References of "Ivanova, K"
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See detailTime correlations and 1/f behavior in backscattering radar reflectivity measurements from cirrus cloud ice fluctuations
Ivanova, K.; Ackerman, T. P.; Clothiaux, E. E. et al

in Journal of Geophysical Research. Atmospheres (2003), 108(D9),

[1] The state of the atmosphere is governed by the classical laws of fluid motion and exhibits correlations in various spatial and temporal scales. These correlations are crucial to understand the short ... [more ▼]

[1] The state of the atmosphere is governed by the classical laws of fluid motion and exhibits correlations in various spatial and temporal scales. These correlations are crucial to understand the short- and long-term trends in climate. Cirrus clouds are important ingredients of the atmospheric boundary layer. To improve future parameterization of cirrus clouds in climate models, it is important to understand the cloud properties and how they change within the cloud. We study correlations in the fluctuations of radar signals obtained at isodepths of winter and fall cirrus clouds. In particular, we focus on three quantities: (1) the backscattering cross-section, (2) the Doppler velocity, and (3) the Doppler spectral width. They correspond to the physical coefficients used in Navier Stokes equations to describe flows, i.e., bulk modulus, viscosity, and thermal conductivity. In all cases we find that power law time correlations exist with a crossover between regimes at about 3 to 5 min. We also find that different type of correlations, including 1/f behavior, characterize the top and the bottom layers and the bulk of the clouds. The underlying mechanisms for such correlations are suggested to originate in ice nucleation and crystal growth processes. [less ▲]

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See detailStatistical derivation of the evolution equation of liquid water path fluctuations in clouds
Ivanova, K.; Ausloos, Marcel ULg

in Journal of Geophysical Research. Atmospheres (2002), 107(D23),

[1] How to distinguish and quantify deterministic and random influences on the statistics of turbulence data in meteorology cases is discussed from first principles. Liquid water path (LWP) changes in ... [more ▼]

[1] How to distinguish and quantify deterministic and random influences on the statistics of turbulence data in meteorology cases is discussed from first principles. Liquid water path (LWP) changes in clouds, as retrieved from radio signals, upon different delay times, can be regarded as a stochastic Markov process. A detrended fluctuation analysis method indicates the existence of long range time correlations. The Fokker-Planck equation which models very precisely the LWP fluctuation empirical probability distributions, in particular, their non-Gaussian heavy tails is explicitly derived and written in terms of a drift and a diffusion coefficient. Furthermore, Kramers-Moyal coefficients, as estimated from the empirical data, are found to be in good agreement with their first principle derivation. Finally, the equivalent Langevin equation is written for the LWP increments themselves. Thus rather than the existence of hierarchical structures, like an energy cascade process, strong correlations on different timescales, from small to large ones, are considered to be proven as intrinsic ingredients of such cloud evolutions. [less ▲]

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See detailApplications of statistical physics to economic and financial topics
Ausloos, Marcel ULg; Vandewalle, Nicolas ULg; Boveroux, P. et al

in Physica A: Statistical Mechanics and its Applications (1999), 274(1-2), 229-240

Problems in economy and finance have started to attract the interest of statistical physicists. Fundamental problems pertain to the existence or not of long-, medium-, short-range power-law correlations ... [more ▼]

Problems in economy and finance have started to attract the interest of statistical physicists. Fundamental problems pertain to the existence or not of long-, medium-, short-range power-law correlations in economic systems as well as to the presence of financial cycles. Methods like the extended detrented fluctuation analysis, and the multi-affine analysis are recalled emphasizing their value in sorting out correlation ranges and predictability. Among spectacular results, the possibility of crash predictions is indicated. The well known financial analyst technique, the so-called moving average, is shown to raise questions about fractional Brownian motion properties. Finally, the (m,k)-Zipf method and the i-variability diagram technique are presented for sorting out short range correlations. Analogies with other fields of modem applied statistical physics are also presented in view of some universal openess. [less ▲]

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