Turbulent dissipation rate in the atmospheric boundary layer: observations and WRF mesoscale modeling during the XPIA field campaign

Turbulent dissipation rate in the atmospheric boundary layer: observations and WRF mesoscale... AbstractA better understanding and prediction of turbulent dissipation rate, ε, in the atmospheric boundary layer (ABL) is important for many applications. Herein, sonic anemometer data from the XPIA field campaign (March – May 2015) are used to derive energy dissipation rate, EDR (=ε1/3), within the first 300 m above the ground employing 2nd-order structure functions. Turbulent dissipation rate is found to be strongly driven by the diurnal evolution of the ABL, presenting a distinct statistical behavior between daytime and nighttime conditions that follows log-Weibull and log-normal distributions, respectively. In addition, the vertical structure of EDR is characterized by a decrease with height above the surface, with the largest gradients occurring within the surface layer (z < 50 m). Convection-permitting mesoscale simulations were carried out with all of the 1.5-order turbulent kinetic energy (TKE) closure planetary boundary layer (PBL) schemes available in the Weather Research and Forecasting model. Overall, the three PBL schemes capture the observed diurnal evolution of EDR as well as the statistical behavior and vertical structure. However, the Mellor–Yamada type schemes underestimate the large EDR levels during the bulk of daytime conditions, with the QNSE scheme providing the best agreement with observations. During stably stratified nighttime conditions, MYJ and QNSE tend to exhibit an artificial ‘clipping’ to their background TKE levels. A reduction in the model constant in the dissipation term for the MYNN scheme did not have a noticeable impact on EDR estimates. In contrast, application of a post-processing statistical remapping technique reduced the systematic negative bias in the MYNN results by ≈ 75%. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Turbulent dissipation rate in the atmospheric boundary layer: observations and WRF mesoscale modeling during the XPIA field campaign

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
D.O.I.
10.1175/MWR-D-17-0186.1
Publisher site
See Article on Publisher Site

Abstract

AbstractA better understanding and prediction of turbulent dissipation rate, ε, in the atmospheric boundary layer (ABL) is important for many applications. Herein, sonic anemometer data from the XPIA field campaign (March – May 2015) are used to derive energy dissipation rate, EDR (=ε1/3), within the first 300 m above the ground employing 2nd-order structure functions. Turbulent dissipation rate is found to be strongly driven by the diurnal evolution of the ABL, presenting a distinct statistical behavior between daytime and nighttime conditions that follows log-Weibull and log-normal distributions, respectively. In addition, the vertical structure of EDR is characterized by a decrease with height above the surface, with the largest gradients occurring within the surface layer (z < 50 m). Convection-permitting mesoscale simulations were carried out with all of the 1.5-order turbulent kinetic energy (TKE) closure planetary boundary layer (PBL) schemes available in the Weather Research and Forecasting model. Overall, the three PBL schemes capture the observed diurnal evolution of EDR as well as the statistical behavior and vertical structure. However, the Mellor–Yamada type schemes underestimate the large EDR levels during the bulk of daytime conditions, with the QNSE scheme providing the best agreement with observations. During stably stratified nighttime conditions, MYJ and QNSE tend to exhibit an artificial ‘clipping’ to their background TKE levels. A reduction in the model constant in the dissipation term for the MYNN scheme did not have a noticeable impact on EDR estimates. In contrast, application of a post-processing statistical remapping technique reduced the systematic negative bias in the MYNN results by ≈ 75%.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Nov 27, 2017

References

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