EVALUATING MODELS OF SEA-STATE BIAS IN SATELLITE ALTIMETRY

Citation
Re. Glazman et al., EVALUATING MODELS OF SEA-STATE BIAS IN SATELLITE ALTIMETRY, J GEO RES-O, 99(C6), 1994, pp. 12581-12591
Citations number
33
Categorie Soggetti
Oceanografhy
Journal title
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
ISSN journal
21699275 → ACNP
Volume
99
Issue
C6
Year of publication
1994
Pages
12581 - 12591
Database
ISI
SICI code
2169-9275(1994)99:C6<12581:EMOSBI>2.0.ZU;2-B
Abstract
Investigations of the sea state bias (SSB) in altimeter measurements o f sea surface height (SSH) have been reported by many authors based on aircraft, sea tower, and satellite-borne observations. These investig ations have resulted in several proposed algorithms of the form SSB = epsilonH, where H is the significant wave height (SWH) and epsilon is a nondimensional function of wind speed U and SWH available from altim eter measurements. In the present work, on the basis of the full set o f Geosat and an 8-month set of TOPEX altimeter measurements, all known algorithms are examined and a conclusion is reached that the altimete r-based U and SWH are insufficient to estimate the SSB correction with uniformly high accuracy. As a criterion of model performance we emplo y the value (called here the accuracy gain) by which the total varianc e 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 geophy sical model function (GMF) of the form epsilon = a0 + a1U is shown to yield an improvement over the simplest GMF with a constant epsilon. A three-parameter linear form epsilon = a0 + a1U + a2H produces somewhat better results. A two-parameter, physically based GMF relating epsilo n to the pseudo wave age xi (where xi is estimated using altimeter win d and SWH) yields even higher accuracy, while the three-parameter GMF of form epsilon = 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 measu re of the SSB models performance) is shown to have serious deficiencie s. We find that for all the SSB models, globally tuned empirical param eters often yield unacceptably poor results for certain regions in whi ch 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.