Estimating determinism rates to detect patterns in geospatial datasets

Rios, Ricardo Araújo; Parrott, Lael; Lange, Holger; Mello, Rodrigo Fernandes de

doi:10.1016/j.rse.2014.09.019

Cited by 13 publications

(12 citation statements)

References 23 publications

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“…The dataset used in this scenario is one of the Land Surface products provided by the Institute for Environment and Sustainability (IES) of the European Commission's Joint Research Centre (JRC). In summary, this data represents the fraction of the incoming solar radiation between 400 and 700 nm which is absorbed by plants and other photosynthetic organisms on the earth surface [19][20][21]11].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In an analysis presented in [11], it was adopted the mutual information estimator proposed by Darbellay and Vajda [12,11], which is based on partitioning the data space into a nite number of nonoverlapping rectangular cells. According to the authors, instead of partitioning data into xed-width bins, the technique must continuously partition it until achieving a conditional independence among cells.…”

Section: Q6mentioning

confidence: 99%

“…Besides this visual inspection, we decided to employ a method to quantify the mutual information (MI) in phase spectra of consecutive IMFs [11]. The concept of mutual information (MI) was initially introduced by Shannon [12] to quantify the information shared by two variables.…”

Section: Q6mentioning

confidence: 99%

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Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals

Rios

Mello

2016

Signal Processing

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Q6mentioning

confidence: 99%

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Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals

Rios

Mello

2016

Signal Processing

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“…The four time series for the period 2001-2010 have been quality checked according to Roesch et al (2011). Next, daily means of SSI were then calculated from these raw time series only if more than 80 % of the data during daylight were valid.…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

“…Regardless of the methods used, when analyzing data there is always the need to discriminate between deterministic signals and what are assumed to be background stochastic realizations (Rios et al, 2015). The classical way to solve this when employing the HHT on geophysical signals, such as the SSI, is to presume some model for the background power spectrum, against which the identified features are then compared (Huang and Wu, 2008;Franzke, 2009Franzke, , 2012.…”

Section: Introductionmentioning

confidence: 99%

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

Bengulescu

Blanc

Wald

2018

Nonlin. Processes Geophys.

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Abstract. This study is concerned with the intrinsic temporal scales of the variability in the surface solar irradiance (SSI). The data consist of decennial time series of daily means of the SSI obtained from high-quality measurements of the broadband solar radiation impinging on a horizontal plane at ground level, issued from different Baseline Surface Radiation Network (BSRN) ground stations around the world. First, embedded oscillations sorted in terms of increasing timescales of the data are extracted by empirical mode decomposition (EMD). Next, Hilbert spectral analysis is applied to obtain an amplitude-modulation-frequencymodulation (AM-FM) representation of the data. The timevarying nature of the characteristic timescales of variability, along with the variations in the signal intensity, are thus revealed. A novel, adaptive null hypothesis based on the general statistical characteristics of noise is employed in order to discriminate between the different features of the data, those that have a deterministic origin and those being realizations of various stochastic processes. The data have a significant spectral peak corresponding to the yearly variability cycle and feature quasi-stochastic high-frequency variability components, irrespective of the geographical location or of the local climate. Moreover, the amplitude of this latter feature is shown to be modulated by variations in the yearly cycle, which is indicative of nonlinear multiplicative cross-scale couplings. The study has possible implications on the modeling and the forecast of the surface solar radiation, by clearly discriminating the deterministic from the quasistochastic character of the data, at different local timescales.

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A novel multivariable hybrid model to improve short and long-term significant wave height prediction

Pang,

Dong

2023

Applied Energy

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Estimating determinism rates to detect patterns in geospatial datasets

Cited by 13 publications

References 23 publications

Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals

Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

A novel multivariable hybrid model to improve short and long-term significant wave height prediction

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