Universal<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mn>1</mml:mn><mml:mi>/</mml:mi><mml:mi mathvariant="italic">f</mml:mi></mml:math>Noise from Dissipative Self-Organized Criticality Models

Rios, Paolo De Los; Zhang, Yicheng

doi:10.1103/physrevlett.82.472

Cited by 76 publications

(47 citation statements)

References 21 publications

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“…This means that our model can be considered as a physically reasonable generalization of the KM model to systems with a threshold -even if they are spatially extended. As a consequence, the present model combines the idea that 1/f α noise may result from the clustering of the signal pulses [24] with the view that an activation-deactivation process and dissipation are the main features relevant for the description of 1/f α noise [13,16]. The robustness of our results strongly supports these views.…”

supporting

confidence: 77%

“…Recently, several authors have sucessfully modified originally self-organized critical models to overcome these problems [12][13][14][15]. Despite the diversity of introduced modifications (continuous driving [12], dissipation [13], (quasi-) one-dimensional geometry [14,15]), there is one common denominator.…”

mentioning

confidence: 99%

“…Despite the diversity of introduced modifications (continuous driving [12], dissipation [13], (quasi-) one-dimensional geometry [14,15]), there is one common denominator. All these models have a preferred propagation direction of the avalanches.…”

mentioning

confidence: 99%

“…The systems in Refs. [13][14][15] are essentially point driven and the system in Ref. [12] is boundary driven.…”

mentioning

confidence: 99%

“…This points towards a generic behavior strongly supporting the view in Refs. [13,16] that nonlinearity (here, activation/deactivation of sites with evolving thresholds) and dissipation are among the relevant features for generating 1/f α noise. The explanation of our results is the following: Due to the absence of critical behavior, only small avalanches occur in our model for dissipative β's.…”

mentioning

confidence: 99%

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1/fαnoise from self-organized critical models with uniform driving

Davidsen

Schuster

2000

Phys. Rev. E

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† accepted by Phys. Rev. E.Using the well-known Olami-Feder-Christensen model as our paradigm, we show how to modify uniform driven self-organized critical models to generate 1/f α noise. Our model can reproduce all the main features of 1/f α noise: (1) α is close to one and does not depend on the dimension of the system. (2) The 1/f α behavior is found for very low frequencies. (3) The spatial correlations do not obey a power law. That proves that spatially extended systems based on activation-deactivation processes do not have to be point-driven to produce 1/f α noise. The essential ingredient is a local memory of the activation-deactivation process. 05.40.Ca, 05.65.+b, 05.45.Ra,

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supporting

confidence: 77%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…The systems in Refs. [13][14][15] are essentially point driven and the system in Ref. [12] is boundary driven.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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1/fαnoise from self-organized critical models with uniform driving

Davidsen

Schuster

2000

Phys. Rev. E

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Scaling properties of observed and simulated satellite visible radiances

Barker

Bélair

et al. 2017

JGR Atmospheres

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Structure functions Sq, which are related to power spectra and used to study turbulence, were computed for GOES‐13 visible radiances measured on 16 May 2015 over French Guiana and adjacent Atlantic Ocean. The nested Global Environmental Multiscale (GEM) numerical weather prediction (NWP) model was run for the same time and area. Cloud data generated by GEM over (300 km)2 domains, with one‐way nesting ending at horizontal grid‐spacing of 0.25 km, were operated on by a 3‐D solar radiative transfer model with resulting radiances degraded to GOES‐13 resolution (~1 km) and Sq computed for them, too. For GOES‐13 radiances, scaling exponents ζ(2) associated with S2, for separation distances between 5 km and 25 km, were typically >0.6 for deep convective and marine boundary layer clouds and <0.4 for shallow cumuli over land. ζ(2) for GEM agreed well with GOES‐13 for deep convective clouds. This suggests that the self‐organizing properties of deep convection in GEM exhibit realistic geometric features, a potentially important point given the link between cloud structure and precipitation, with the latter being much more difficult to measure and assess than visible radiances. Regarding radiances for GEM's marine boundary layer clouds, their Sq differed markedly from GOES‐13's; better resembling fair‐weather cumulus. Likewise, GEM's shallow cumuli over land appear to have bypassed the “scattered” fair‐weather stage and went straight into more organized convection. Thus, it appears that comparing time series of Sq for geostationary satellite data and corresponding modeled radiances has the potential to benefit assessment of cloud system‐resolving models.

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