H. Branger et al., A KU-BAND LABORATORY EXPERIMENT ON THE ELECTROMAGNETIC BIAS, IEEE transactions on geoscience and remote sensing, 31(6), 1993, pp. 1165-1179
Sea-surface electromagnetic bias (EM bias), the difference between the
mean reflecting surface and the geometric mean sea level, must be acc
urately determined to realize the full potential of satellite altimete
rs. A uniformly valid algorithm relating the normalized (or nondimensi
onal) EM bias, i.e., ''bias/significant wave height,'' to physical var
iables has not yet been established, so we conducted laboratory experi
ments to guide model development. Simultaneous and collocated measurem
ents of surface topography and altimeter back-scattered power were mad
e in the large IMST wind-wave facility for a wide range of wind and me
chanically generated wave conditions. A small microwave footprint on t
he water surface was produced by a focused-beam 13.5 GHz radar system
that has a high signal-to-noise ratio. Consequently specular facets ar
e easily identifiable and the data show that troughs are on average be
tter reflectors than crests. Dimensional relations seldom yield robust
algorithms and in fact, although rather high correlation is found bet
ween normalized EM bias and either wind speed or wave height, the labo
ratory coefficients are considerably greater than those of in situ alg
orithms. Nondimensional parameterization is more useful for deriving s
caling laws, and when the normalized EM bias is displayed as a functio
n of wave height skewness or wave age, laboratory and field data conve
rge into consistent trends. In particular, normalized bias decreases w
ith wave age, but unfortunately, even the wave age model does not acco
unt for the effects of mechanically generated waves, which produce app
reciable scatter relative to the pure wind cases. Thus, we propose a t
wo-parameter model using 1) a nondimensional wave height, which is com
puted for local winds, and 2) a significant slope, which is computed f
or nonlocally generated waves. Analysis of the laboratory data shows t
hat the normalized EM bias for mixed conditions is well modeled as a p
roduct of these two parameters.