TITLE:
Stable Boundary Layer Height Parameterization: Learning from Artificial Neural Networks
AUTHORS:
Wei Li
KEYWORDS:
Artificial Neural Network; Large-Eddy Simulation; Stable Boundary Layer Height
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.3 No.4,
September
23,
2013
ABSTRACT: Artificial neural networks (ANN) are employed using
different combinations among the surface friction velocity u*, surface buoyancy flux Bs, free-flow stability N, Coriolis parameter f,
and surface roughness length z0 from large-eddy simulation data as inputs
to investigate which variables are essential in determining the stable boundary
layer(SBL) height h. In addition, the
performances of several conventional linear SBL height parameterizations are
evaluated. ANN results indicate that the surface friction velocity u* is the most predominant
variable in the estimation of SBL height h.
When u* is absent, the
secondly important variable is the surface buoyancy flux Bs. The relevance of N, f, and z0 to h is also discussed; f affects more than N does, and z0 shows to be the most
insensitive variable to h.