The upcoming Jason-1 and ENVISAT altimeter data sets present new challenges and opportunities related to optimal altimeter range correction for the sea state bias (SSB). The lack of accurate and collocated information on changing ocean surface wave dynamics remains as a key roadblock to further refinement of the SSB algorithm. Strategies to acquire and utilize new information on long wave dynamics are outlined here. Improved surface information should also support ongoing studies into precise altimeter wind estimation and anomalously high radar signal levels encountered under light wind conditions.
SSB range correction
Mean sea level is the ultimate derivative from a satellite altimeter's range measurement over the ocean. To obtain this estimate, range corrections are applied for known atmospheric, orbital, oceanic, and geoid-related factors. The prime task for this research group is optimal correction for range error attributed to changes in the overall shape of the ocean waves that reflect the impinging altimeter signal. This range correction is termed the sea state bias and its description is summarized in Chelton et al. . The ocean's geometry is constantly altered due to changing winds and to swell that arrives from distant storms. These changes affect both the total power and time-dependence of the altimeter ocean reflection. The most current SSB algorithm [Gaspar and Florens, 1998] provides the best range correction based on the surface wind speed and ocean wave height (SWH) derived directly from the altimeter. This globally-derived routine was developed using a non-parametric estimation technique that removes the need to assume a functional form for the two dependent variables. The error left after correction remains at a level of about 1% of SWH. Over much of the ocean this implies a remaining 1-3 cm range error attributed to wave dynamics.
The basic task for future research, as affirmed by recent empirical and theoretical studies [e.g., Elfouhaily et al., 2000; Millet et al., 2001], is to provide additional long-wave information beyond that of simply SWH. Two empirical studies towards this end are planned. First, Météo-France has agreed to provide one year of global wave model data at fine grid and every six hours for collocation with the TOPEX and/or Jason-1 crossover data used in SSB model work. This data set will support the first large-scale test of WAM fidelity for use in SSB refinement. The initial long-wave parameter of interest will be the wave orbital velocity (i.e., heave) variance. Second, global analysis of the Jason-1 radar altimeter waveforms will be performed using an approach designed to relate the fit residual to changing wave dynamics. This latter method was impeded by TOPEX waveform imperfections but may prove worthwhile for Jason-1.
Wind speed estimation
Wind speed near the ocean's surface controls the growth or decay of ocean waves. This variation of wave roughness (mostly small wavelets) with the wind provides the means to estimate the wind speed using a satellite altimeter [see Chelton et al., 2001]. Basically, the level of reflected signal is inversely proportional to the surface roughness and hence to wind speed. This first-order physical rule is the basis for the empirical relationship of wind speed and the radar's roughness estimate, the normalized radar cross section (NRCS).
Sigma naught anomalies
Roughly 5% of TOPEX over-ocean data are contaminated by a phenomenon we have designated as the "sigma naught bloom".
This study addresses the characterization, modeling, and proper data flagging of sigma naught bloom data.
The bloom-finding procedure also collects information about how much bloom-contaminated data remain in the
Chelton D.B., J.C. Ries, B.J. Haines, L.L. Fu, P. Callahan, 2001: Satellite altimetry, in Satellite altimetry and earth sciences, ed. L.L. Fu and A. Cazanave. Academic Press, NY, 57-64.
Elfouhaily T., D.R. Thompson, B. Chapron, D. Vandemark, 2000: Improved electromagnetic bias theory. J. Gephys. Res., 105 (C1), 1299-1310.
Gaspar P., J.P. Florens, 1998: Estimation of the sea state bias in radar altimeter measurements of sea level: Results from a new non-parametric method. J. Geophys. Res. 103 (C8), 15803-15814.
Gourrion J., D. Vandemark, S. Bailey, B. Chapron, 2000: Satellite altimeter models for surface wind speed developed using ocean satellite crossovers. IFREMER Tech. Report, DRO/OS-00/01.
Millet F.W., D. Arnold, K. Melville, J. Smith, 2001: Electromagnetic bias estimation using in situ and satellite data: A Wave slope argument. (in preparation).
Ocean wave impacts on altimetry