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A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐ t Distribution
Author(s) -
Tagle Felipe,
Castruccio Stefano,
Crippa Paola,
Genton Marc G.
Publication year - 2019
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12437
Subject(s) - wind speed , skew , wind power , environmental science , meteorology , gaussian , econometrics , climatology , computer science , geography , mathematics , geology , engineering , telecommunications , physics , quantum mechanics , electrical engineering
Facing increasing domestic energy consumption from population growth and industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden its energy mix by expanding investment in renewable energy sources, including wind energy. A preliminary task in the development of wind energy infrastructure is the assessment of wind energy potential, a key aspect of which is the characterization of its spatio‐temporal behavior. In this study we examine the impact of internal climate variability on seasonal wind power density fluctuations over Saudi Arabia using 30 simulations from the Large Ensemble Project (LENS) developed at the National Center for Atmospheric Research. Furthermore, a spatio‐temporal model for daily wind speed is proposed with neighbor‐based cross‐temporal dependence, and a multi‐variate skew‐ t distribution to capture the spatial patterns of higher‐order moments. The model can be used to generate synthetic time series over the entire spatial domain that adequately reproduce the internal variability of the LENS dataset.