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
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(8), 5784-5798; doi:10.3390/e17085784

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
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)
View Full-Text   |   Download PDF [17474 KB, uploaded 11 August 2015]   |  

Abstract

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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top