For vertical Doppler velocity measurements of a homogeneous rain field, the standard spectral moment estimation techniques commonly used by ground-based and airborne Doppler rain radars can be readily extended for spaceborne application, provided that the radar antenna size is chosen to adequately reduce the satellite motion-induced Doppler spectral broadening. When encountering an inhomogeneous rain field, on the other hand, the nonuniform beam filling (NUBF) causes additional biases on Doppler velocity estimates, which (i) often reach several meters per second, (ii) cannot be corrected with standard spectral moment techniques, and (iii) are strongly dependent on the along-track reflectivity profile within the radar footprint. One approach to overcome this difficulty is to further increase the antenna size such that the radar’s horizontal resolution would be sufficiently small to resolve the inhomogeneity in rain cells. Unfortunately, this approach is very challenging in terms of antenna technology and spacecraft resources and accommodation.
In this paper, an alternate data processing approach is presented to overcome the NUBF difficulty. This combined frequency–time (CFT) processing technique is used to process a series of Doppler spectra collected over measurement volumes that are partially overlapping in the along-track direction. Its expected performance is evaluated through a spaceborne simulation study using three case studies from high-resolution 3D rainfall datasets acquired by the NASA JPL airborne rain mapping radar. In each of these cases, each representing a different rain regime with a different degree of spatial variability, the CFT technique can effectively remove the NUBF-induced bias such that the mean Doppler velocity estimates achieve the desired accuracy of 1 m s Ϫ1 for a signal-to-noise ratio greater than 10 dB.