Characterization of the intraday variability regime of solar irradiation of climatically distinct locations

Lauret, Philippe; Perez, Richard; Aguiar, L. Mazorra; Tapaches, Emeric; Diagne, Hadja Maïmouna; David, Mathieu

doi:10.1016/j.solener.2015.11.032

Cited by 35 publications

(22 citation statements)

References 16 publications

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“…Such a doubling in frequency in IMFs has been previously reported in astro-physical and geophysical signals. When investigating three independent datasets of satellite observations of the (extraterrestrial) total solar irradiance, Lee et al (2015) consistently find a similar dyadic-scale progression of modes at 13.5, 27, and 54 days, statistically significant within the 95 % level, that correspond to the 27-day solar rotation period and its (sub-)harmonics. Kolotkov et al (2015) find intrinsic periodicities having an average of 25 +7 −2 and 44 +10 −5 days in five different solar proxy signals.…”

Section: The Intrinsic Timescales Of Variability In the Ssimentioning

confidence: 65%

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Answer to Tommaso Alberti

Wald¹

2017

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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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Section: The Intrinsic Timescales Of Variability In the Ssimentioning

confidence: 65%

“…3 with Fig. 1 in Lee et al, 2015). If the solar rotation signature were to be seen in the IMFs of the SSI this would require the existence of amplifying processes.…”

Section: The Intrinsic Timescales Of Variability In the Ssimentioning

confidence: 90%

Answer to Tommaso Alberti

Wald¹

2017

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“…physical and geophysical signals. When investigating three independent datasets of satellite observations of the (extraterrestrial) total solar irradiance, Lee et al (2015) consistently find a similar dyadic-scale progression of modes at 13.5, 27, and 54 days, statistically significant within the 95 % level, that correspond to the 27-day solar rotation period and its (sub-)harmonics. Kolotkov et al (2015) find intrinsic periodicities having an average of 25 +7 −2 and 44 +10 −5 days in five different solar proxy signals.…”

Section: The Intrinsic Timescales Of Variability In the Ssimentioning

confidence: 65%

On the intrinsic time-scales of temporal variability in measurements of the surface solar radiation

Bengulescu¹,

Blanc²,

Wald³

2016

Preprint

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Abstract. This study is concerned with the intrinsic temporal scales of the variability of the surface solar irradiance (SSI). The data consist of decennial time-series of daily means of the SSI spanning ten years, obtained from high quality measure- ments 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 roughly sorted by ranges of in- creasing time-scales of the data are extracted by empirical mode decomposition. Next, Hilbert spectral analysis is applied to obtain an amplitude modulation–frequency-modulation (AM–FM) representation of the data. The time-varying nature of the characteristic time-scales of variability, along with the variations of 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 realisations of various stochastic processes. The data has a significant spectral peak corresponding to the yearly variability cycle and features quasi-stochastic high-frequency "weather noise", irrespective of the geographical location or of the local climate. Moreover, the amplitude of this latter feature is shown to be modulated by variations of the yearly cycle, indicative of non-linear multiplicative cross-scale couplings. The study has possible implications on the modelling and the forecast of the surface solar radiation, by clearly discriminating the deterministic from the quasi-stochastic character of the data, at different local time-scales.

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“…[26,52]. With these two parameters we could clearly classify the days in nine different frames, see Fig.…”

Section: Ground Data Analysismentioning

confidence: 99%

Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting

Aguiar

Pereira

Lauret³

et al. 2016

Renewable Energy

122

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International audienceIsolated power systems need to generate all the electricity demand with their own renewable resources. Among the latter, solar energy may account for a large share. However, solar energy is a fluctuating source and the island power grid could present an unstable behavior with a high solar penetration. Global Horizontal Solar Irradiance (GHI) forecasting is an important issue to increase solar energy production into electric power system. This study is focused in hourly GHI forecasting from 1 to 6 h ahead. Several statistical models have been successfully tested in GHI forecasting, such us autoregressive (AR), autoregressive moving average (ARMA) and Artificial Neural Networks (ANN). In this paper, ANN models are designed to produce intra-day solar forecasts using ground and exogenous data. Ground data were obtained from two measurement stations in Gran Canaria Island. In order to improve the results obtained with ground data, satellite GHI data (from Helioclim-3) as well as solar radiation and Total Cloud Cover forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as additional inputs of the ANN model. It is shown that combining exogenous data (satellite and ECMWF forecasts) with ground data further improves the accuracy of the intra-day forecasts

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Characterization of the intraday variability regime of solar irradiation of climatically distinct locations

Cited by 35 publications

References 16 publications

Answer to Tommaso Alberti

Answer to Tommaso Alberti

On the intrinsic time-scales of temporal variability in measurements of the surface solar radiation

Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting

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