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A numerical simulation of BOMEX data using a turbulence closure model coupled with ensemble cloud relations
Author(s) -
Yamada Tetsuji,
Mellor George L.
Publication year - 1979
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.49710544614
Subject(s) - turbulence , advection , turbulence modeling , mechanics , meteorology , environmental science , closure (psychology) , turbulence kinetic energy , large eddy simulation , momentum (technical analysis) , wind speed , mixing (physics) , water vapor , atmospheric sciences , physics , thermodynamics , finance , quantum mechanics , economics , market economy
A one‐dimensional version of a simplified second‐moment turbulence closure model, coupled with a recently developed cloud model, is used to simulate BOMEX (Barbados Oceanographic and Meteorological Experiment) data. Partial differential equations for the turbulence energy and a master length scale are solved. Simulated mixing ratios of water vapour, virtual temperatures and horizontal wind speeds are compared with observations. Horizontal wind speeds agree quite well; however, simulated temperature and mixing ratio of water vapour at the end of the fourth day are about 2K and 1.5 g kg −1 higher, respectively, than corresponding observations; possibly this is due to the fact that the surface temperature used in the simulation is too high. Mean liquid water, cloud volume, liquid water variance, turbulence energy and eddy viscosity coefficients are presented, but data for these variables are not available for comparison. Surface momentum, heat and moisture fluxes are also presented and are compared with data. Sensitivity studies indicate that the simulated mixing ratios of water vapour agree best with observations when both vertical wind and horizontal advection obtained from the data are included. The present study is encouraging, although further research is required to improve the model and to develop confidence in its predictive capability.