A model function for ocean radar cross sections at 14.6 GHz

Abstract

The relationship between the ocean's normalized radar cross section (NRCS) at 14.6 GHz and the surface wind vector is derived using the 3 months of Seasat microwave scatterometer (SASS) measurements. The derivation is based on the statistics of the SASS observations, and no in situ measurements are required, other than a mean global wind speed, which comes from climatology. The frequency distribution of the global wind vectors observed by SASS is assumed to be a bivariate normal probability function. A NRCS model function is found that maps the assumed wind vector statistics into the observed SASS NRCS statistics. This function is compared with a NRCS model coming from the Joint Air Sea Interaction Experiment (JASIN) and with aircraft scatterometer measurements. The results indicate that the statistically derived NRCS model is an improvement over the JASIN model, which was based on a limited number of in situ anemometer measurements.

Statistical distribution of wind speeds and directions globally observed by NSCAT

“…Thus dependence of the histogram of wind speeds on incidence angle is investigated in order to assess self-consistency of NSCAT wind speeds. In the SASS 2 model function, which has been used as the tentative prelaunch model function to produce NSCAT preliminary science products, an exponential relation between the wind speed at a height of 19.5 m and the radar cross section is assumed [Wentz et al, 1984]. At very low wind ranges, however, radar backscattering from the sea surface might be much weaker than that predicted by the exponential relation throughout all wind speed ranges.…”

“…Wind vectors contained in the preliminary data products are retrieved using the prelaunch geophysical model function, SASS 2, which was developed for the Seasat A scatterometer [Wentz et al, 1984]. Since the reference height of the SASS 2 model function is 19.5 m, the wind speed is corrected to that at a height of 10 m by multiplying a constant factor of 0.94, which has been commonly used for the conversion.…”

The effect of wind speed products and wind speed–gas exchange relationships on interannual variability of the air–sea CO₂ gas transfer velocity

“…The difference between k W92 i and k W92 av is of a different nature; it arises because at a regional level the wind speed distribution departs from the Rayleigh distribution function that was employed when the relationship for long-term winds (k W92 av ) was deconvolved to a relationship for short-term winds (k W92 i ) (Wanninkhof, 1992). The global ocean wind speed distribution can be represented by a Rayleigh distribution function (Wentz et al, 1984), and this implies that the transfer velocities should be scaled with a factor of 1.25 when long-term rather than short-term winds are employed. However, Wanninkhof et al (2002) showed that although this is true on a global basis, there is non-Rayleigh behaviour in many regions so that the correct scaling factor is frequently only 1.1-1.2.…”

Postlaunch sensor verification and calibration of the NASA Scatterometer

“…A principal difficulty with any empirical technique is the acquisition of comparison data spanning a sufficiently large range of conditions. The SASS-2 empirical model function developed for the Seasat scatterometer [23], [24] was used to produce the preliminary NSCAT vector wind data set. Although the refined model outlined below addresses systematic errors in the SASS-2 model, the fact that vector winds retrieved from NSCAT data using the SASS-2 model came close to meeting the NSCAT science requirements showed both that SASS-2 properly incorporated the basic speed, direction, and incidence angle modulation of sigma-0 and that the NSCAT sigma-0 measurements were reasonably well calibrated in the absolute sense.…”