Abstract
Abstract. Modelling the fluctuations of the Earth's surface wind has a significant role in understanding the dynamics of atmosphere besides its impact on various fields ranging from agriculture to structural engineering. Most of the studies on the modelling and prediction of wind speed and power reported in the literature are based on statistical methods or the probabilistic distribution of the wind speed data. In this paper we investigate the suitability of a deterministic model to represent the wind speed fluctuations by employing tools of nonlinear dynamics. We have carried out a detailed nonlinear time series analysis of the daily mean wind speed data measured at Thiruvananthapuram (8.483° N,76.950° E) from 2000 to 2010. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic suggesting the possibility of accurate short-term prediction. As most of the chaotic systems are confined to laboratories, this is another example of a naturally occurring time series showing chaotic behaviour.
Highlights
Surface wind plays a crucial role in climate and weather system of the Earth
A good number of such tools rely on statistical methods, either moving average models such as ARMA and ARIMA fitted to the time series of wind speed (Kamal and Jafri, 1997; Torresa et al, 2005; Cadenas and Rivera, 2007; Kavasseri and Seetharaman, 2009) or models based on probability distribution of wind speed (Hennessey, 1977; Celik, 2004; Mathew et al, 2011)
We demonstrate, using the daily mean wind speed (DMWS)-data, that the dynamics of wind speed is essentially deterministic with a low-dimensional chaotic character
Summary
Surface wind plays a crucial role in climate and weather system of the Earth. It has significant impact on agriculture, navigation, structural engineering calculations and reduction of atmospheric pollution as well as the economy of the region as an alternate energy source (Martın et al, 1999; Elliott, 2004; Bantaa et al, 2011; Finzi et al, 1984). Palmer et al (1995) have analysed several time series of wind components and X-band Doppler radar signals gathered over an area of ocean surface and have found the presence of a low-dimensional dynamical attractor in the case of time series of the horizontal wind speed as well as the vertically polarized radar reflectivity They were able to achieve better short-term predictions from the deterministic models than from statistical models. The denoised data still contain irregular persistent fluctuations, which upon analysis using tools of non-linear dynamics reveals many attributes of a chaotic system with a low-dimensional attractor Since some of these attributes may be found in linear stochastic processes, we further subject the denoised data to a detailed surrogate analysis to confirm that the underlying dynamics is deterministic and could not be described by a linear Gaussian stochastic model. Most of these analyses were carried out using tools implemented in the TISEAN package (Hegger et al, 1999)
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