Simulating Radar Observations of Precipitation at Higher Frequencies from Lower-Frequency Polarimetric Measurements

The translation of radar data from one frequency to another based on electromagnetic absorption and scattering models has been used to convert S-band measurements to higher frequencies, such as those within the X or Ku bands, in order to create realistic simulations. As the target frequency of simulations for weather radar increase to X band and above, the size of large raindrops and hail either approach or exceed the radar wavelength, resulting in a radar cross section that is no longer in the linear Rayleigh region. The Mie solution to scattering models is then required. With the advent of dual-polarization systems, a more complete characterization of the effect of precipitation on radar is possible in order to improve model effectiveness. However, typical curve- or surface-fitting methods still have limitations as particle size increases. A more robust solution is presented here in the form of a neural network that incorporates the nonlinear relationship among various polarimetric observation variables and the radar wavelength and look angle. Thus, high-frequency observations of a convective storm containing hail can be simulated using polarimetric ground radar measurements. Adequate polarimetric data from hail storms at high frequencies do not exist, however, so network outputs for this case can only be compared to theoretical observations. An application of the simulation procedure to characterize the effect of precipitation on spaceborne synthetic aperture radar (SAR) using ground observations is presented.