Relationship between ERS Scatterometer Measurement and Integrated Wind and Wave Parameters

Abstract

Potential effects of environmental parameters such as sea state or atmospheric boundary layer stability on the normalized radar cross section (NRCS) measured by spaceborne sensors have been investigated for a long time. Using neural networks and large high quality collocated datasets, the relation between the European Remote Sensing Satellite (ERS) C-band scatterometer NRCS measurement and integrated sea state parameters (i.e., the mean wave period and significant wave height) measured by buoys is studied. As anticipated, NRCS measurements correlate well with an empirically derived parameter Hα/T?, revealing the mean bulk relationship between a mean 10-m wind speed and the corresponding sea state development. The correlation and exponents exhibit dependency on the scatterometer incidence angles. A neural model that relates the scatterometer NRCS measurements to these wave spectral integrated parameters and wind speed is also developed. As obtained, the retrieval skill is significantly improved, by comparison with operational empirical models such as CMOD-IFR2 or CMOD4, when including wave effects. As illustrated, systematic biases occur under particular environmental conditions when using the operational scatterometer backscatter model functions.