Articles | Volume 9, issue 9
https://doi.org/10.5194/gmd-9-2925-2016
https://doi.org/10.5194/gmd-9-2925-2016
Model evaluation paper
 | 
31 Aug 2016
Model evaluation paper |  | 31 Aug 2016

Large-eddy simulation and stochastic modeling of Lagrangian particles for footprint determination in the stable boundary layer

Andrey Glazunov, Üllar Rannik, Victor Stepanenko, Vasily Lykosov, Mikko Auvinen, Timo Vesala, and Ivan Mammarella

Related authors

On dissipation timescales of the basic second-order moments: the effect on the energy and flux budget (EFB) turbulence closure for stably stratified turbulence
Evgeny Kadantsev, Evgeny Mortikov, Andrey Glazunov, Nathan Kleeorin, and Igor Rogachevskii
Nonlin. Processes Geophys., 31, 395–408, https://doi.org/10.5194/npg-31-395-2024,https://doi.org/10.5194/npg-31-395-2024, 2024
Short summary
Dissipation rate of turbulent kinetic energy in stably stratified sheared flows
Sergej Zilitinkevich, Oleg Druzhinin, Andrey Glazunov, Evgeny Kadantsev, Evgeny Mortikov, Iryna Repina, and Yulia Troitskaya
Atmos. Chem. Phys., 19, 2489–2496, https://doi.org/10.5194/acp-19-2489-2019,https://doi.org/10.5194/acp-19-2489-2019, 2019
Short summary

Related subject area

Atmospheric sciences
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025,https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025,https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025,https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025,https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025,https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary

Cited articles

Anderson, P. S.: Measurement of Prandtl number as a function of Richardson number avoiding self-correlation, Bound.-Lay. Meteorol., 131, 345–362, https://doi.org/10.1007/s10546-009-9376-4, 2009.
Banta, R. M., Pichugina, Y. L., and Brewer, W. A.: Turbulent Velocity-Variance Profiles in the Stable Boundary Layer Generated by a Nocturnal Low-Level Jet, J. Atmos. Sci., 63, 700–2719, https://doi.org/10.1175/JAS3776.1, 2006.
Barad, M.: Project Prairie Grass, a field program in diffusion, vol 2. Technical Report Geophysical Research Papers No. 59, TR-58-235(II)m Air Force Cambridge Research Center, Bedford, 209 pp., http://www.jsirwin.com/PGrassVolumeII.pdf, 1958.
Bardina, J., Ferziger, J. H., and Reynolds, W. C.: Improved subgrid scale models for large-eddy simulation, Am. Inst. Aeronaut. Astronaut., paper 80-1357, https://doi.org/10.2514/6.1980-1357, 1980.
Basu, S. and Porté-Agel, F.: Large-Eddy Simulation of Stably Stratified Atmospheric Boundary Layer Turbulence: A Scale-Dependent Dynamic Modeling Approach, J. Atmos. Sci., 63, 2074–2091, https://doi.org/10.1175/JAS3734.1, 2006.
Download
Short summary
Large-eddy simulation (LES) and Lagrangian stochastic modeling of passive particle dispersion were applied to the scalar flux footprint determination in the stable atmospheric boundary layer. The footprint functions obtained in LES were compared with the functions calculated with the use of first-order single-particle Lagrangian stochastic models (LSMs) and zeroth-order Lagrangian stochastic models - the random displacement models (RDMs).
Share