Classification of synoptic and local-scale wind patterns using k-means clustering in a Tyrrhenian coastal area (Italy)

TitleClassification of synoptic and local-scale wind patterns using k-means clustering in a Tyrrhenian coastal area (Italy)
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2022
AuthorsDi Bernardino, A., Iannarelli A.M., Casadio S., Pisacane Giovanna, Mevi G., and Cacciani M.
JournalMeteorology and Atmospheric Physics
Volume134
ISSN01777971
Keywordsalgorithm, Atmospheric circulation, Italy, sea breeze, Time series, Tyrrhenian Coast, ventilation
Abstract

In coastal regions, the complex interaction of synoptic-scale dynamics and breeze regimes influence the local atmospheric circulation, permitting to distinguish typical yet alternative patterns. In this paper, the k-means clustering algorithm is applied to the hourly time series of wind intensity and direction collected by in-situ weather stations at seven locations within 30 km from the western coastline of central Italy, in the proximity of Rome, over the period 2014–2020. The selection of both wind-integral quantities and ad hoc objective parameters allows for the identification of three characteristic clusters, two of which are closely related to the synoptic circulation and governed by persistent winds, blowing from either the northeast or the southeast direction throughout the day. In the latter case, synoptic and mesoscale contributions add up, giving rise to a complex circulation at the ground level. On the contrary, the third cluster is closely related to the sea breeze regime. The results allow the identification of some general information about the low-level circulation, showing that the synoptic circulation dominates in winter and, partly, in spring and autumn, when high ventilation and low recirculation conditions occur. Conversely, during summer the sea breeze regime is more frequent and stronger, generating intense air recirculation. Our analysis permits to discern rigorously and objectively the typical coastal meteorological patterns, only requiring anemological in-situ data. © 2022, The Author(s).

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125464086&doi=10.1007%2fs00703-022-00871-z&partnerID=40&md5=d4d7f21b3fb690fbabb44faad4a02437
DOI10.1007/s00703-022-00871-z