Spatial and Temporal Variability of Turbulence Dissipation Rate in Complex Terrain

Julie Lundquist, Nicola Bodini, Raghavendra Krishnamurthy, Mikhail Pekour, Larry Berg, Aditya Choukulkar

Research output: Contribution to journalArticlepeer-review

25 Scopus Citations

Abstract

To improve parameterizations of the turbulence dissipation rate (ϵ) in numerical weather prediction models, the temporal and spatial variability of ϵ must be assessed. In this study, we explore influences on the variability of ϵ at various scales in the Columbia River Gorge during the WFIP2 field experiment between 2015 and 2017. We calculate ϵ from five sonic anemometers all deployed in a ∼ 4 km2 area as well as from two scanning Doppler lidars and four profiling Doppler lidars, whose locations span a ∼ 300 km wide region.We retrieve ϵ from the sonic anemometers using the second-order structure function method, from the scanning lidars with the azimuth structure function approach, and from the profiling lidars with a novel technique using the variance of the line-of-sight velocity. The turbulence dissipation rate shows large spatial variability, even at the microscale, especially during nighttime stable conditions. Orographic features have a strong impact on the variability of ϵ, with the correlation between ϵ at different stations being highly influenced by terrain. ϵ shows larger values in sites located downwind of complex orographic structures or in wind farm wakes. A clear diurnal cycle in ϵ is found, with daytime convective conditions determining values over an order of magnitude higher than nighttime stable conditions. ϵ also shows a distinct seasonal cycle, with differences greater than an order of magnitude between average ϵ values in summer and winter.

Original languageAmerican English
Pages (from-to)4367-4382
Number of pages16
JournalAtmospheric Chemistry and Physics
Volume19
Issue number7
DOIs
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Author(s).

NREL Publication Number

  • NREL/JA-5000-73831

Keywords

  • numerical weather prediction models
  • spatial variability
  • temporal variability
  • turbulence

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