Download presentation
Presentation is loading. Please wait.
1
Beyond fractals: surrogate time series and fields Victor Venema and Clemens Simmer Meteorologisches Institut, Universität Bonn, Germany Cloud measurements: Susanne Crewell, Ulrich Löhnert, Sebastian Schmidt Climate data & analysis: Susanne Bachner, Alice Kapala, Henning Rust Radiative transfer & analysis: Sebastián Gimeno García, Anke Kniffka, Steffen Meyer, Sebastian Schmidt 3D cloud modelling: Andreas Chlond, Frederick Chosson, Siegfried Raasch, Michael Schroeter
2
Clouds are not spheres, mountains are not cones, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line Benoit B. Mandelbrot in The Fractal Geometry of Nature (1983)
3
Fractals Implied: nature is fractal Fractal, self-similar –Zoom in, looks the same –Structure measure is a power law of scale –Linear on a double logarithmic plot Beginning of complex system sciences? Structure on all scales My experience: good approximation for turbulence and stratiform clouds, but often see different signals
4
The great tragedy of science — the slaying of a beautiful theory by an ugly fact Thomas Henry Huxley (1825–1895)
5
Content Motivation – What I do –Radiative transfer through clouds –Basic algorithm Case study – 3D clouds Validation - 3D clouds Structure functions of surrogates Motivation – compare multifractals Conclusions More information
6
Motivation – Cloud structure
10
Motivation Can not measure a full 3D cloud field Need 3D field for radiative transfer calculations Can measure many (statistical) cloud properties Generate cloud field based on statistics measurements Nonlinear processes –Precise distribution Non-local processes –E.g. power spectrum (autocorrelation function) In geophysics you generally do not have full fields, but can estimate these two statistics
11
Time series The iterative IAAFT algorithm Schreiber and Schmitz DistributionFlow diagramTime series
12
Case study Two flights: Stratocumulus, Cumulus Airplane measurements –Liquid water content –Drop sizes Triangle horizontal leg (horizontal structure) A few ramps, for vertical profile Three cloud generators Irradiance modelling and measurement
13
Surrogates from airplane data
14
Three reconstructions
15
Irradiances stratocumulus
16
Irradiances cumulus
17
Validation – 3D clouds 3D models clouds -> 3D surrogates Full information, perfect statistics Test if the statistics are good enough The root-mean-square (RMS) differences are less than 1 percent (not significant) Significant differences –Fourier surrogates: distribution is important –PDF surrogates: correlations are important Trivial problem, but just numerical result
18
“Validation” time series 1D climate time series and clouds 4th order structure function –Surrogates more accurate (as multifractal) Full information, perfect statistics Numerical test how good the statistics are
20
Structure functions Increment time series: (x,l)= (t+l)- (t) SF(l,q) = (1/N) Σ | | q SF(l,2) is equivalent to auto-correlation function Correlated time series SF increases with l Higher q focuses on larger jumps
21
4th order SF cumulus
22
Error 4th order structure function
23
Generators Iterative amplitude adjusted Fourier transform algorithm –Schreiber and Schmitz (1996, 2000) –Masters and Gurley (2003) –... Search algorithm –Simulated annealing (Schreiber, 1998) –Genetic algorithm (Venema, 2003) Geostatistics: stochastic simulation –Search algorithms –Gaussian distribution
24
Comparison FARIMA modelling, Fourier methods –Gaussian distribution AR modelling & Multifractals –Idealised structure Linear statistics –Kriging –Assimilation –Optimal estimation –Kernel smoothing –....
25
Surrogates vs. multifractals Measured power spectrum Perfect distribution Indirect over distribution One specific measured field Empirical studies Power law fit Indirect control distribution Direct intermittence Ensemble of fields Theoretical studies
26
Cloud structure is not fractal Scale breaks Waves Land sea mask … Satellite pictures: Eumetsat
27
Land surface is not fractal 15 Reasons the surface is not uni-fractal (Steward and McClean, 1985): –Fractal landscape have the same number of tops and pits –Glacial cirques has a narrow size range and size dependent shape
28
Conclusions IAAFT algorithm can generate structures –Accurately –Flexibly –Efficiently Many useful extensions are possible –Local values –Increment distributions –Downscaling
29
More information Homepage –Papers, Matlab-programs, examples http://www.meteo.uni-bonn.de/ venema/themes/surrogates/ Google –surrogate clouds –multifractal surrogate time series IAAFT in R: Tools homepage Henning Rust –http://www.pik-potsdam.de/~hrust/tools.html IAAFT in Fortran (multivariate): search for TISEAN (Time SEries ANalysis)
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.