Analysis of Wind Roses Using Hierarchical Cluster and Multidimensional Scaling Analysis at La Plata,Argentina |
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Authors: | Gustavo Ratto Ricardo Maronna Guillermo Berri |
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Institution: | 1.CIOp (Centro Investigaciones Opticas),La Plata,Argentina;2.Facultad de Ingeniería,Universidad Nacional de La Plata,La Plata,Argentina;3.Departamento de Matemáticas, Facultad de Ciencias Exactas,Universidad Nacional de La Plata,La Plata,Argentina;4.SMN (Servicio Meteorológico Nacional),Buenos Aires,Argentina;5.CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas),Buenos Aires,Argentina |
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Abstract: | Knowledge of frequency wind patterns is very important for air pollution modelling, especially in a city like La Plata (approximately
850,000 inhabitants) with high vehicular and industrial activities and no air monitoring network. An hourly wind analysis
was carried out on data from two local weather stations (points A and J). An initial result was that, in spite of differences
in data quality, the local weather stations observations were consistent with local and regional National Meteorological Service
(NMS) monthly based observations. Two non conventional multivariate statistical methods were employed to further analyse hourly
data at points A and J. Hierarchical cluster resulted in a good summarising tool to visualise prevailing hourly winds. Resultant
vectors emerging from the clustering process showed good similarity between sites and seasons; this allowed a further visualization
of the average diurnal wind development. Multidimensional scaling (MDS) permitted a pairwise comparison of a large number
of hourly wind roses. These wind roses were more similar to each other in colder seasons and at site A (the one that is closer
to the river) than in warmer seasons and at site J. Most of the observed variations regarding seasons and sites revealed by
cluster and MDS analysis are explained in terms of the sea-land breeze circulations. The methodology applied proved to be
of utility for simplifying the analysis of high dimensional data with numerous observations. |
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