https://orcid.org/0000-0002-8343-7739
Abstract
Wind direction is one of the fundamental parameters of weather. In this study we investigate the wind direction climate 10 m above surface level in the Baltic States (Estonia, Latvia, Lithuania). The analysis of wind direction over larger regions is usually hindered by the fact that wind direction is a circular variable, which means that averaged values are meaningless. Here we show how Principal Component Analysis (PCA) can be applied to give a large scale overview of typical wind direction patterns in the region. Here we apply PCA to both observational and reanalysis data. The most significant wind direction patterns are detected in both synoptic scale and mesoscale, and we attempt to link the identified patterns with meteorological phenomena. In addition, the differences in the PCA results between observation and model data are analysed.
The results show that PCA method is successful in identifying and ranking the wind direction climate features, leading to a complete and thorough investigation for the whole region that would be not possible by human researchers analysing individual distributions of wind direction.
Data availability
Observations are available from the Latvian Environment, Geology and Meteorology Centre: https://www.meteo.lv/en/meteorologija-datu-meklesana/?nid=924.
UERRA reanalysis data are available from Copernicus Data Store: https://cds.climate.copernicus.eu/cdsapp\#!/dataset/reanalysis-uerra-europe-single-levels?tab=overview.
Disclosure statement
No potential conflict of interest was reported by the authors.