F. Blanc et al., REDUCING ORBIT ERROR FOR A BETTER ESTIMATE OF OCEANIC VARIABILITY FROM SATELLITE ALTIMETRY, Journal of atmospheric and oceanic technology, 12(1), 1995, pp. 150-160
The variable ocean dynamic topography is generally estimated from the
satellite altimeter signal once the orbit error has been removed. To c
ompute the orbit error, the most conventional technique is to fit a po
lynomial function (zeroth, first, or second degree) over lengths of se
veral thousand kilometers to each altimetric profile. However, the met
hod induces significant errors. To reduce them, one needs a more detai
led representation of the orbit error spectrum and to take account of
the spatial and temporal characteristics of the signal and noise. This
can be achieved by the form of optimal analysis known as ''inverse th
eory.'' If a realistic statistical description of the altimeter signal
components (i.e., oceanic variability and orbit error) is provided, t
he inverse formalism optimally separates the components. Although the
whole set of altimeter data is reduced to the data at the intersection
s of ascending and descending ground tracks (crossover points), the me
thod remains quasi-optimal. The authors highlight the effectiveness of
the method by applying it to the altimeter data for the Brazil-Malvin
as confluence area, a few thousand kilometers wide. The authors compar
e the orbit error estimates to those of the most conventional method t
hat is a method set to a similar environment (short-arc analyses). Wit
h a homogeneous oceanic variability of 15 cm rms and a nominal orbit e
rror of 30 cm rms, the error on the estimation is reduced to 2 cm all
along the altimetric profiles. Taking into account the nonhomogeneous
characteristics of the variability signal improves the estimation. It
can be further improved simply by adding to the selected altimeter dat
aset the crossover points one orbital revolution away. For the Geosat
satellite, they are at the same latitude but 25-degrees 25' farther we
st or east. The results encourage the use of the inverse method for or
bit error reduction. The method is good at separating signals once the
a priori parameters are well defined. Unlike polynomial fits, it does
not remove other residual environmental terms.