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| Monin–Obukhov | |
|---|---|
| Name | Monin–Obukhov |
| Fields | Atmospheric science |
| Known for | Monin–Obukhov similarity theory |
Monin–Obukhov.
Monin–Obukhov relates to a foundational framework developed in the mid-20th century that links surface fluxes, turbulence, and stratification in the atmospheric surface layer, connecting work influenced by figures such as Andrei Kolmogorov, Ludwig Prandtl, Osborne Reynolds, Lewis Fry Richardson and institutions including the Soviet Union research establishments, Leningrad State University, Institute of Atmospheric Physics (IAP), and later propagated through literature from Massachusetts Institute of Technology, University of Cambridge, University of Chicago, Imperial College London, and University of California, Berkeley. The framework underpins observational campaigns by organizations like World Meteorological Organization, National Oceanic and Atmospheric Administration, European Centre for Medium-Range Weather Forecasts, and research projects such as Project Stormfury and Global Atmospheric Research Programme. Prominent researchers who extended the approach include A. S. Monin, A. M. Obukhov, J. C. Kaimal, P. A. Taylor, and Jules Charney.
Monin–Obukhov Similarity Theory (MOST) formalizes self-similarity in the surface layer using scale arguments influenced by prior work from Ilya Prigogine, Andrey Kolmogorov, G. I. Taylor, Lewis F. Richardson, and practitioners at Meteorological Office (UK), National Center for Atmospheric Research, Scripps Institution of Oceanography, and Jet Propulsion Laboratory. MOST posits that turbulence statistics in the near-surface layer can be nondimensionalized by flux-based scales derived from quantities measured in field programs like Kansas experiment (1968), HAPEX-MOBILHY, LOPAP, BOMEX, and GATE. The theory uses scaling parameters associated with researchers such as Vilhelm Bjerknes, John von Neumann, André-Louis Cholesky, and methods developed in collaboration with groups at CNRS, Max Planck Institute for Meteorology, National Physical Laboratory (UK), and Argonne National Laboratory.
The Obukhov length L is a flux-based stability length scale introduced by Obukhov within the Soviet research tradition tied to laboratories like Voeikov Main Geophysical Observatory and disseminated through translation and citation chains involving Royal Meteorological Society, American Meteorological Society, WMO, and conferences at AGU Fall Meeting and EUG events. L quantifies the relative importance of buoyancy production and shear production of turbulence and features in formulations used by modeling centers such as ECMWF, NOAA GFS, UK Met Office Unified Model, COAMPS, and research codes from NCAR. The concept influenced boundary-layer parameterizations in community models like WRF and RAMS and observational analyses from campaigns coordinated by IOP teams and observatories at Cabauw Experimental Site, Boulder Atmospheric Observatory, Mace Head, and SMHI.
Practitioners apply MOST and Obukhov length in surface flux estimation, micrometeorology, dispersion modeling, wind energy siting, agriculture, and urban climatology in projects supported by European Commission, NASA, NSF, DARPA, FAO, and UNEP. Operational assimilation systems at ECMWF, NCEP, JMA, and Met Office employ stability functions derived from this framework for turbulence closure in single-column models and large-eddy simulations by groups at Princeton University, Stanford University, University of Colorado Boulder, and ETH Zurich. Field deployments referencing the approach include experiments by US Department of Energy, European Space Agency, JAXA, CSIRO, and collaborative networks like FLUXNET and ICOS.
Empirical stability correction functions (psi-functions) used with Monin–Obukhov scaling were formulated and tested by researchers in multi-institutional collaborations including A. S. Monin, A. M. Obukhov, J. C. Wyngaard, F. T. M. Nieuwstadt, J. O. Stull, and teams at NCAR, NCEP, ECMWF, UK Met Office and universities such as University of Helsinki, Stockholm University, University of Reading, University of Manchester, and University of Oslo. Common formulations appear in operational schemes of GFS, IFS, HWRF, and are implemented in surface-layer modules of community models like OpenFOAM and LES frameworks developed at Los Alamos National Laboratory and Sandia National Laboratories. Observational validation used instrument platforms produced by Met Office, Vaisala, Gill Instruments, and measurement programs associated with NOAA ESRL.
Limitations of MOST have been highlighted by investigators at Max Planck Institute for Meteorology, NCAR, Imperial College London, ETH Zurich, and MIT, particularly for heterogeneous surfaces observed in projects like Landsat-based campaigns, urban studies in Tokyo, New York City, and London, and complex terrain studies in Rocky Mountains and Himalayas. Extensions include generalized similarity approaches, local scaling by Nieuwstadt, generalized flux–gradient relationships used in Climate Models Intercomparison Project studies, spectral corrections used in LES by Mason and Moeng, and Lagrangian stochastic models promoted by Garrett Deen, Thomson and groups at University of Newcastle upon Tyne. Hybrid parameterizations integrate data-assimilation advances from ECMWF and machine-learning experiments from Google DeepMind collaborations.
The derivation employs dimensional analysis and balance equations analogous to treatments in textbooks by Tennekes and Lumley, Stull, Monin and Yaglom, and scholarly articles in journals like Journal of Atmospheric Sciences, Boundary-Layer Meteorology, Quarterly Journal of the Royal Meteorological Society, and Tellus. Core assumptions include horizontally homogeneous, stationary conditions typical of measurements at Cabauw, Høvsøre, and Lauder, scale separation paralleling theories from Kolmogorov, and dominance of surface fluxes similar to setups in Kansas experiment (1968). The mathematical form ties turbulent fluxes, friction velocity, and buoyancy flux to produce nondimensional profiles used by parameterization schemes in community codes from NCAR, ECMWF, Met Office and research groups at University of Washington, Cornell University, and Yale University.
Category:Boundary layer meteorology