54
G. Lagerloef and J. Font
be added to the CBE without exceeding the allocation. Table 3.2 also provides the
separation of monthly average error by latitude range, according to the prevailing
sea surface temperature. As noted earlier, the sensitivity decreases as temperature
decreases, increasing the errors in higher latitudes.
The sample rate increases in higher latitudes from the polar orbiting orientation
of the satellite, which is reflected in the mean number of samples per month shown
in the table. The largest single error source in the table is the roughness effect
due to wind and waves, as described above, and is the reason why the Aquarius
instrument includes an integrated L-band (1.26 GHz) radar scatterometer to measure simultaneous oceanic backscatter in the footprint as noted above. The Aquarius
roughness error allocation is presently based on limited airborne radiometer and
radar combined measurements (Wilson et al., 2001) and is expected to improve with
additional airborne data collected in March 2009, which are still being analyzed.
More improvements will be derived once the satellite is on orbit. The remaining geophysical error sources are the estimated uncertainty residuals after the best known
correction models have been applied. The degree of understanding has been the
result of rigorous studies of the ionosphere, galactic reflections, sun and so forth
(Le Vine and Abraham, 2002, 2004; Le Vine et al., 2005).
The estimates in Table 3.2 are based on the assumption that all the errors are
uncorrelated. In nature, some of these errors are likely to exhibit long correlation
scales, especially among the various geophysical effects, and with slow variations
in the sensor calibration. The assumptions are tested with the Aquarius science simulator as noted above. Those results, also shown in Lagerloef et al. (2008, 2010),
indicate retrieval errors within the science requirement of 0.2 pss monthly average
with substantial margin.
3.6 Summary: A Look to the Future Follow-on Possibilities
The simultaneous flights of SMOS and Aquarius/SAC-D give the oceanographic
community a rare opportunity to test and evaluate two very different technical
approaches: phased-array versus real aperture radiometry. In addition, Aquarius
will be the first L-band integrated passive-active (radiometer and radar scatterometer) sensor in space, providing added value for land and ice data analysis as well.
Both missions, as pathfinders, have as part of their objectives to demonstrate the
technical feasibility and scientific value of the data as a foundation for future salinity missions. Also, as pathfinders, they do not try to do too much – the focus is
to provide rather coarse resolution averaged data, consistent with climatological
scales (∼100–200 km, monthly), for the open ocean and removed from land and ice
boundaries.
Once SSS measurements on these scales are demonstrated, calibrated and
validated, the demand will grow to obtain ∼10 km resolution, near coastal measurements and higher accuracy. The higher spatial resolution and near-coast measurement issues can only be solved by flying ∼25 m aperture antennas. Will
phased-array or real-aperture be the optimal approach? Can radiometric accuracy
G. Lagerloef and J. Font
be added to the CBE without exceeding the allocation. Table 3.2 also provides the
separation of monthly average error by latitude range, according to the prevailing
sea surface temperature. As noted earlier, the sensitivity decreases as temperature
decreases, increasing the errors in higher latitudes.
The sample rate increases in higher latitudes from the polar orbiting orientation
of the satellite, which is reflected in the mean number of samples per month shown
in the table. The largest single error source in the table is the roughness effect
due to wind and waves, as described above, and is the reason why the Aquarius
instrument includes an integrated L-band (1.26 GHz) radar scatterometer to measure simultaneous oceanic backscatter in the footprint as noted above. The Aquarius
roughness error allocation is presently based on limited airborne radiometer and
radar combined measurements (Wilson et al., 2001) and is expected to improve with
additional airborne data collected in March 2009, which are still being analyzed.
More improvements will be derived once the satellite is on orbit. The remaining geophysical error sources are the estimated uncertainty residuals after the best known
correction models have been applied. The degree of understanding has been the
result of rigorous studies of the ionosphere, galactic reflections, sun and so forth
(Le Vine and Abraham, 2002, 2004; Le Vine et al., 2005).
The estimates in Table 3.2 are based on the assumption that all the errors are
uncorrelated. In nature, some of these errors are likely to exhibit long correlation
scales, especially among the various geophysical effects, and with slow variations
in the sensor calibration. The assumptions are tested with the Aquarius science simulator as noted above. Those results, also shown in Lagerloef et al. (2008, 2010),
indicate retrieval errors within the science requirement of 0.2 pss monthly average
with substantial margin.
3.6 Summary: A Look to the Future Follow-on Possibilities
The simultaneous flights of SMOS and Aquarius/SAC-D give the oceanographic
community a rare opportunity to test and evaluate two very different technical
approaches: phased-array versus real aperture radiometry. In addition, Aquarius
will be the first L-band integrated passive-active (radiometer and radar scatterometer) sensor in space, providing added value for land and ice data analysis as well.
Both missions, as pathfinders, have as part of their objectives to demonstrate the
technical feasibility and scientific value of the data as a foundation for future salinity missions. Also, as pathfinders, they do not try to do too much – the focus is
to provide rather coarse resolution averaged data, consistent with climatological
scales (∼100–200 km, monthly), for the open ocean and removed from land and ice
boundaries.
Once SSS measurements on these scales are demonstrated, calibrated and
validated, the demand will grow to obtain ∼10 km resolution, near coastal measurements and higher accuracy. The higher spatial resolution and near-coast measurement issues can only be solved by flying ∼25 m aperture antennas. Will
phased-array or real-aperture be the optimal approach? Can radiometric accuracy