Utilization of radar backscattering coefficient from sea surface in rainfall rate retrieval algorithms

Radar methods and data processing techniques for improving rainfall rate estimates over such surfaces are gaining increasing interest. Algorithms proposed for rainfall rate retrieval exploit backscatter or attenuation. A number of single frequency algorithms have been surveyed by Marzoug and Amayenc (see Journ. of Atmosph. Ocean. Rem. Sensing, vol.11, p.1480-1506, 1994), each aiming to minimize the effects of errors. They showed that the surface-referenced algorithm referred to as kZS is potentially the most effective and stable, since it is not sensitive to calibration errors and to errors related to path integrated attenuation (PIA). It is reasonably expected that a well-grounded prediction of the backscattering behaviour of the sea surface when it is perturbed jointly by wind and rainfall can be usefully exploited to improve the kZS algorithm. To fulfil such prediction function, EM models are needed that suitably represent effects of rainfall on the NRCS (normalised radar cross section) of the sea surface. We first show that kZS performs at its best when uncertainty related to the sea surface NRCS is limited. Then, we suggest a possible kZS upgrade, based on the prediction of the NRCS of the water surface roughened by both wind and rainfall. Such prediction relies on the full wave model of Bahar (1987), which incidentally highlights the relevance of rainfall induced corrugation at the considered frequency (13.75 GHz). Results of numerical simulations are finally presented, that confirm the potential of the upgraded algorithm.