Ocean Radar Scattering Modeling
NOAA's climate-change and weather-prediction models require measurements of wind speed and direction, as well as heat and moisture fluxes, over the global ocean surface. Since the required instruments on many ocean buoys would be prohibitively expensive to deploy and maintain, remote sensors that could take the necessary measurements from satellites are being developed. Prototype passive (radiometric) and active (radar) ocean sensors have already been placed in orbit by several nations. Continuous high-resolution monitoring of near-surface wind vectors over global oceans using satellite radar scatterometers and radiometers allows quantifying atmospheric forcing, ocean response, and air-sea interaction on various spatial and temporal scales. Combining wind data with measurements from other instruments helps us better understand the mechanisms of global climate change and weather patterns. This information will greatly enhance weather forecasts and storm predictions, which in turn will result in saved lives and property.
The basic idea of the radar scatterometric technique relies on the assumption that the ocean surface normalized radar cross section (NRCS) is correlated with the local surface wind speed and direction. The inverse problem of wind retrieval from radar scatterometric data was developed based on existing data sets of collocated contact and scatterometric measurements.
The assumption of a strong correlation between the surface wind vector and the surface NRCS works well for moderate and uniform winds, and explains the success of various empirical geophysical model functions (GMFs) such as SASS-II, CMOD2-I3 and NSCAT2, all of which use this assumption. However, backscattering depends on the previous history of wave fields, wave age, fetch, wind variability, atmospheric stability and other parameters, as well as on the radar wavelength and angles of observation. Recent airborne scatterometer experiments show that these models are not quite adequate for high wind speeds, especially in hurricanes. Currently, attempts are being made by researchers to develop new empirical GMFs that would work for high winds. However, in view of the large number of parameters involved, the best solution of the inverse problem cannot be based on purely empirical relations between scattering and environmental data.
Here, we use another approach that relies on an understanding of both the detailed structure and dynamics of the ocean surface and how electromagnetic waves interact with the rough ocean surface. This approach, which considers a direct problem rather than the an inverse one, includes both analytical and numerical modeling of radar signal scattering from the ocean surface at low grazing angles.
If the first approach can be used for an improvement of wind-speed retrievals from satellite scatterometric measurements, the second one can be indispensable for developing systems based on conventional meteorological and nautical X-band radars. The remote-sensing capabilities of those radars offer valuable operational potential for forecasters in ocean coastal areas. We propose to investigate the capability of these radars to provide high-resolution data of nearshore wave heights, which is not currently available to forecasters and cannot be obtained by other remote sensing techniques such as coastal HF radars. Our modeling is not limited to the case of traditional monostatic radars. We also work on modeling of bistatic radar scattering from the ocean surface, especially, on the version of it that incorporates the satellites of Global Positioning System (GPS) as transmitters of opportunity.