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

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## Abstract

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## 1. Introduction

## 2. Methods

#### 2.1. Data and the Summarizing Minimum Ellipse

**Figure 1.**A summarizing ellipse constructed from daily wind speed and direction marked with the (1) 2-dimensional center of mass location, (2) lengths of the two axes and (3) tilt to the horizontal axis.

#### 2.1.1. Computations and Algorithms

## 3. Results and Discussion

#### 3.1. Multiscale Community Structures of Daily Wind

#### 3.2. Relationships between Daily Wind DCG Trees and Meteorological Covariates

**Figure 5.**Twenty four covariate-factor DCG tree based on 339-dimdaily measurements with distance modified by the daily-wind DCG tree.

**Figure 6.**Heat maps of the daily covariate distance matrix. (

**a**) The distance matrix ordered by date and (

**b**) the distance matrix ordered by DCG tree.

**Figure 7.**Year-round series of three covariate variables: (

**a**) air pressure, (

**b**) air temperature and (

**c**) sea temperature color marked on the five-cluster level (left panel) and 16-cluster level (right panel) of the DCG tree.

**Figure 8.**Heat map of covariate matrix ordered by the 24-covariate DCG tree coupled with the daily wind DCG tree marked on the 10-cluster level.

#### 3.3. Comparisons with Decision Tree

**Figure 9.**The decision tree classified the 10 daily wind clusters with the 24 original covariate variables.

**Figure 10.**The 339-by-339 heat map of daily ellipse distances with rows arranged according to the daily wind DCG tree and columns arranged according to the (

**a**) covariate DCG tree and (

**b**) the leaves of the fitted decision tree.

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Appendix

Statistics | ||||

Observations | mean | range | total variation | linear trend |

$PRES$ | a.p.avg | a.p.range | a.p.tv | a.p.trn |

$ATMP$ | a.t.avg | a.t.range | a.t.tv | a.t.trn |

$WTMP$ | s.t.avg | s.t.range | s.t.tv | s.t.trn |

$ATMP-WTMP$ | as.diff.avg | as.diff.range | as.diff.tv | as.diff.trn |

$|ATMP-WTMP|$ | abs.as.diff.avg | abs.as.diff.range | abs.as.diff.tv | abs.as.diff.trn |

$PRES\times ATMP$ | atxap.avg | atxap.range | atxap.tv | atxap.trn |

## Conflicts of Interest

## References

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**MDPI and ACS Style**

Wu, H.-T.; Fushing, H.; Chuang, L.Z.H.
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.
https://doi.org/10.3390/e17085784

**AMA Style**

Wu H-T, Fushing H, Chuang LZH.
Computing and Learning Year-Round Daily Patterns of Hourly Wind Speed and Direction and Their Global Associations with Meteorological Factors. *Entropy*. 2015; 17(8):5784-5798.
https://doi.org/10.3390/e17085784

**Chicago/Turabian Style**

Wu, Hsing-Ti, Hsieh Fushing, and Laurence Z.H. Chuang.
2015. "Computing and Learning Year-Round Daily Patterns of Hourly Wind Speed and Direction and Their Global Associations with Meteorological Factors" *Entropy* 17, no. 8: 5784-5798.
https://doi.org/10.3390/e17085784