Five space-borne X-band synthetic aperture radars (SARs) are now operating and several more will be aloft in the coming years. These sensors provide an opportunity to measure rainfall over both land and sea with unsurpassed ~ 200 m spatial resolution. A brief description of rainfall retrieval algorithms from such radar data, and their validation is presented.

Unlike the TRMM PR, which provides highly resolved vertical precipitation profiles, X-SARs mainly measure the slant-path integrated scattering and attenuation of precipitation in orthogonal oblique directions. As a consequence, the algorithms to retrieve rainfall distributions from X-SAR Normalized Radar Cross Sections (NRCS) are more complex than those used for conventional radars. As in all rainfall retrievals from single frequency radar data, numerous simplifying assumptions are needed. Retrieval models over land assumed that the surface below the rain had the same ó0 as the neighboring surface. (The same assumption used in the TRMM PR surface reference technique). The size distribution of the hydrometeors was assumed to be known. A sharp transition was assumed between rain and snow at the freezing height. No graupel or supercooled water were considered. The shadow zone of the NRCS mainly depends on the extinction coefficient of the rain. That quantity is more closely related to the path integrated rainfall rate than the effective radar reflectivity factor, which is widely used for rainfall retrievals from conventional radar studies.

This study employed rainfall retrieval algorithms based on a statistical approach and on solutions to a Volterra Integral Equation of the second kind to infer the rainfall distribution over the uniform land surface of the Amazon Basin. Although the techniques are quite distinct, they gave reasonably consistent rainfall distributions.

The coincident measurements of rain bands in Hurricane Gustav near the Mississippi delta in 2008 obtained from NOAA NEXRAD and the TerraSAR-X (TSX) satellite provide a validation of the retrieved rainfall distributions. Preliminary analysis suggests that rainfall producing radar reflectivity factors greater than 35 dBZ over land and those more than 20 dBZ over water can be observed by TSX.

In spite of the variability of the NRCS of sea surfaces around raining clouds, we exploited the insensitivity of the C-band NRCS to rain to infer the C-band NRCS of the sea surface. We transformed the C-band NRCS to an X-band sea surface NRCS that was used as a boundary condition for the X-band NRCS analysis. A combined C and X-band retrieval from two SARs carried in a common polar orbital plane may yield additional information regarding precipitation distributions. One such pair of SARs might be the TSX and the RadarSAT-2.

Although we had no independent measurements with which to validate a retrieved maritime rainfall rate distribution, we had measurements of the C-band Differentially Polarized Phase Shift (DPPS). While the measured and modeled DPPS were reasonably consistent, we made several simplifying assumptions about the Kdp, of the various species of hydrometeors. More comprehensive polarization measurements will lend more credence to future retrieved results.