Next Article in Journal
Entropy Bounds and Field Equations
Next Article in Special Issue
Entropic Dynamics
Previous Article in Journal
Active Control of a Chaotic Fractional Order Economic System
Previous Article in Special Issue
Consistency of Learning Bayesian Network Structures with Continuous Variables: An Information Theoretic Approach
Open AccessArticle

Computing and Learning Year-Round Daily Patterns of Hourly Wind Speed and Direction and Their Global Associations with Meteorological Factors

1
Institute of Ocean Technology and Marine Affairs, NCKU, No.1, University Road, Tainan, 70101,Taiwan
2
Department of Statistics, University of California, Davis, One Shields Avenue, 95616, CA, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cafaro
Entropy 2015, 17(8), 5784-5798; https://doi.org/10.3390/e17085784
Received: 12 March 2015 / Revised: 16 June 2015 / Accepted: 3 August 2015 / Published: 11 August 2015
(This article belongs to the Special Issue Dynamical Equations and Causal Structures from Observations)
Daily wind patterns and their relational associations with other metocean (oceanographic and meteorological) variables were algorithmically computed and extracted from a year-long wind and weather dataset, which was collected hourly from an ocean buoy located in the Penghu archipelago of Taiwan. The computational algorithm is called data cloud geometry (DCG). This DCG algorithm is a clustering-based nonparametric learning approach that was constructed and developed implicitly based on various entropy concepts. Regarding the bivariate aspect of wind speed and wind direction, the resulting multiscale clustering hierarchy revealed well-known wind characteristics of year-round pattern cycles pertaining to the particular geographic location of the buoy. A wind pattern due to a set of extreme weather days was also identified. Moreover, in terms of the relational aspect of wind and other weather variables, causal patterns were revealed through applying the DCG algorithm alternatively on the row and column axes of a data matrix by iteratively adapting distance measures to computed DCG tree structures. This adaptation technically constructed and integrated a multiscale, two-sample testing into the distance measure. These computed wind patterns and pattern-based causal relationships are useful for both general sailing and competition planning. View Full-Text
Keywords: algorithmic clustering computations; data cloud geometry; distance adaptation; metocean (oceanographic and meteorological) variables; hierarchical wind patterns; multiscale patterns; pattern-based causal relations algorithmic clustering computations; data cloud geometry; distance adaptation; metocean (oceanographic and meteorological) variables; hierarchical wind patterns; multiscale patterns; pattern-based causal relations
Show Figures

Figure 1

MDPI and ACS Style

Wu, H.-T.; Fushing, H.; Chuang, L.Z. Computing and Learning Year-Round Daily Patterns of Hourly Wind Speed and Direction and Their Global Associations with Meteorological Factors. Entropy 2015, 17, 5784-5798.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop