statistical characteristics of surrogate data based on geophysical measurements
DESCRIPTION
Statistical characteristics of surrogate data based on geophysical measurements. Victor Venema 1 , Henning W. Rust 2 , Susanne Bachner 1 , and Clemens Simmer 1 1 Meteorological Institute University of Bonn 2 PIK, Potsdam Institute for Climate Impact Research. Content. - PowerPoint PPT PresentationTRANSCRIPT
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Statistical characteristics of surrogate data based on geophysical
measurements
Victor Venema 1, Henning W. Rust 2, Susanne Bachner 1, and
Clemens Simmer 1
1 Meteorological Institute University of Bonn2 PIK, Potsdam Institute for Climate Impact Research
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Content Surrogate data: time series generated based on
statistical properties of measurements– Distribution and/or power spectrum
7 Geophysical time series Generated surrogates with 7 different algorithms
from their statistics Compared the measurements to their surrogates
– Increment distribution– Structure functions
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Motivation Need time series with a known structure
– Statistical reconstruction– Bootstrap confidence intervals– Studying non-local process– …
FARIMA & Fourier methods vs. Multifractals Multifractals vs. surrogates
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4Satellite pictures: Eumetsat
Motivation - generator Empirical studies
– Exact measured distribution– Measured power spectrum
Scale breaks Waves Deviations large scales …
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7 Generators for surrogate data D: distribution
– PDF surrogates
S: spectrum– Fourier surrogates– FARIMA surrogates
– Seasonal cycle and logarithm if needed
DS: distribution + spectrum– AAFT, IAAFT, SIAAFT surrogates– FARIMA + IAAFT surrogates
– seasonal cycle and log. if needed
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Mea
sure
men
ts400 600 800 1000 1200 1400 1600 1800
0
5
10
Time (pixel)
Val
ue (1
)
p-model
1894 1894.5 1895 1895.5 1896 1896.5 1897 1897.50
20
Time (year)R
ain
(mm
/d)
daily rain sums
1828 1828.5 1829 1829.5 1830 1830.5 1831 1831.5500
100015002000
Time (year)
Run
off (
m3 /s
)runoff Burghausen
1818 1818.5 1819 1819.5 1820 1820.5 1821 1821.52000400060008000
Time (year)
Run
off (
m3 /s
)
runoff Cologne
0 200 400 600 800 1000 1200 1400 16000
0.20.4
Time (s)
LWC
(g m
-3)
cumulus
0 100 200 300 400 500 600 700 800 900 1000
0.4
0.6
Time (s)
LWC
(g m
-3)
stratocumulus
1894 1894.5 1895 1895.5 1896 1896.5 1897 1897.5-10
01020
Time (year)
Tem
p. (°
C)
temperature
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Surr
ogat
e ty
pes
00.20.4 Cumulus measurement
00.20.4 SIAAFT surrogate
00.20.4 IAAFT surrogate
00.20.4 AAFT surrogate
00.20.4 Fourier surrogate
LWC
(g m
-3)
00.20.4 PDF surrogate
00.20.4 FARIMA surrogate
0 200 400 600 800 1000 1200 1400 1600
00.20.4 FARIMA + IAAFT surrogate
Time (s)
DS
DS
DS
S
D
S
DS
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Increment distribution Measurement: (t) Increment time series for lag l:
(x,l) = (t+l) - (t)
Distribution jumps sizes Next plots: l is 1 day
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Increment distribution temperature
-15 -10 -5 0 5 10 15
10-4
10-3
10-2
10-1
100
Temperature increments (°C)
Rel
. fre
quen
cy
originalsiaaft (DS)iaaft (DS)aaft (DS)fourier (S)farima (S)farima+iaaft (DS)
-2 0 20.5
0.6
0.7
0.8
0.9
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Increment distribution Rhine
-4000 -2000 0 2000 4000
10-4
10-2
100
Runoff increments (m3 s-1)
Rel
. fre
quen
cy
originalsiaaft (DS)iaaft (DS)aaft (DS)fourier (S)farima (S)farima+iaaft (DS)
-200 0 200
0.2
0.4
0.6
0.8
1
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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 Higher q focuses on larger jumps
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Structure function Salzach
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Structure function stratocumulus
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RMSE 4th order structure functions Best surrogate in bold Multifractal means: power law fit
SIAAFT IAAFT AAFT Fourier PDF FARIMA FARIMA + IAAFT Multifractal Bias P-model 0.018 0.019 0.016 0.071 0.065 0.068 0.020 0.017 Rain Potsdam 0.0027 0.0021 0.0038 0.093 0.0023 0.099 0.0029 0.0020 Runoff Burghausen 0.011 0.0069 0.029 0.076 0.081 0.076 0.023 0.028 Runoff Cologne 0.016 0.016 0.025 0.064 1.6 0.043 0.034 0.19 Cumulus 0.012 0.0080 0.016 0.076 0.044 0.063 0.0070 0.029 Stratocumulus 0.018 0.017 0.018 0.026 0.49 0.042 0.038 0.028 Temperature 0.0027 0.0037 0.016 0.0062 1.7 0.0060 0.0055 0.073 Variabilty P-model 0.18 0.19 0.17 0.71 0.65 0.68 0.20 0.17 Rain Potsdam 0.032 0.028 0.048 0.92 0.036 0.99 0.037 0.020 Runoff Burghausen 0.12 0.081 0.31 0.76 0.81 0.76 0.24 0.28 Runoff Cologne 0.16 0.16 0.26 0.64 16 0.45 0.35 1.9 Cumulus 0.14 0.10 0.20 0.76 0.45 1.5 0.13 0.29 Stratocumulus 0.19 0.18 0.19 0.26 4.9 0.46 0.40 0.28 Temperature 0.034 0.040 0.17 0.066 17 0.062 0.057 0.73
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Extension IAAFT algorithm 2D and 3D fields with PDF(z) PDF(t), i.e. distribution varies as function of
– Season, time of day– Break point
Multivariate statistics, cross correlations Increment distribution at small scales
– More accurate increment distribution– Asymmetric increment distribution (runoff)
Downscaling– Extrapolate spectrum – Iterate the original coarse mean values
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Conclusions DS-Surrogates of geophysical reproduce
measurements accurately– spectrum– increments– structure functions
IAAFT algorithm– Flexibly– Efficiently– Many useful extensions are possible
Surrogates for empirical work Multifractals for theoretical work (use IAAFT)
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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)