Location
University of Utah
Start Date
6-12-1996 2:30 PM
Description
A scatterometer is a satellite-borne instrument designed to measure wind over the ocean. Scatterometer wind retrieval is based on the relationship between the wind over the ocean and the resulting scattering cross section of the surface; this relationship, termed the "geophysical model function," maps the wind speed, relative wind direction (relative to the antenna azimuth angle), antenna incidence angle, polarization and frequency band to the scattering cross section. The sea surface temperature, salinity, long waves, wind variability within a scatterometer footprint, etc., lend variability to the backscatter. A particular observation of the wind-dependent backscatter can be viewed as a random variable with mean given by the geophysical model function and variability due to unmodelled effects and measurement errors. Little is known about the variability due to unmodelled effects, or the statistics of this variability; this paper presents some considerations and simulations to estimate the magnitude of the model function error.
Geophysical Modeling Error in Wind Scatterometry
University of Utah
A scatterometer is a satellite-borne instrument designed to measure wind over the ocean. Scatterometer wind retrieval is based on the relationship between the wind over the ocean and the resulting scattering cross section of the surface; this relationship, termed the "geophysical model function," maps the wind speed, relative wind direction (relative to the antenna azimuth angle), antenna incidence angle, polarization and frequency band to the scattering cross section. The sea surface temperature, salinity, long waves, wind variability within a scatterometer footprint, etc., lend variability to the backscatter. A particular observation of the wind-dependent backscatter can be viewed as a random variable with mean given by the geophysical model function and variability due to unmodelled effects and measurement errors. Little is known about the variability due to unmodelled effects, or the statistics of this variability; this paper presents some considerations and simulations to estimate the magnitude of the model function error.