Marija Bervida (Author), Samo Stanič (Author), Griša Močnik (Author), Longlong Wang (Author), Klemen Bergant (Author), Xiaoquan Song (Author)

Abstract

This paper complements the existing studies of Bora flow properties in the Vipava valley with the study of Bora turbulence in a lower region of the troposphere. The turbulence characteristics of Bora flow were derived from high resolution Doppler wind lidar measurements during eight Bora wind episodes that occurred in November and December 2019. Based on the vertical profiles of wind velocity, from 80 to 180 m above the valley floor, the turbulence intensity related to all three spatial directions and the along-wind integral length scales related to three velocity components were evaluated and compared to the approximations given in international standards. The resulting turbulence characteristics of Bora flow in a deep mountain valley exhibited interesting behaviour, differing from the one expected and suggested by standards. The intensity of turbulence during Bora episodes was found to be quite strong, especially regarding the expected values for that particular category of terrain. The specific relationship between along-wind, lateral and vertical intensity was evaluated as well. The scales of turbulence in the along-wind direction were found to vary widely between different Bora episodes and were rather different from the approximations given by standards, with the most significant deviations observed for the along-wind length scale of the vertical velocity component. Finally, the periodicity of flow structures above the valley was assessed, yielding a wide range of possible periods between 1 and 10 min, thus confirming some of the previous observations from the studies of Bora in the Vipava valley.

Keywords

doppler wind lidar;Bora wind;turbulence intensity;complex terrain;turbulence integral length scale;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UNG - University of Nova Gorica
UDC: 528
COBISS: 83704835 Link will open in a new window
ISSN: 2072-4292
Views: 1259
Downloads: 39
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Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

URN: URN:SI:UNG
Pages: str. 1-24
Volume: ǂVol. ǂ13
Issue: ǂiss. ǂ21
Chronology: 2021
DOI: 10.3390/rs13214363
ID: 13641625