Victor Zlotnicki

Correlated environmental corrections in TOPEX/POSEIDON, with a note on ionospheric accuracy

Estimates of the effectiveness of an altimetric correction, and interpretation of sea level variability as a response to atmospheric forcing, both depend upon assuming that residual errors in altimetric corrections are uncorrelated among themselves and with residual sea level, or knowing the correlations. Not surprisingly, many corrections are highly correlated since they involve atmospheric properties and the ocean surfaces response to them. The full corrections (including their geographically varying time mean values), show correlations between electromagnetic bias (mostly the height of wind waves) and either atmospheric pressure or water vapor of -40%, and between atmospheric pressure and water vapor of 28%. In the more commonly used collinear differences (after removal of the geographically varying time mean), atmospheric pressure and wave height show a -30% correlation, atmospheric pressure and water vapor a -10% correlation, both pressure and water vapor a 7% correlation with residual sea level, and a bit surprisingly, ionospheric electron content and wave height a 15% correlation. Only the ocean tide is totally uncorrelated with other corrections or residual sea level. The effectiveness of three ionospheric corrections (TOPEX dual-frequency, a smoothed version of the TOPEX dual-frequency, and Doppler orbitography and radiopositioning integrated by satellite (DORIS) is also evaluated in terms of their reduction in variance of residual sea level. Smooth (90-200 km along-track) versions of the dual-frequency altimeter ionosphere perform best both globally and within 20 deg in latitude from the equator. The noise variance in the 1/s TOPEX inospheric samples is approximately (11 mm) squared, about the same as noise in the DORIS-based correction; however, the latter has its error over scales of order 10(exp 3) km. Within 20 deg of the equator, the DORIS-based correction adds (14 mm) squared to the residual sea level variance.

On the accuracy of gravimetric geoids and the recovery of oceanographic signals from altimetry

Abstract Any attempt at recovering the surface expres‐sion of time‐averaged circulation from altimetric measurements of sea surface topography requires both an independent geoid and a realistic assessment of its accuracy. It is shown that publicly available marine gravity data over the North Atlantic yield expected geoid accuracies ranging from 30 to 260 cm (root mean square, lower bound), depending on location. Most of the error is due to unsampled short wavelengths in the gravity field. These expected errors underestimate the difference between sea surface (measured from SEASAT) and the geoid computed here, by a factor of about two. Most of this discrepancy can be attributed to a failure of the power spectral model used to describe the geoid at short wavelengths. The difference between altimetric and geoidal surfaces is dominated by geoid errors. It is shown that oceanographic information can only be identified after the expected signal and the expected errors are included in an optimization scheme.

Can the weak surface currents of the Cape Verde frontal zone be measured with altimetry

Three data types are compared in the low-current-velocity regime in the southeastern North Atlantic, between 12-degrees-N and 30-degrees-N, 29-degrees-W and 18-degrees-W: Geosat altimetric sea level and derived surface geostrophic velocities, shallow current meter velocities, and dynamic heights derived from hydrographic data from cruises 4, 6, and 9 of the research vessel Meteor. The four current meter daily time series, at depths around 200 m, were smoothed over 1 month; the altimetric geostrophic velocities were computed from sea surface slopes over 142 km every 17 days. The correlation coefficients between the current meter and altimetric geostrophic velocities range between 0.64 and 0.90 for the moorings near 29-degrees-N but between 0.32 and 0.71 for the two around 21-degrees-N; the associated rms discrepancies between the two measurement types range between 1.5 and 4.4 cm/s, which is 49% to 127% of the rms of the respective current meter time series. Dynamic heights relative to 1950 dbar for the months of November 1986 (d(M4)), November 1987 (d(M6)), and February 1989 (d(M9)) were computed from Meteor cruises 4, 6, and 9. Both dynamic heights and altimetric heights (h(M4), h(M6), h(M9)) were averaged over 1-degrees boxes for the duration of each cruise. Differences d(M4) - d(M6) and d(M9) - d(M6) were computed only at bins where at least one station from both cruises existed, Assuming that dynamic heights d in dynamic centimeters are equivalent to sea level h in centimeters, the standard deviation sigma of the differences ((h(M4) - h(M6)) - (d(M4) - d(M6))) and corresponding M9 - M6 values was 2.1 cm. This value (squared) is only 13% of the (5.8 cm)2 variance of the dynamic height differences and is indistinguishable from the 2.7- to 5.6-cm natural variability of sea level in the area expected between the times when the ship and the satellite sampled the ocean. The areally averaged discrepancy for M9 - M6 was only 0.7 cm, but the corresponding value for M4 - M6 was 5.2 cm. A systematic difference between the water vapor corrections used before and after July 1987 is responsible for the M4 - M6 difference. The average M4 - M6 discrepancy is only 0.1 cm using the Fleet Numerical Oceanography Center correction, with a standard deviation of 3.1 cm. In spite of the underlying differences in sampling and physics, including unknown barotropic components not included in our hydrographic dynamic heights, and in data errors, including water vapor, ionospheric, and orbital effects on the altimetry, consistent interannual changes of the mean sea level from the independently obtained altimetric and hydrographic data sets are obtained, and correlated seasonal changes in surface currents are observed with both altimetry and current meters.

Evaluating models of sea state bias in satellite altimetry

Investigations of the sea state bias (SSB) in altimeter measurements of sea surface height (SSH) have been reported by many authors based on aircraft, sea tower, and satellite-borne observations. These investigations have resulted in several proposed algorithms of the form SSB = eH, where H is the significant wave height (SWH) and e is a nondimensional function of wind speed U and SWH available from altimeter measurements. In the present work, on the basis of the full set of Geosat and an 8-month set of TOPEX altimeter measurements, all known algorithms are examined and a conclusion is reached that the altimeter-based U and SWH are insufficient to estimate the SSB correction with uniformly high accuracy. As a criterion of model performance we employ the value (called here the accuracy gain) by which the total variance of temporal changes in surface elevation is reduced owing to an SSB correction. This quantity is estimated for global data as well as for several selected regions of sufficiently large size. The linear geophysical model function (GMF) of the form e = a0 + a1U is shown to yield an improvement over the simplest GMF with a constant e. A three-parameter linear form e = a0 + a1U + a2H produces somewhat better results. A two-parameter, physically based GMF relating e to the pseudo wave age ξ (where ξ is estimated using altimeter wind and SWH) yields even higher accuracy, while the three-parameter GMF of form e = a0 + a1U + a2U2 yields the highest accuracy gain for global data sets. However, in terms of the SSB values, the difference between different GMFs is marginal, and the accuracy gain (as a measure of the SSB models performance) is shown to have serious deficiencies. We find that for all the SSB models, globally tuned empirical parameters often yield unacceptably poor results for certain regions in which local physical conditions differ from the global average: when the globally tuned GMFs are applied to such regions, the SSB-related error in SSH may well exceed 5 cm.