0172, arXiv:1005.1336, arXiv:0908.2321, arXiv:1002.3230, arXiv:0704.2114

Atmospheric flows exhibit scale-free fractal fluctuations. A general systems theory based on classical statistical physical concepts visualizes the fractal fluctuations to result from the coexistence of eddy fluctuations in an eddy continuum, the larger scale eddies being the integrated mean of enclosed smaller scale eddies. The model predicts (i) the eddy energy (variance) spectrum and corresponding eddy amplitude probability distribution are quantified by the same universal inverse power law distribution incorporating the golden mean. (ii) The steady state ordered hierarchical growth of atmospheric eddy continuum is associated with maximum entropy production. (iii) atmospheric particulate size spectrum is derived in terms of the model predicted universal inverse power law for atmospheric eddy energy spectrum. Model predictions are in agreement with observations. Universal inverse power law for power spectra of fractal fluctuations rules out linear secular trends in meteorological parameters. Global warming related climate change, if any, will be manifested as intensification of fluctuations of all scales manifested immediately in high frequency fluctuations. The universal aerosol size spectrum presented in this paper may be computed for any location with two measured parameters, namely, the mean volume radius and the total number concentration and may be incorporated in climate models for computation of radiation budget of earth-atmosphere system., Comment: 37 pages, 10 figures

Atmospheric flows, an example of turbulent fluid flows, exhibit fractal fluctuations of all space-time scales ranging from turbulence scale of mm -sec to climate scales of thousands of kilometers - years and may be visualized as a nested continuum of weather cycles or periodicities, the smaller cycles existing as intrinsic fine structure of the larger cycles. The power spectra of fractal fluctuations exhibit inverse power law form signifying long - range correlations identified as self - organized criticality and are ubiquitous to dynamical systems in nature and is manifested as sensitive dependence on initial condition or 'deterministic chaos' in finite precision computer realizations of nonlinear mathematical models of real world dynamical systems such as atmospheric flows. Though the selfsimilar nature of atmospheric flows have been widely documented and discussed during the last three to four decades, the exact physical mechanism is not yet identified. There now exists an urgent need to develop and incorporate basic physical concepts of nonlinear dynamics and chaos into classical meteorological theory for more realistic simulation and prediction of weather and climate. A review of nonlinear dynamics and chaos in meteorology and atmospheric physics is summarized in this paper., Comment: 36 pages, 6 figures

Atmospheric flows exhibit self-similar fractal space-time fluctuations on all space-time scales in association with inverse power law distribution for power spectra of meteorological parameters such as wind, temperature, etc., and thus implies long-range correlations, identified as self-organized criticality generic to dynamical systems in nature. A general systems theory based on classical statistical physical concepts developed by the author visualizes the fractal fluctuations to result from the coexistence of eddy fluctuations in an eddy continuum, the larger scale eddies being the integrated mean of enclosed smaller scale eddies. The model satisfies the maximum entropy principle and predicts that the probability distributions of component eddy amplitudes and the corresponding variances (power spectra) are quantified by the same universal inverse power law distribution which is a function of the golden mean. Atmospheric particulates are held in suspension by the vertical velocity distribution (spectrum). The atmospheric particulate size spectrum is derived in terms of the model predicted universal inverse power law characterizing atmospheric eddy spectrum. Model predicted spectrum is in agreement with the following four experimentally determined data sets: (i) CIRPAS mission TARFOX_WALLOPS_SMPS aerosol size distributions (ii) CIRPAS mission ARM-IOP (Ponca City, OK) aerosol size distributions (iii) SAFARI 2000 CV-580 (CARG Aerosol and Cloud Data) cloud drop size distributions and (iv) TWP-ICE (Darwin, Australia) rain drop size distributions., Comment: 43 pages, 13 diagrams

Dynamical systems in nature exhibit self-similar fractal space-time fluctuations on all scales indicating long-range correlations and therefore the statistical normal distribution with implicit assumption of independence, fixed mean and standard deviation cannot be used for description and quantification of fractal data sets. The author has developed a general systems theory which predicts the following (i) The fractal fluctuations signify an underlying eddy continuum, the larger eddies being the integrated mean of enclosed smaller-scale fluctuations. (ii) The probability distribution of eddy amplitudes and the variance (square of eddy amplitude) spectrum of fractal fluctuations follow the universal Boltzmann inverse power law expressed as a function of the golden mean. The predicted distribution is very close to statistical normal distribution for moderate events within two standard deviations from the mean but exhibits a fat long tail that are associated with hazardous extreme events. Continuous periodogram power spectral analyses of available GHCN annual total rainfall time series for the period 1900 to 2008 for Indian and USA stations show that the power spectra and the corresponding probability distributions follow model predicted universal inverse power law form signifying an eddy continuum structure underlying the observed inter-annual variability of rainfall. Global warming related atmospheric energy input will result in intensification of fluctuations of all scales and can be seen immediately in high frequency (short-term) fluctuations such as devastating floods/droughts resulting from excess/deficit annual, quasi-biennial and other shorter period (years) rainfall cycles., Comment: 28 pages, 9 figures

Atmospheric flows exhibit selfsimilar fractal spacetime fluctuations manifested as the fractal geometry to global cloud cover pattern and inverse power law form for power spectra of meteorological parameters such as windspeed, temperature, rainfall etc. Inverse power law form for power spectra indicate long-range spacetime correlations or non-local connections and is a signature of selforganised criticality generic to dynamical systems in nature such as river flows, population dynamics, heart beat patterns etc. The author has developed a general systems theory which predicts the observed selforganised criticality as a signature of quantumlike chaos in dynamical systems. The model predictions are (i) The fractal fluctuations can be resolved into an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure. (ii) The probability distribution represents the power (variance) spectrum for fractal fluctuations and follows universal inverse power law form incorporating the golden mean. Such a result that the additive amplitudes of eddies when squared represent probability distribution is observed in the subatomic dynamics of quantum systems such as the electron or photon. Therefore the irregular or unpredictable fractal fluctuations exhibit quantumlike chaos. (iii) Atmospheric aerosols are held in suspension by the vertical velocity distribution (spectrum). The atmospheric aerosol size spectrum is derived in terms of the universal inverse power law characterizing atmospheric eddy energy spectrum. Model predicted spectrum is in agreement with the following two experimentally determined atmospheric aerosol data sets, (i) SAFARI 2000 CV-580 Aerosol Data, Dry Season 2000 (CARG) (ii) World Data Centre Aerosols data sets for the three stations Ny {\AA}lesund, Pallas and Hohenpeissenberg., Comment: 32 pages, 8 figures

The frequency of occurrence of prime numbers at unit number spacing intervals exhibits selfsimilar fractal fluctuations concomitant with inverse power law form for power spectrum generic to dynamical systems in nature such as fluid flows, stock market fluctuations, population dynamics, etc. The physics of long-range correlations exhibited by fractals is not yet identified. A recently developed general systems theory visualises the eddy continuum underlying fractals to result from the growth of large eddies as the integrated mean of enclosed small scale eddies, thereby generating a hierarchy of eddy circulations, or an inter-connected network with associated long-range correlations. The model predictions are as follows: (i) The probability distribution and power spectrum of fractals follow the same inverse power law which is a function of the golden mean. The predicted inverse power law distribution is very close to the statistical normal distribution for fluctuations within two standard deviations from the mean of the distribution. (ii) Fractals signify quantumlike chaos since variance spectrum represents probability density distribution, a characteristic of quantum systems such as electron or photon. (ii) Fractal fluctuations of frequency distribution of prime numbers signify spontaneous organisation of underlying continuum number field into the ordered pattern of the quasiperiodic Penrose tiling pattern. The model predictions are in agreement with the probability distributions and power spectra for different sets of frequency of occurrence of prime numbers at unit number interval for successive 1000 numbers. Prime numbers in the first 10 million numbers were used for the study., Comment: 35 pages, 11 figures

Selfsimilar space-time fractal fluctuations are generic to dynamical systems in nature such as atmospheric flows, heartbeat patterns, population dynamics, etc. The physics of the long-range correlations intrinsic to fractal fluctuations is not yet identified. It is important to quantify the physics underlying the irregular fractal fluctuations for predictability studies. A general systems theory for fractals visualizes the emergence of successively larger scale fluctuations to result from the space-time integration of enclosed smaller scale fluctuations. The model predictions are as follows. The probability distribution and the power spectrum for fractal fluctuations is the same inverse power law function incorporating the golden mean. The predicted distribution is close to the Gaussian distribution for small-scale fluctuations but exhibits fat long tail for large-scale fluctuations with higher probability of occurrence than predicted by Gaussian distribution. Since the power (variance i.e. square of eddy amplitude) spectrum also represents the probability densities as in the case of quantum systems such as the electron or photon, fractal fluctuations exhibit quantumlike chaos. The fine structure constant for spectrum of fractal fluctuations is a function of the golden mean and is analogous to atomic spectra equal to about 1/137. Global gridded time series data sets of monthly mean temperatures for the period 1880 - 2007/2008 are analysed to show that data sets and corresponding power spectra exhibit distributions close to the model predicted inverse power law distribution. The model predicted and observed universal spectrum for interannual variability rules out linear secular trends in global monthly mean temperatures. Global warming results in intensification of fluctuations of all scales and manifested immediately in high frequency fluctuations., Comment: 31 pages, 11 figures

Dynamical systems in nature exhibit selfsimilar fractal fluctuations and the corresponding power spectra follow inverse power law form signifying long-range space-time correlations identified as self-organized criticality. The physics of self-organized criticality is not yet identified. The Gaussian probability distribution used widely for analysis and description of large data sets underestimates the probabilities of occurrence of extreme events such as stock market crashes, earthquakes, heavy rainfall, etc. The assumptions underlying the normal distribution such as fixed mean and standard deviation, independence of data, are not valid for real world fractal data sets exhibiting a scale-free power law distribution with fat tails. A general systems theory for fractals visualizes the emergence of successively larger scale fluctuations to result from the space-time integration of enclosed smaller scale fluctuations. The model predicts a universal inverse power law incorporating the golden mean for fractal fluctuations and for the corresponding power spectra, i.e., the variance spectrum represents the probabilities, a signature of quantum systems. Fractal fluctuations therefore exhibit quantum-like chaos. The model predicted inverse power law is very close to the Gaussian distribution for small-scale fluctuations, but exhibits a fat long tail for large-scale fluctuations. Extensive data sets of Dow Jones index, Human DNA, Takifugu rubripes (Puffer fish) DNA are analysed to show that the space/time data sets are close to the model predicted power law distribution., Comment: 26 pages, 7 figures

The frequency distribution of DNA bases A, C, G, T exhibit fractal fluctuations ubiquitous to dynamical systems in nature. The power spectra of fractal fluctuations exhibit inverse power law form signifying long-range correlations between local (small-scale) and global (large-scale) perturbations. The author has developed a general systems theory based on classical statistical physics for fractal fluctuations which predicts that the probability distribution of eddy amplitudes and the variance (square of eddy amplitude)spectrum of fractal fluctuations follow the universal Boltzmann inverse power law expressed as a function of the golden mean. The model predicted distribution is very close to statistical normal distribution for fluctuations within two standard deviations from the mean and exhibits a fat long tail. In this paper it is shown that DNA base CG frequency distribution in Takifugu rubripes (Puffer fish) Genome Release 4 exhibit universal inverse power law form consistent with model prediction. The observed long-range correlations in the DNA bases implies that the non-coding 'junk' or 'selfish' DNA which appear to be redundant, may also contribute to the efficient functioning of the protein coding DNA, a result supported by recent studies., Comment: 32 pages, 8 figures

Power spectra of human DNA base C+G frequency distribution in all available contiguous sections exhibit the universal inverse power law form of the statistical normal distribution for the 24 chromosomes. Inverse power law form for power spectra of space-time fluctuations is generic to dynamical systems in nature and indicate long-range space-time correlations. A recently developed general systems theory predicts the observed non-local connections as intrinsic to quantumlike chaos governing space-time fluctuations of dynamical systems. The model predicts the following. (1) The quasiperiodic Penrose tiling pattern for the nested coiled structure of the DNA molecule in the chromosome resulting in maximum packing efficiency. (2) The DNA molecule functions as a unified whole fuzzy logic network with ordered two-way signal transmission between the coding and non-coding regions. Recent studies indicate influence of non-coding regions on functions of coding regions in the DNA molecule., Comment: 19 pages, 7 diagrams

Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power-law form for power spectra of temporal fluctuations of all scales ranging from turbulence (millimeters-seconds) to climate (thousands of kilometers-years). Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures of self-organized criticality. Standard models for turbulent fluid flows in meteorological theory cannot explain satisfactorily the observed multifractal (space-time) structures in atmospheric flows. Numerical models for simulation and prediction of atmospheric flows are subject to deterministic chaos and give unrealistic solutions. Deterministic chaos is a direct consequence of round-off error growth in iterative computations. Round-off error of finite precision computations doubles on an average at each step of iterative computations. Round-off error will propagate to the mainstream computation and give unrealistic solutions in numerical weather prediction and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. A recently developed non-deterministic cell dynamical system model for atmospheric flows predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics. Further, the fractal space-time structure to the stringlike atmospheric flow trajectory is resolved into a continuum of eddies. The eddy circulations obey Kepler third law of planetary motion and therefore eddy inertial masses obey Newton inverse square law of gravitation on all scales from microscopic to macroscale., Comment: 10 pages, 2 figures

The day-to day fluctuations of Dow Jones Index exhibit fractal fluctuations, namely, a zigzag pattern of successive increases followed by decreases on all space-time scales. Self-similar fractal fluctuations are generic to dynamical systems in nature and imply long-range space-time correlations. The apparently unpredictable (chaotic) fluctuations of dynamical systems exhibit underlying order with the power spectra exhibiting inverse power law form, now identified as self-organized criticality. The physics of self-organized criticality is not yet identified. A general systems theory developed by the author shows that self-similar fractal fluctuations are signatures of quantum-like chaos in dynamical systems of all size scales ranging from the subatomic dynamics of quantum systems to macro-scale fluid flows. The model predicts the universal inverse power-law form of the statistical normal distribution for the power spectra of fractal space-time fluctuations of dynamical systems. In this paper it is shown that the power spectrum of 100 years of normalized month to month fluctuations of Dow Jones index exhibits the universal inverse power law form of the statistical normal distribution consistent with model prediction. It is shown that prediction of times of occurrence of maxima and minima during the two years subsequent to the data period used for the study is possible using the dominant peak periodicities obtained from the continuous periodogram spectral analysis of historic data., Comment: 15 pages, 3 figures

Non-local connections, i. e. long-range space-time correlations intrinsic to the observed subatomic dynamics of quantum systems is also exhibited by macro-scale dynamical systems as selfsimilar fractal space-time fluctuations and is identified as self-organized criticality. The author has developed a general systems theory for the observed self-organized criticality applicable to dynamical systems of all space-time scales based on the concept that spatial integration of enclosed small-scale fluctuations results in the formation of large eddy circulation. The eddy energy spectrum therefore represents the statistical normal distribution according to the Central Limit Theorem. The additive amplitudes of eddies, when squared (variance or eddy kinetic energy), represent the statistical normal (probability) distribution, a result observed in the subatomic dynamics of quantum systems. The model predicts Kepler's laws of planetary motion for eddy circulation dynamics. Inverse square law of gravitation therefore applies to the eddy continuum ranging from subatomic to macro-scale dynamical systems, e.g. weather systems. The model is similar to a superstring model for subatomic dynamics which unifies quantum mechanical and classical concepts and manifestation of matter is visualised as vibrational modes in string-like energy flow patterns. The cumulative sum of centripetal forces in a hierarchy of vortex circulations may result in the observed inverse square law form for gravitational attraction between inertial masses of the eddies., Comment: 20 pages, 4 figures

Dynamical systems in nature such as fluid flows, heart beat patterns, rainfall variability, stock market price fluctuations, etc. exhibit selfsimilar fractal fluctuations on all scales in space and time. Power spectral analyses of fractal fluctuations exhibit inverse power law form indicating long-range space-time correlations, identified as self-organized criticality. The author has proposed a general systems theory, which predicts the observed self-organized criticality as signatures of quantumlike chaos. The model shows that (1) the fractal fluctuations result from an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure. Conventional power spectral analysis of such a logarithmic spiral trajectory will show a continuum of eddies with progressive increase in phase. (2) Power spectral analyses of fractal fluctuations of dynamical systems exhibit the universal inverse power law form of the statistical normal distribution. Such a result indicates that the additive amplitudes of eddies, when squared (namely the variance) represent the probabilities, a characteristic exhibited by the subatomic dynamics of quantum systems such as the electron or photon. Further, long-range space-time correlations or non-local connections such as that exhibited by macroscale dynamical systems characterize quantum systems also. Therefore selfsimilar fractal fluctuations generic to dynamical systems of all scales in nature is a signature of quantumlike chaos. The model concepts are applied to show that the frequency distribution of bases A, C, G, T in Human Chromosome Y DNA exhibit long-range spatial correlations., Comment: 10 pages, 2 figures

The spatial distribution of DNA base sequence A, C, G and T exhibit selfsimilar fractal fluctuations and the corresponding power spectra follow inverse power law form, which implies the following: (1) A scale invariant eddy continuum, namely, the amplitudes of component eddies are related to each other by a scale factor alone. In general, the scale factor is different for different scale ranges and indicates a multifractal structure for the spatial distribution of DNA base sequence. (2) Long-range spatial correlations of the eddy fluctuations. Multifractal structure to space-time fluctuations and the associated inverse power law form for power spectra is generic to spatially extended dynamical systems in nature and is a signature of self-organized criticality. The exact physical mechanism for the observed self-organized criticality is not yet identified. The author has developed a general systems theory where quantum mechanical laws emerge as self-consistent explanations for the observed long-range space-time correlations, i.e. the apparently chaotic fractal fluctuations are signatures of quantum-like chaos in dynamical systems. The model provides unique quantification for the observed inverse power law form for power spectra in terms of the statistical normal distribution. In this paper it is shown that the frequency distribution of the bases C+G in all available contiguous sequences for Human chromosome Y DNA exhibit model predicted quantum-like chaos., Comment: 23 pages, 4 figures

Recent studies of DNA sequence of letters A, C, G and T exhibit the inverse power law form frequency spectrum. Inverse power-law form of the power spectra of fractal space-time fluctuations is generic to the dynamical systems in nature and is identified as self-organized criticality. In this study it is shown that the power spectra of the frequency distributions of bases C+G in the Human chromosome X DNA exhibit self-organized criticality. DNA is a quasicrystal possessing maximum packing efficiency in a hierarchy of spirals or loops. Self-organized criticality implies that non-coding introns may not be redundant, but serve to organize the effective functioning of the coding exons in the DNA molecule as a complete unit., Comment: 4 pages, 2 figures

Recent studies of DNA sequence of letters A, C, G and T exhibit the inverse power law form. Inverse power-law form of the power spectra of fractal space-time fluctuations is generic to the dynamical systems in nature and is identified as self-organized criticality. In this study it is shown that the power spectra of the frequency distributions of bases A, C, G, T in the Human chromosome 1 DNA exhibit self-organized criticality. DNA is a quasicrystal possessing maximum packing efficiency in a hierarchy of spirals or loops. Self-organized criticality implies that non-coding introns may not be redundant, but serve to organize the effective functioning of the coding exons in the DNA molecule as a complete unit., Comment: 10 pages, 2 figures

Continuous periodogram power spectral analyses of fractal fluctuations of frequency distributions of bases A, C, G, T in Drosophila DNA show that the power spectra follow the universal inverse power-law form of the statistical normal distribution. Inverse power-law form for power spectra of space-time fluctuations is generic to dynamical systems in nature and is identified as self-organized criticality. The author has developed a general systems theory, which provides universal quantification for observed self-organized criticality in terms of the statistical normal distribution. The long-range correlations intrinsic to self-organized criticality in macro-scale dynamical systems are a signature of quantumlike chaos. The fractal fluctuations self-organize to form an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure. Power spectral analysis resolves such a spiral trajectory as an eddy continuum with embedded dominant wavebands. The dominant peak periodicities are functions of the golden mean. The observed fractal frequency distributions of the Drosophila DNA base sequences exhibit quasicrystalline structure with long-range spatial correlations or self-organized criticality. Modification of the DNA base sequence structure at any location may have significant noticeable effects on the function of the DNA molecule as a whole. The presence of non-coding introns may not be redundant, but serve to organize the effective functioning of the coding exons in the DNA molecule as a complete unit., Comment: 46 pages, 9 figures

Dow Jones Index time series exhibit irregular or fractal fluctuations on all time scales from days, months to years. The nonlinear fluctuations are selfsimilar as exhibited in inverse power law form for power spectra of temporal fluctuations. Inverse power law form for power spectra of fractal fluctuations is generic to all dynamical systems in nature and is identified as self-organized criticality.The author has developed a general systems theory for universal quantification of the observed self-organized criticality in dynamical systems. The model predicts that the power spectra of fractal fluctuations follow the universal and unique inverse power law form of the statistical normal distribution and is identified as a signature of quantum-like chaos. Continuous periodogram power spectral analyses of normalised daily, monthly and annual Dow Jones Index for the past 100-years show that the power spectra follow the universal inverse power law form of the statistical normal distribution in agreement with model prediction. Dow Jones Index therefore exhibits self-organized criticality which is a signature of quantum-like chaos on all time scales from days to years. All the data sets exhibit periodicities close to model predicted values. The apparently noisy, irregular fractal fluctuations of the Dow Jones Index contribute to the formation of robust selfsimilar geometrical structures which indicate the surprising resilience of market economy over a period of 100 years., Comment: 20 pages, 7 figures

The apparantly irregular (unpredictable) space-time fluctuations in atmospheric flows ranging from climate (thousands of kilometers - years) to turbulence (millimeters - seconds) exhibit the universal symmetry of self-similarity. Self-similarity or scale invariance implies long-range spatiotemporal correlations and is manifested in atmospheric flows as the fractal geometry to spatial pattern concomitant with inverse power-law form for power spectra of temporal fluctuations. Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures of self-organized criticality. Standard meteorological theory cannot explain satisfactorily the observed self-organized criticality. Numerical models for simulation and prediction of atmospheric flows are subject to deterministic chaos and give unrealistic solutions. Deterministic chaos is a direct consequence of round-off error growth in iterative computations. Round-off error of finite precision computations doubles on an average at each step of iterative computations. Round-off error will propagate to the mainstream computation and give unrealistic solutions in numerical weather prediction (NWP) and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. A recently developed non-deterministic cell dynamical system model for atmospheric flows predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics., Comment: 5 pages, Proc. Amer. Meteorol. Soc. 10th Conf. Applied Climatology, Fall 1997, Western USA

Turbulence, namely, irregular fluctuations in space and time characterize fluid flows in general and atmospheric flows in particular.The irregular,i.e., nonlinear space-time fluctuations on all scales contribute to the unpredictable nature of both short-term weather and long-term climate.It is of importance to quantify the total pattern of fluctuations for predictability studies. The power spectra of temporal fluctuations are broadband and exhibit inverse power law form with different slopes for different scale ranges. Inverse power-law form for power spectra implies scaling (self similarity) for the scale range over which the slope is constant. Atmospheric flows therefore exhibit multiple scaling or multifractal structure.Standard meteorological theory cannot explain satisfactorily the observed multifractal structure of atmospheric flows.Selfsimilar spatial pattern implies long-range spatial correlations. Atmospheric flows therefore exhibit long-range spatiotemporal correlations, namely,self-organized criticality,signifying order underlying apparent chaos. A recently developed non-deterministic cell dynamical system model for atmospheric flows predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics.The model predictions are in agreement with continuous periodogram spectral analysis of meteorological data sets., Comment: 4 pages

The spacing intervals of adjacent Riemann zeta zeros(non-trivial) exhibit fractal(irregular) fluctuations generic to dynamical systems in nature such as fluid flows, heart beat patterns, stock market price index, etc., and are associated with unpredictability or chaos. The power spectra of such fractal space-time fluctuations exhibit universal inverse power law form and signify long-range correlations, identified as self-organized criticality . A cell dynamical system model developed by the author for turbulent fluid flows provides a unique quantification for the observed power spectra in terms of the statistical normal distribution, such that the variance represents the statistical probability densities. Such a result that the additive amplitudes of eddies when squared, represent the statistical probabilities is an observed feature of the subatomic dynamics of quantum systems such as an electron or photon. Self-organized criticality is therefore a signature of quantum-like chaos in dynamical systems. The model concepts are applicable to all real world(observed) and computed(mathematical model) dynamical systems. Continuous periodogram analyses of the fractal fluctuations of Riemann zeta zero spacing intervals show that the power spectra follow the unique and universal inverse power law form of the statistical normal distribution. The Riemann zeta zeros therefore exhibit quantum-like chaos, the spacing intervals of the zeros representing the energy(variance) level spacings of quantum-like chaos inherent to dynamical systems in nature. The cell dynamical system model is a general systems theory applicable to dynamical systems of all size scales., Comment: 25 pages, 5 figures

Recent studies indicate a close association between the distribution of prime numbers and quantum mechanical laws governing the subatomic dynamics of quantum systems such as the electron or the photon. Number theoretical concepts are intrinsically related to the quantitative description of dynamical systems of all scales ranging from the microscopic subatomic dynamics to macroscale turbulent fluid flows such as the atmospheric flows. It is now recognised that Cantorian fractal spacetime characterise all dynamical systems in nature. A cell dynamical system model developed by the author shows that the continuum dynamics of turbulent fluid flows consist of a broadband continuum spectrum of eddies which follow quantumlike mechanical laws. The model concepts enable to show that the continuum real number field contains unique structures, namely prime numbers which are analogous to the dominant eddies in the eddy continuum in turbulent fluid flows. In this paper it is shown that the prime number frequency spectrum follows quantumlike mechanical laws., Comment: 29 pages,11 figures, to be submitted for journal publication. Replacement with corrections in 'references'

Fluid flows such as gases or liquids exhibit space-time fluctuations on all scales extending down to molecular scales. Such broadband continuum fluctuations characterise all dynamical systems in nature and are identified as selfsimilar fractals in the newly emerging multidisciplinary science of nonlinear dynamics and chaos. A cell dynamical system model has been developed by the author to quantify the fractal space-time fluctuations of atmospheric flows. The earth's atmosphere consists of a mixture of gases and obeys the gas laws as formulated in the kinetic theory of gases developed on probabilistic assumptions in 1859 by the physicist James Clerk Maxwell. An alternative theory using the concept of fractals and chaos is applied in this paper to derive these fundamental gas laws., Comment: 6 pages, 1 figure

The circadian rhythm in the occurrence of acute myocardial infarction (AMI) was assessed in three hundred and twenty three patients admitted with AMI during the two-year period June 1992 to May 1994. The influence of the following meteorological, solar-geophysical and cosmic parameters in the causation of an infarct was also considered : (1) surface pressure (2) maximum temperature (3) minimum temperature (4) relative humidity (5) cosmic ray index (6) geomagnetic aa index (7) solar flares and (8) sunspot number. A well pronounced diurnal variability in AMI with a peak in the morning hours (6-12 a.m.) was seen. Further analysis of the data by considering one-hour periods revealed the presence of a smaller evening (10 p.m.) increase in incidence, i.e., the existence of a bimodal circadian rhythm. The simultaneous occurrence of the well documented semi-diurnal rhythm in surface pressure and incidence of acute myocardial infarction were evident. This may be one of the factors involved in the causation of the smaller evening peak-the reasons for which were unclear till now. Month-to-month variation in surface pressure was also found to be significantly correlated with incidence of acute myocardial infarction. Recognition of a circadian rhythm in the onset of AMI suggests the need for enhanced pharmacological protection during the vulnerable periods. Significant correlations were also found between monthly incidence of AMI and month-to-month variation of cosmic ray index and solar flare counts. The pattern of incidence of AMI was seen to be modified by full moon and new moon. There was no association between maximum temperature, minimum temperature or relative humidity and incidence of AMI., Comment: 31 pages, 8 figures

Continuous periodogram power spectral analysis of daily incidence of acute myocardial infarction (AMI) reported at a leading hospital for cardiology in Pune, India for the two-year period June 1992 to May 1994 show that the power spectra follow the universal and unique inverse power law form of the statistical normal distribution. Inverse power law form for power spectra of space-time fluctuations are ubiquitous to dynamical systems in nature and have been identified as signatures of self-organized criticality. The unique quantification for self-organized criticality presented in this paper is shown to be intrinsic to quantumlike mechanics governing fractal space-time fluctuation patterns in dynamical systems. The results are consistent with El Naschie's concept of cantorian fractal spacetime characteristics for quantum systems., Comment: 19 pages, 3 figures. Submitted for consideration of its publication in CHAOS, SOLITONS AND FRACTALS

Atmospheric flows exhibit cantorian fractal space-time fluctuations signifying long-range spatiotemporal correlations. A recently developed cell dynamical system model shows that such non-local connections are intrinsic to quantum-like chaos governing flow dynamics. The dynamical evolution of fractal structures can be quantified in terms of ordered energy flow described by mathematical functions which occur in the field of number theory. The quantum-like chaos in atmospheric flows can be quantified in terms of the following mathematical functions / concepts: (1) The fractal structure of the flow pattern is resolved into an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure and is equivalent to a hierarchy of vortices. The incorporation of Fibonacci mathematical series, representative of ramified bifurcations, indicates ordered growth of fractal patterns. (2) The steady state emergence of progressively larger fractal structures incorporates unique primary perturbation domains of progressively increasing number equal to z/lnz where z, the length step growth stage is equal to the length scale ratio of large eddy to turbulent eddy. In number theory z/lnz gives the number of primes less than z. The model also predicts that z/lnz represents the normalised cumulative variance spectrum of the eddies and which follows statistical normal distribution. The important result of the study is that the prime number spectrum is the same as the eddy energy spectrum for quantum-like chaos in atmospheric flows., Comment: 17 pages, 3 figures. Submitted for consideration of its publication in Chaos, Solitons and Fractals

The neural networks of the human brain act as very efficient parallel processing computers co-ordinating memory related responses to a multitude of input signals from sensory organs. Information storage, update and appropriate retrieval are controlled at the molecular level by the neuronal cytoskeleton which serves as the internal communication network within neurons. Information flow in the highly ordered parallel networks of the filamentous protein polymers which make up the cytoskeleton may be compared to atmospheric flows which exhibit long-range spatiotemporal correlations, i.e. long-term memory. Such long-range spatiotemporal correlations are ubiquitous to real world dynamical systems and is recently identified as signature of self-organized criticality or chaos. The signatures of self-organized criticality i.e. long-range temporal correlations have recently been identified in the electrical activity of the brain. A recently developed non-deterministic cell dynamical system model for atmospheric flows predicts the observed long-range spatiotemporal correlations as intrinsic to quantum-like mechanics governing flow dynamics. The model visualises large scale circulations to form as the result of spatial integration of enclosed small scale perturbations with intrinsic two-way ordered energy flow between the scales. Such a concept maybe applied for the collection and integration of a multitude of signals at the cytoskeletal level and manifested in activation of neurons in the macroscale. The cytoskeleton networks inside neurons may be the elementary units of a unified dynamic memory circulation network with intrinsic global response to local stimuli., Comment: 16 Pages. Submitted for consideration of publication in "Chaos, Solitons and Fractals"(1998)

A cell dynamical system model is developed for thundercloud electrification by consideration of microscopic domain eddy dynamical processes in the atmospheric boundary layer (ABL). This non-deterministic model based on cellular automata computational technique enables formulation of simple scale invariant governing equations for cloud electrical parameters in terms of non-dimensional steady state mass and momentum fluxes in the ABL., Comment: 17 pages, 1 figure

Cantorian fractal spacetime fluctuations characterize quantumlike chaos in atmospheric flows. The macroscale atmospheric flow structure behaves as a unified whole quantum system, where, the superimposition of a continuum of eddies results in the observed global weather patterns with long-range spatiotemporal correlations such as that of the widely investigated El Nino phenomenon. Large eddies are visualised as envelopes enclosing smaller eddies, thereby generating a hierarchy of eddy circulations, originating initially from a fixed primary small scale energising perturbation, e.g. the frictional upward momentum flux at the boundary layer of the earth's surface. In this paper it is shown that the relative motion concepts of Einstein's special and general theories of relativity are applicable to eddy circulations originating from a constant primary perturbation., Comment: 14 pages, 1 figure. Submitted for consideration of publication in Chaos, Solitons and Fractals

Observational studies indicate that there is a close association between geomagnetic storm and meteorological parameters. Geomagnetic field lines follow closely the isobars of surface pressure . A Physical mechanism linking upper atmospheric geomagnetic storm disturbances with tropospheric weather has been proposed by the author and her group where it is postulated that vertical mixing by turbulent eddy fluctuations results in the net transport upward of positive charges originating from lower levels accompanied simultaneously by downward flow of negative charges from higher levels. The present study reports enhancement of high frequency (<15 days period) fluctuations in daily surface pressure during March 1989 in association with major geomagnetic storm (Ap index = 246) on 13 march 1989., Comment: 1 Page, 1 Figure

Atmospheric flows exhibit fluctuations of all scales (space -time) ranging from turbulence (millimeters-seconds) to climate (thousands of kilometers-years). The apparently random fluctuations however exhibit long-range spatio-temporal correlations manifested as the selfsimilar fractal geometry to the global cloud cover pattern concomitant with inverse power law form for power spectra of temporal fluctuations . Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are recently identified as signatures of self-organized criticality . Traditional meteorological theory cannot explain satisfactorily the observed selforganized criticality in atmospheric flows . This paper gives a summary of an alternative non-deterministic cell dynamical systems model for atmospheric flows which predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics., Comment: 3 pages, 1 figure

The variability of temporal (or spatial) fluctuations of any variable is represented in conventional statistical theory by the relative dispersion equal to the standard deviation divided by the mean . The Relative Dispersion decreases with increase in time(or space) resolution and for uncorrelated fluctuations dealt with in traditional statistics, is given as a linear function of Relative Dispersion at the smallest resolution and the ratio of the time resolutions.However, it is now established that temporal (or spatial) fluctuations of dynamical systems exhibit selfsimilarity or long-range correlations and traditional statistical concepts are not valid. In this paper, it is shown that resolution dependent variance is described by the fractal dimension ., Comment: 3 pages, 1 Figure

Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power law form for spectra of temporal fluctuations. Such non-local connections are ubiquitous to dynamical systems in nature and are identified as signatures of self-organized criticality A recently developed cell dynamical system model for atmospheric flows predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics. The model predicts the following: (a) The flow structure consists of an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure. (b) The universal algorithm for self-organized criticality is expressed in terms of the universal Feigenbaum's constants. (c) The Feigenbaum's constants are expressed as functions of the golden mean. (d) The quantum mechanical constants ' fine structure constant' and 'ratio of proton mass to electron mass' which are pure numbers and obtained by experimental observations only, are now derived in terms of the Feigenbaum's constant a. (e) Atmospheric flow structure follows Kepler's third law of planetary motion. Therefore Newton's inverse square law for gravitation applies to eddy masses also. The centripetal acceleration representing inertial masses (of eddies) are equivalent to gravitational masses. Fractal structure to the space-time continuum can be visualized as a nested continuum of vortex (eddy) circulations whose inertial masses obey Newton's inverse square law of gravitation. The model concepts are equivalent to a superstring model for subatomic dynamics which incorporates gravitational forces., Comment: 20 pages, 3 figures. Submitted for publication in 'Chaos,Solitons and Fractals' removed a hyperlink to my website

Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power law form for power spectra of temporal fluctuations on all space-time scales ranging from turbulence(centimeters-seconds) to climate(kilometers-years). Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures of self-organized criticality. Standard models in meteorological theory cannot explain satisfactorily the observed self-organized criticality in atmospheric flows. Mathematical models for simulation and prediction of atmospheric flows are nonlinear and do not possess analytical solutions. Finite precision computer realizations of nonlinear models give unrealistic solutions because of deterministic chaos, a direct consequence of round-off error growth in iterative numerical computations. Recent studies show that round-off error doubles on an average for each iteration of iterative computations. Round-off error propagates to the main stream computation and gives unrealistic solutions in numerical weather prediction (NWP) and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. An alternative non-deterministic cell dynamical system model for atmospheric flows described in this paper predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics., Comment: 26 pages, 11 figures. Meteorology and Atmospheric Physics, 1998 (in Press) (Springer-Verlag, Austria)